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1
Hegar, Veronica, Kristin Oliveira, Bharat Kakarala, Alicia Mangram, and Ernest Dunn. "Annual Mammography Screening: Is it Necessary?" American Surgeon 78, no. 1 (January 2012): 104–6. http://dx.doi.org/10.1177/000313481207800145.
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Recent recommendations from the U.S. Preventative Services Task Force suggest that screening mammography for women should be biennial starting at age 50 years and continue to age 74 years. With these recommendations in mind, we proposed a study to evaluate women at our institution in whom breast cancer is diagnosed within 1 year of a previously benign mammogram. A retrospective chart review was performed over a 4-year period. Only patients who had both diagnostic mammograms and previous mammograms performed at our institution and a pathologic diagnosis of breast cancer were included. Benign mammograms were defined as either Breast Imaging Reporting And Data System 1 or 2. Analysis of the time elapse between benign mammogram and subsequent mammogram indicative of the diagnosis of breast cancer was performed. A total of 205 patients were included. The average age was 64 years. From our results, 48 patients, 23 per cent of the total, had a documented benign mammogram at 12 months or less before a breast cancer diagnosis. One hundred forty-three (70%) patients had a benign mammogram at 18 months or less prior. This study raises concern that 2 years between screening mammograms may delay diagnosis and possible treatment options for many women.
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Oza, Parita Rajiv, Paawan Sharma, and Samir Patel. "Transfer Learning Assisted Classification of Artefacts Removed and Contrast Improved Digital Mammograms." Scalable Computing: Practice and Experience 23, no. 3 (October 13, 2022): 115–27. http://dx.doi.org/10.12694/scpe.v23i3.1992.
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Mammograms are essential radiological images used to diagnose breast cancer well in advance. However, an accurate diagnosis also depends on the quality of mammogram images. Therefore, removal of artefacts and mammogram enhancement are necessary pre-processing steps. Artefact removal helps exclude unsolicited regions in the mammograms and limits the search for suspicious regions without excessive impact from the background. Mammogram enhancements improve apparent visual details and improve some features of an image. In this paper, we propose a method for mammogram pre-processing. These pre-processed mammograms are then fed into Deep Convolutional Neural Network for the classification process. Two approaches are used and compared to classify mammograms; Training model from scratch and Transfer Learning. Transfer Learning is an excellent approach to dealing with the small-sized training set, allowing us to consume the extendibility of deep learning entirely. By employing VGG16 as a pre-trained network on the pre-processed MIAS dataset, we improved training accuracy (96.14\%) compared to the model developed from scratch and other strategies described in the literature.
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Manik, Ritika, Connor B. Grady, Sara Ginzberg, Christine E. Edmonds, Emily F. Conant, Rebecca A. Hubbard, and Oluwadamilola Motunrayo Fayanju. "Racial disparities in diagnostic follow-up following BIRADS 0 mammogram." Journal of Clinical Oncology 40, no. 16_suppl (June 1, 2022): 6559. http://dx.doi.org/10.1200/jco.2022.40.16_suppl.6559.
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6559 Background: Delays in follow-up after abnormal mammograms can lead to worse outcomes and may contribute to health disparities. BIRADS 0 mammograms necessitate additional diagnostic imaging, and BIRADS 4 or 5 mammograms should be followed by biopsy. The goal of this study was to investigate racial disparities in rates and timeliness of (1) diagnostic follow-up after a BIRADS 0 screening mammogram and (2) biopsy following a subsequent BIRADS 4 or 5 diagnostic mammogram. Methods: We included women ≥18 years old who underwent a screening mammogram at the Hospital of the University of Pennsylvania with an assessment of BIRADS 0 between September 2010 and February 2018. The distributions of time from screening to diagnostic mammogram and from BIRADS 4 or 5 diagnostic mammogram to biopsy were estimated using the Kaplan Meier method. Follow-up was censored at 365 days. Case-mix adjusted Cox proportional hazards models were used to estimate the association between race/ethnicity and time to diagnostic mammogram and biopsy. Results: We identified 6299 women (Asian/PI=257, Black=3223, Hispanic=124, White=2420, Other/Unknown=275) with 6880 BIRADS 0 screening mammograms during the study period. Following these BIRADS 0 mammograms, the overall rate of diagnostic mammograms within 365 days was 87.3% (n=6006 mammograms), with a rate of 90.6% (2432) for White women and 85.3% (2971) for Black women. For the 1151 BIRADS 4-5 diagnostic mammograms in the cohort, the overall rate of follow-up biopsies within 365 days was 91.8% (n=1057 biopsies), with a rate of 93.8% (396) for White women and 91.1% (575) for Black women. Compared to mammograms obtained by White women, those obtained by Black women were less likely to be followed up with a diagnostic mammogram (HR 0.71, 95% CI 0.63-0.80, p<0.001) and biopsy (HR 0.74, 95% CI 0.55-0.98, p=0.037) when indicated (Table). Almost 1/4 (24.2%, 95% CI 23.1-25.9%) of BIRADS 0 screening mammograms among Black women were not followed by diagnostic imaging within 30 days as compared to 14.6% among White women (95% CI 13.3-16.0%, p<0.001). 23.6% (95% CI 20.5-27.2%) of BIRADS 4-5 diagnostic mammograms among Black women were not followed up with biopsy within 30 days vs 18.7% for White women (95% CI 15.4-22.8%, p=0.61) (Table). These disparities persisted at 90 days. Conclusions: Racial disparities exist in rates of follow-up after BIRADS 0 mammograms. The additive effects of delays at each diagnostic step put Black women at disproportionately greater risk for worse outcomes. [Table: see text]
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Zhong, Yang Jun, and Qian Cai. "A Novel Registration Approach for Mammograms Based on SIFT and Graph Transformation." Applied Mechanics and Materials 157-158 (February 2012): 1313–19. http://dx.doi.org/10.4028/www.scientific.net/amm.157-158.1313.
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Mammogram registration is an important step in the processing of automatic detection of breast cancer. It provides aid to better visualization correspondence on temporal pairs of mammograms. This paper presents a novel algorithm based on SIFT feature and Graph Transformation methods for mammogram registration. First, features are extracted from the mammogram images by scale invariant feature transform (SIFT) method. Second, we use graph transformation matching (GTM) approach to obtain more accurate image information. At last, we registered a pair of mammograms using Thin-Plate spline (TPS) interpolation based on corresponding points on the two breasts, and acquire the mammogram registration image. Performance of the proposed algorithm is evaluated by three criterions. The experimental results show that our method is accurate and closely to the source images.
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Biggs, MJP, and D. Ravichandran. "Mammography in Symptomatic Women Attending a Rapid Diagnosis Breast Clinic: A Prospective Study." Annals of The Royal College of Surgeons of England 88, no. 3 (May 2006): 306–8. http://dx.doi.org/10.1308/003588406x98603.
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INTRODUCTION We determined whether it is safe to avoid mammograms in a group of symptomatic women with a non-suspicious history and clinical examination. PATIENTS AND METHODS Symptomatic women aged 35 years or over newly referred to a rapid-diagnosis breast clinic underwent mammography on arrival in the clinic. A breast radiologist reported on the mammograms. An experienced clinician who was unaware of the mammogram findings examined patients and decided whether a mammogram was indicated or not. If not, a management plan was formulated. Mammogram findings were then provided to the clinician and any change to the original management plan as a result of mammography was recorded. RESULTS In two-thirds (67%) of 218 patients, the clinician felt a mammogram was indicated. Half (46%) of these mammograms showed an abnormality; of these abnormal mammograms, 41% were breast cancer. Among the third (n = 71) of mammograms felt not to be indicated, 3 showed abnormalities of which 2 were breast cancer. One cancer was not suspected clinically or mammographically but was diagnosed on cyto/histopathological assessment. CONCLUSIONS A significant proportion of patients attending a symptomatic breast clinic have a non-suspicious history and normal clinical findings on examination. However, even in this group avoiding mammograms risks missing clinically occult breast cancers. It would appear sensible to offer mammograms to all symptomatic women over 35 years of age.
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Abdullah, Nasturah, Noorhida Baharudin, Mariam Mohamad, and Mohamed-Syarif Mohamed-Yassin. "Factors Associated with Screening Mammogram Uptake among Women Attending an Urban University Primary Care Clinic in Malaysia." International Journal of Environmental Research and Public Health 19, no. 10 (May 17, 2022): 6103. http://dx.doi.org/10.3390/ijerph19106103.
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Screening mammograms have resulted in a reduction in breast cancer mortality, yet the uptake in Malaysia was low. This study aimed to determine the prevalence and factors associated with screening mammogram uptake among women attending a Malaysian primary care clinic. A cross-sectional study was conducted among 200 women aged 40 to 74 attending the clinic. The data was collected using questionnaires assessing sociodemographic, clinical characteristics, knowledge and health beliefs. Multiple logistic regression was used to identify factors associated with mammogram uptake. The prevalence of screening mammograms was 46.0%. About 45.5% of women with high breast cancer risk had never undergone a mammogram. Older participants, aged 50 to 74 (OR = 2.57, 95% CI: 1.05, 6.29, p-value = 0.039) and those who received a physician’s recommendation (OR = 7.61, 95% CI: 3.81, 15.20, p-value < 0.001) were more likely to undergo screening mammography. Significant health beliefs associated with mammogram uptake were perceived barriers (OR = 0.81, 95% CI: 0.67, 0.97, p-value = 0.019) and cues to action (OR = 1.30, 95% CI: 1.06, 1.59, p-value = 0.012). Approximately half of the participants and those in the high-risk group had never undergone a mammogram. Older age, physician recommendation, perceived barriers and cues to action were significantly associated with mammogram uptake. Physicians need to play an active role in promoting breast cancer screening and addressing the barriers.
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Howard, Daniel, Simon C. Roberts, Conor Ryan, and Adrian Brezulianu. "Textural Classification of Mammographic Parenchymal Patterns with the SONNET Selforganizing Neural Network." Journal of Biomedicine and Biotechnology 2008 (2008): 1–11. http://dx.doi.org/10.1155/2008/526343.
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In nationwide mammography screening, thousands of mammography examinations must be processed. Each consists of two standard views of each breast, and each mammogram must be visually examined by an experienced radiologist to assess it for any anomalies. The ability to detect an anomaly in mammographic texture is important to successful outcomes in mammography screening and, in this study, a large number of mammograms were digitized with a highly accurate scanner; and textural features were derived from the mammograms as input data to a SONNET selforganizing neural network. The paper discusses how SONNET was used to produce a taxonomic organization of the mammography archive in an unsupervised manner. This process is subject to certain choices of SONNET parameters, in these numerical experiments using the craniocaudal view, and typically produced O(10), for example, 39 mammogram classes, by analysis of features from O() mammogram images. The mammogram taxonomy captured typical subtleties to discriminate mammograms, and it is submitted that this may be exploited to aid the detection of mammographic anomalies, for example, by acting as a preprocessing stage to simplify the task for a computational detection scheme, or by ordering mammography examinations by mammogram taxonomic class prior to screening in order to encourage more successful visual examination during screening. The resulting taxonomy may help train screening radiologists and conceivably help to settle legal cases concerning a mammography screening examination because the taxonomy can reveal the frequency of mammographic patterns in a population.
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Palos, Guadalupe R., Katherine Gilmore, Patricia Chapman, Weiqi Bi, Paula Lewis-Patterson, and Alma Maria Rodriguez. "HSR19-104: Patterns of Providers’ Mammogram Referrals for Asymptomatic Breast Cancer Survivors." Journal of the National Comprehensive Cancer Network 17, no. 3.5 (March 8, 2019): HSR19–104. http://dx.doi.org/10.6004/jnccn.2018.7227.
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Background: NCCN Guidelines recommend annual mammography for surveillance in asymptomatic women diagnosed with breast cancer. There is limited evidence to support which type of mammogram—screening or diagnostic—should be ordered for asymptomatic breast cancer survivors. Our objective was to assess the referral patterns for mammograms in 2 breast clinics: survivorship (SC) and oncology (OC). Methods: A retrospective analysis of institutional databases was conducted to identify a convenience sample of women with limited invasive breast cancer who were (1) alive 5 years post-treatment and (2) seen at a SC or OC visit scheduled from January 1 to December 31, 2015. The primary outcome was women who received a diagnostic or screening mammogram during the 2015 calendar year. Demographic, clinical, and mammogram characteristics were also analyzed. Simple descriptive statistics were used to aggregate and compare data. Chi-square analysis tested for statistical significance. Results: A convenience sample of 354 cases was identified. Of those, 247 met the eligibility criteria for this analysis, SC=147 and OC=100. In this cohort, the mean age of diagnosis was 50.42 (±11.12), the majority were white (74.1%), and most received a diagnostic mammogram (64.4%). A greater proportion of diagnostic mammograms were ordered for women seen in OC (40.1%) vs SC (24. 3%). This finding was statistically significant (P=.00). Overall, 91.9 % of the mammogram results were negative and with a low proportion of “positive” (2.0%) or “need for additional imaging testing” (6.1%) reported. Recurrence rates in this cohort were also found to be low (1.6%). Conclusions: In this cohort, we found a difference in the type of mammogram ordered by providers in dedicated survivorship or medical oncology clinics. Our findings suggest that despite the greater use of diagnostic mammograms among this cohort of long-term breast cancer survivors, the overall proportion of positive findings, further imaging tests, or recurrence rates was low. It is concerning that many asymptomatic breast cancer survivors continue to receive a diagnostic mammogram as part of their surveillance visit. Further studies are needed to identify the emotional, financial, and physical toxicities associated with diagnostic mammograms.
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Eberl, M., G. Broffman, J. Pomerantz, N. Watroba, M. Reinhardt, M. C. Mahoney, C. Fox, and S. B. Edge. "Linked claims and medical records for case management of breast cancer diagnosis." Journal of Clinical Oncology 24, no. 18_suppl (June 20, 2006): 6000. http://dx.doi.org/10.1200/jco.2006.24.18_suppl.6000.
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6000 Background: Real time linkage of medical records and administrative claims may assist physicians in managing cancer care. Failure to obtain requisite follow-up (f/u) of abnormal mammograms may delay cancer diagnosis and impact outcome with studies showing that up to 20% of women do not get recommened f/u. We tested the accuracy of claims data linked to medical records in identifying women who did not receive f/u for an abnormal mammogram (BI-RADS 0,3,4,5). Methods: Electronic medical records in a staff model practice affiliated with a single payer were scanned to identify the BI-RADS code for all mammograms performed by practice radiologists. For each woman covered by the payer with a BI-RADS 0,3,4 or 5 mammogram, claims were searched for f/u breast procedures (imaging, biopsy, surgery). For women with more than 1 abnormal mammogram in the study period, only f/u of the first abnormal mammogram was studied. Cases were censured if their insurance coverage with the payer terminated before the required period of follow-up. Medical records of cases defined by claims as not having recommended f/u were reviewed to determine the accuracy of claims analysis. Results: 17,329 women covered by the payer had at least one mammogram in the practice from 1/1/2001 to 12/31/2003. The BI-RADS was 0, 3, 4 or 5 in 1,319 (7.5%). Among 1,206 eligible for f/u, 189 (16%) did not receive the BI-RADS recommended f/u (see table ). Medical record review showed that the claims search accurately identified the follow-up care in 95% of these cases. Conclusions: Administrative claims accurately identify care for diagnostic management of abnormal mammograms. Real-time linkage of claims to mammogram BI-RADS data is being tested as a regional case management system to assist physicians in assuring approrpriate follow-up for abnormal mammograms. [Table: see text] No significant financial relationships to disclose.
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Hermawan, Edy. "Active Contour Lankton untuk Segmentasi Kanker Payudara pada Citra Mammogram." Eksplora Informatika 9, no. 1 (September 30, 2019): 28–37. http://dx.doi.org/10.30864/eksplora.v9i1.258.
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Mammografi merupakan salah satu alat terbaik sampai saat ini untuk melakukan deteksi dini terhadap keberadaan kanker payudara. Penggunaan mammografi efektif menurunkan tingkat kematian akibat kanker payudara sebesar 30% sampai 70%. Akan tetapi, terdapat kesulitan melakukan interpretasi terhadap mammogram sebagai hasil luaran dari mammografi karena sangat bergantung pada kualitas mammogram dan pengalaman dari ahli radiologi dalam mendeteksi lesi kanker payudara. Computer Aided Diagnosis (CAD) sebagai pembaca ganda mammogram dapat dipergunakan untuk meningkatkan akurasi deteksi dan segmentasi dari ahli radiologi. Penelitian ini merupakan upaya untuk melakukan deteksi dan segmentasi dengan menggunakan teknik pemrosesan citra terhadap objek yang dicurigai sebagai lesi kanker payudara pada citra mammogram. Untuk meningkatkan akurasi deteksi dan segmentasi maka dilakukan preprocessing untuk mengurangi noise dan meningkatkan homogenitas aras keabuan mammogram. Deteksi dan segmentasi terhadap keberadaan lesi kanker dilakukan dengan menerapkan metode active contour Lankton. Hasil penelitian menunjukkan metode yang diajukan mampu melakukan deteksi dan segmentasi terhadap lesi kanker payudara dengan baik. Wilayah kanker payudara dapat terdeteksi sesuai dengan wilayah kanker payudara yang dideteksi radiolog dan tersegmentasi dengan jelas. Fitur FO dan GLCM hasil ekstraksi dari lesi kanker payudara dapat diperoleh signifikan tanpa terlalu banyak terkontaminasi dari fitur non lesi kanker payudara. Fitur FO dan GLCM dari lesi kanker payudara hasil ekstraksi dapat dipergunakan sebagai input untuk analisis lanjutan berupa klasifikasi lesi kanker.
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Jen, Chun-Chu, and Shyr-Shen Yu. "AUTOMATIC NIPPLE DETECTION IN MAMMOGRAMS USING LOCAL MAXIMUM FEATURES ALONG BREAST CONTOUR." Biomedical Engineering: Applications, Basis and Communications 27, no. 04 (August 2015): 1550035. http://dx.doi.org/10.4015/s1016237215500350.
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Mammogram registration is an important preprocessing technique, which helps in finding asymmetrical regions in left and right breast. However, correct nipple position is the crucial key point of mammogram registration since it is the only consistent and stable landmark upon a mammogram. To locate the nipple coordinates accurately in mammogram images, this work improves previous algorithms such as maximum height of the breast border (MHBB) and proposes a novel method consisting of local spatial-maximum mean intensity (LSMMI), local maximum zero-crossing (LMZC) based on the second-order derivative, and a combined approach dependent on LSMMI and LMZC. The proposed method is tested on 413 mammogram images from MIAS and DDSM databases. Consequently, the mean Euclidean distance (MED) between the ground truth identified by the radiologist and the detected nipple position is 0.64 cm, within 1 cm of the gold standard, for estimating the proposed method. The experimental results hence indicate that our proposed method can detect the nipple positions more accurately than other previous methods. Furthermore, the proposed select visible-nipple mammograms (SVNM) algorithm with the ability of generalization has achieved a 99% selection rate for automatic clustering of nipples in a mammography database, besides automatically detecting the breast border and nipple positions in mammograms.
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Estrella, F., C. del Frate, T. Hauer, M. Odeh, D. Rogulin, S. R. Amendolia, D. Schottlander, T. Solomonides, R. Warren, and R. McClatchey. "Resolving Clinicians’ Queries Across a Grid’s Infrastructure." Methods of Information in Medicine 44, no. 02 (2005): 149–53. http://dx.doi.org/10.1055/s-0038-1633936.
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Summary Objectives: The past decade has witnessed order of magnitude increases in computing power, data storage capacity and network speed, giving birth to applications which may handle large data volumes of increased complexity, distributed over the internet. Methods: Medical image analysis is one of the areas for which this unique opportunity likely brings revolutionary advances both for the scientist’s research study and the clinician’s everyday work. Grids [1] computing promises to resolve many of the difficulties in facilitating medical image analysis to allow radiologists to collaborate without having to co-locate. Results: The EU-funded MammoGrid project [2] aims to investigate the feasibility of developing a Grid-enabled European database of mammograms and provide an information infrastructure which federates multiple mammogram databases. This will enable clinicians to develop new common, collaborative and co-operative approaches to the analysis of mammographic data. Conclusion: This paper focuses on one of the key requirements for large-scale distributed mammogram analysis: resolving queries across a grid-connected federation of images.
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Mullai, N., N. Murugesan, L. Burton, V. Goodin, and A. Stout. "Risk of noncompliance due to patient discomfort during screening mammogram." Journal of Clinical Oncology 27, no. 15_suppl (May 20, 2009): 1522. http://dx.doi.org/10.1200/jco.2009.27.15_suppl.1522.
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1522 Background: There is enough evidence supporting the benefits of screening mammograms than any other screening procedure for the early detection of malignancy. Compliance to the screening method is very important to achieve this purpose. There is some concern about future trends in breast cancer mortality as a decline in rates of mammogram screening has been noticed. Pain during mammography has been recognized as a significant deterrent to breast screening. Hence this study was done to evaluate compliance in screening mammogram and to analyze major reasons for noncompliance. Methods: Randomly selected patients visiting the doctor's office for various reasons were asked about mammogram and their experience. Their responses were tabulated and analyzed statistically. Results: Out of 160 people questioned, 155 patients had mammogram regularly at 1–2 year interval and five patients did not. 50% of respondents were 51–70 years of age, 15% were 50 years or under, and 31% were over 70 years. Ninety patients (58%) reported their mammogram experience was unpleasant, causing pain and bruising. In spite of the discomforts reported, 132 (82.5%) patients said they would continue the screening as recommended. However, 28 patients (17.5%) indicated their intention not to get further mammograms based on their painful experience, unless the screening technology was improved. Conclusions: Breast cancer incidence after peaking in 1998 has decreased 9.8% since then with a 12% decline in women aged 50–60. The sharpest decline was noted from 2003 that could be due to the decrease in the hormone replacement therapy after the Women's Health Initiative report. However, it may also reflect some reduction in screening mammograms from 70% (in 2000) to 66% (in 2005) as estimated by National Health Interview Survey. Various causes were attributed for the decline in screening mammogram rate including the discomfort and pain caused by compression during film mammography. More than 1 in 6 patients refusing to undergo further screening is definitely a sign of concern. Better screening methods like patient controlled compression mammogram reported by Duke University Medical Center or other methods are worth investigating, to improve the compliance of the one of the effective preventive measures. No significant financial relationships to disclose.
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Ma, Fei, Limin Yu, Gang Liu, and Qiang Niu. "Computer aided mass detection in mammography with temporal change analysis." Computer Science and Information Systems 12, no. 4 (2015): 1255–72. http://dx.doi.org/10.2298/csis141230049m.
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This paper presents a method to extract change information from temporal mammogram pairs and to incorporate the temporal change information in the malignant mass classification. In this method, a temporal mammogram registration framework which is based on spatial relations between regions of interest and graph matching was used to create correspondences between regions of current mammogram and regions of previous mammogram. 18 image features were then used to capture the differences (temporal changes) between the matched regions. To assess the contribution of temporal change information to the mass detection, 5 methods were designed to combine mass classification on image features measured on single regions and mass classification on temporal features to improve overall mass classification. The method was tested on 95 pairs of temporal mammograms using k-fold cross validation procedure. The experimental results showed that, when combining two classification results using linear combination or by taking minimum value, the Az score of overall classification performance increased from 0.8843 to 0.8989 and 0.8863 respectively. The results demonstrated that registering temporal mammograms, measuring temporal changes from matched regions and incorporating the change information in the mass classification improves the overall mass detection.
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Chiou, Yih-Chih, Chern-Sheng Lin, and Cheng-Yu Lin. "HYBRID REGISTRATION OF CORRESPONDING MAMMOGRAM IMAGES FOR AUTOMATIC DETECTION OF BREAST CANCER." Biomedical Engineering: Applications, Basis and Communications 19, no. 06 (December 2007): 359–74. http://dx.doi.org/10.4015/s101623720700046x.
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Mammogram registration is a critical step in automatic detection of breast cancer. Much research has been devoted to registering mammograms using either feature-matching or similarity measure. However, a few studies have been done on combining these two methods. In this research, a hybrid mammogram registration method for the early detection of breast cancer is developed by combining feature-based and intensity-based image registration techniques. Besides, internal and external features were used simultaneously during the registration to obtain a global spatial transformation. The experimental results indicates that the similarity between the two mammograms increases significantly after a proper registration using the proposed TPS-registration procedures.
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North, Frederick, Elissa M. Nelson, Rebecca J. Buss, Rebecca J. Majerus, Matthew C. Thompson, and Brian A. Crum. "The Effect of Automated Mammogram Orders Paired With Electronic Invitations to Self-schedule on Mammogram Scheduling Outcomes: Observational Cohort Comparison." JMIR Medical Informatics 9, no. 12 (December 7, 2021): e27072. http://dx.doi.org/10.2196/27072.
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Background Screening mammography is recommended for the early detection of breast cancer. The processes for ordering screening mammography often rely on a health care provider order and a scheduler to arrange the time and location of breast imaging. Self-scheduling after automated ordering of screening mammograms may offer a more efficient and convenient way to schedule screening mammograms. Objective The aim of this study was to determine the use, outcomes, and efficiency of an automated mammogram ordering and invitation process paired with self-scheduling. Methods We examined appointment data from 12 months of scheduled mammogram appointments, starting in September 2019 when a web and mobile app self-scheduling process for screening mammograms was made available for the Mayo Clinic primary care practice. Patients registered to the Mayo Clinic Patient Online Services could view the schedules and book their mammogram appointment via the web or a mobile app. Self-scheduling required no telephone calls or staff appointment schedulers. We examined uptake (count and percentage of patients utilizing self-scheduling), number of appointment actions taken by self-schedulers and by those using staff schedulers, no-show outcomes, scheduling efficiency, and weekend and after-hours use of self-scheduling. Results For patients who were registered to patient online services and had screening mammogram appointment activity, 15.3% (14,387/93,901) used the web or mobile app to do either some mammogram self-scheduling or self-cancelling appointment actions. Approximately 24.4% (3285/13,454) of self-scheduling occurred after normal business hours/on weekends. Approximately 9.3% (8736/93,901) of the patients used self-scheduling/cancelling exclusively. For self-scheduled mammograms, there were 5.7% (536/9433) no-shows compared to 4.6% (3590/77,531) no-shows in staff-scheduled mammograms (unadjusted odds ratio 1.24, 95% CI 1.13-1.36; P<.001). The odds ratio of no-shows for self-scheduled mammograms to staff-scheduled mammograms decreased to 1.12 (95% CI 1.02-1.23; P=.02) when adjusted for age, race, and ethnicity. On average, since there were only 0.197 staff-scheduler actions for each finalized self-scheduled appointment, staff schedulers were rarely used to redo or “clean up” self-scheduled appointments. Exclusively self-scheduled appointments were significantly more efficient than staff-scheduled appointments. Self-schedulers experienced a single appointment step process (one and done) for 93.5% (7553/8079) of their finalized appointments; only 74.5% (52,804/70,839) of staff-scheduled finalized appointments had a similar one-step appointment process (P<.001). For staff-scheduled appointments, 25.5% (18,035/70,839) of the finalized appointments took multiple appointment steps. For finalized appointments that were exclusively self-scheduled, only 6.5% (526/8079) took multiple appointment steps. The staff-scheduled to self-scheduled odds ratio of taking multiple steps for a finalized screening mammogram appointment was 4.9 (95% CI 4.48-5.37; P<.001). Conclusions Screening mammograms can be efficiently self-scheduled but may be associated with a slight increase in no-shows. Self-scheduling can decrease staff scheduler work and can be convenient for patients who want to manage their appointment scheduling activity after business hours or on weekends.
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Alkhaifi, Sarah, and Hanan Badr. "Association between Breast Cancer Knowledge and Mammogram Utilization among Immigrant Muslim Arab Women in California: Cross-Sectional Design." Healthcare 10, no. 12 (December 14, 2022): 2526. http://dx.doi.org/10.3390/healthcare10122526.
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Background: Regular mammogram screenings have contributed to early breast cancer (BC) diagnoses and lowered the mortality rate by 40% in the United States of America (USA). Nonetheless, ethnic women living in developed countries, such as immigrant Muslim Arab women (IMAW), are less likely to get mammograms. Aim of the study: In our study, we aimed to understand health behaviors among IMAWs as understudied populations in the USA. Methods: We conducted a cross-sectional study on a convenience sample of IMAW living in southern California. We used logistic regression and multivariate logistic regressions to analyze the data. Results: The total number of participants who completed the survey was 184 IMAW. Participants who had a higher level of knowledge about BC signs and symptoms and mammogram knowledge were more likely to have obtained a mammogram at some point compared with their counterparts (OR = 1.23, p = 0.03, CI: 1.07–1.42; OR = 2.23, p = 0.23, CI: 1.11–4.46, respectively). Conclusions: Our results provide more evidence emphasizing the important influence of BC and mammogram knowledge on immigrant women’s behavior regarding mammogram utilization. The average level of knowledge in all three domains (BC risk factors, BC signs and symptoms, and mammogram use) reported in this study is considered low.
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Sivakumar, V., and P. Shanmugavadivu. "Mammogram Mass Segmentation Using Fractals." Journal of Computational and Theoretical Nanoscience 16, no. 8 (August 1, 2019): 3437–42. http://dx.doi.org/10.1166/jctn.2019.8305.
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A new fractal-based technique for the segmentation of masses from digital mammograms is shown in this paper. This proposed fractal-based approach aims to detect and segment the masses from mammograms after successful suppression of pectoral muscle. This paper identifies and suppresses the pectoral muscle efficiently from the input mammogram and further segments the masses precisely with the help of fractal dimensions and fractal hurst based thresholding oriented computation. This approach is proved to be efficient in getting accurate segmentation results that guarantees the virtue of the projected technique.
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Picton, M. E., B. Ramirez, D. Liles, T. R. Sastry, and M. Petruzziello. "Barriers to screening and treatment of breast cancer: Data analysis from Edgecombe County." Journal of Clinical Oncology 29, no. 27_suppl (September 20, 2011): 232. http://dx.doi.org/10.1200/jco.2011.29.27_suppl.232.
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232 Background: Edgecombe County in NC was described as the 3rd highest location breast cancer mortality according to the Susan G. Komen report (2007). The major issues detected were low education, lack of healthcare providers, and large numbers of uninsured individuals. Our analysis sought to further characterize the circumstances unique to this region and measures to improve mortality. Methods: Between October 2008 and January 2009, 493 surveys were conducted throughout the county. The surveyors randomly approached female residents of Edgecombe County who completed a questionnaire, which was analyzed for this study. Results: Of the total population 354 women were older than age 40. In this group 82.5% had recent mammograms and 79.8% clinical breast examinations. Also, 91.7% had a Primary Physician who recommended mammograms in 85% of the cases. Only 27.1% had family history of breast cancer and, of those, 86.2% were recommended mammograms. Most were educated (58.6%), had low income (76%) and health insurance (87.4%). Nearly equal numbers of Caucasians and African Americans completed the survey (50.6% vs. 47.6%). Just 8.1% had transportation problems and 3.6% were aware of free mammograms in the health department. Statistical analysis by the Fisher’s Exact Test evaluated the relationship between the likelihood of having a screening mammogram and different variables. Women who attended church were more likely to undergo mammograms (p=0.00054), as were women with insurance (p=0.024). Family histories of breast cancer, lack of transportation, low income or deficient education were not significant determinants to obtain a mammogram. A logistical regression model demonstrated that attendance to church and insurance were the two factors statistically significant in terms of obtaining a mammogram. Conclusions: The main issues identified by our analysis were low-income, low health care literacy and lack of awareness regarding breast cancer programs. Our results were discordant with some of the Susan Komen report data, particularly that the majority of participants had a mammogram. Transportation and religious beliefs were not barriers to screening of breast cancer.
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Southwell, Brian G., Jonathan S. Slater, Christina L. Nelson, and Alexander J. Rothman. "Does It Pay to Pay People to Share Information? Using Financial Incentives to Promote Peer Referral for Mammography among the Underinsured." American Journal of Health Promotion 26, no. 6 (July 2012): 348–51. http://dx.doi.org/10.4278/ajhp.110120-arb-34.
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Purpose. Efforts to screen underinsured women for breast cancer face challenges in reaching desired audiences. One option is viral marketing through peer referral. We sought the optimal way to solicit nominations of peers. Design. An experiment (N = 2968) compared impact of incentives on peer nomination. Women were offered a $20 incentive each time someone they referred was screened, a $5 incentive for each name and valid address or phone number (regardless of screening completion), or no financial incentive for nomination. Setting. Study sample was drawn from free mammography program participants in Minnesota. Analysis. Post hoc Scheffé t-tests compared conditions on nominees per invitation card sent (N = 2968), mean number of nominees scheduling mammogram per referrer (n = 107), and proportion of total nominees (N = 1041) scheduling a mammogram for each incentive condition. Results. Offering $5 per nomination yielded .52 nominations per referral invitation sent, compared to .36 nominations per invitation sent for $20 per completed mammogram and only .17 nominations per invitation in the no incentive group. In the no incentive condition, however, each referrer generated .35 scheduled mammograms on average, which was statistically equivalent to the .16 scheduled mammograms delivered on average by $20 per completed mammogram referrers and statistically superior, p <.05, to the .09 rate produced by $5 per name referrers. Conclusion. Programs interested in peer referral could productively pursue the strategy without incurring incentive costs.
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Sundaram, M., K. Ramar, N. Arumugam, and G. Prabin. "EFFICIENT EDGE EMPHASIZED MAMMOGRAM IMAGE ENHANCEMENT FOR DETECTION OF MICROCALCIFICATION." Biomedical Engineering: Applications, Basis and Communications 26, no. 05 (September 26, 2014): 1450056. http://dx.doi.org/10.4015/s1016237214500562.
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An efficient detection of microcalcification based on edge enhancement using discrete wavelet transform (DWT) is presented in this paper. The proposed method is implemented by separating the wavelet coefficients into weak and strong edge coefficients for effective detection of microcalcification. Identification of strong and weak edge locations corresponding to microcalcification is obtained by allowing the input image through appropriate filters before wavelet decomposition. Before reconstructing the output image, the strong and weak edge coefficients are modified based on the energy of the coefficients. The reconstructed image exhibits a better enhancement with the fine detail components of microcalcification than the original mammogram image. Standard Mias mammogram database images and clinical mammograms are used for testing and comparing subjective and objective measures of the mammogram images. A comparative study is made with the existing state-of-the-art edge enhancement and contrast enhancement methods and results are encouraging. The edge emphasizing ability of the proposed method is highly proficient in detection of microcalcification from the mammogram.
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Stojic, Tomislav. "Visual enhancement of microcalcifications and masses in digital mammograms using modified multifractal analysis." Nuclear Technology and Radiation Protection 30, no. 1 (2015): 61–69. http://dx.doi.org/10.2298/ntrp1501061s.
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Microcalcifications and masses, as breast tissue anomalies (deviations from observed background regularity), may be viewed as statistically rare occurrences in a mammogram image. After recognizing their principal common features - bright image parts not belonging to the surrounding tissue, with significant local contrast just around the edges - several modifications to multifractal image analysis have been introduced. Starting from a mammogram image, the proposed method creates corresponding multifractal images. Additional post-processing, based on mathematical morphology, refines the procedure by selecting and outlining only regions with possible microcalcifications and masses. The proposed method was tested through referent mammograms from the MiniMIAS database. In all cases involving the said database, the method has successfully enhanced declared anomalies: microcalcifications and masses. The results obtained have shown that the described procedure may provide visual assistance to radiologists in clinical mammogram examinations or be used as a preprocessing step for further mammogram processing, such as segmentation, classification, and automatic detection of suspected bright breast tissue lesions.
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Hsu, John, Jie Huang, and Bruce Fireman. "Mammography screening among women 40-49 years old, before and after USPSTF guideline changes." Journal of Clinical Oncology 31, no. 26_suppl (September 10, 2013): 113. http://dx.doi.org/10.1200/jco.2013.31.26_suppl.113.
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113 Background: The age at which to start breast cancer screening is controversial. The US Preventive Task Force (USPSTF) recommended against routine mammography screening in women 40-49 years old in November 2009 (the 2002 report had recommended screening), and instead suggested that women consult their physicians. We examined the frequency of screening among 40-49yo, and characteristics of the screened. Methods: We examined mammography screening among 85,650 women who were continuously enrolled in a prepaid integrated delivery system and who were between 40-45yo in 2006. We excluded women with any cancer diagnosis (2005-2009). Using logistic regression, we examined the association between screening in 2010 and several traits, e.g., mammogram cost-sharing, screening behavior before 2010, comorbidity score, race/ethnicity, and neighborhood socio-economic status indictors. In 2010, 75% of women had free mammograms; for the others, the median cost-sharing amount was $10. Results: Nearly all subjects (92%) received at least one mammogram between 2006-10: 14% received one; 37% received two; 33% received three; 6% received four; and 1% received 5+ mammograms. The unadjusted percent screened during each year increased slightly from 2006-2009 (38% to 47%) before dropping in 2010 (45%). Among women screened before the guideline changes, 47% received a mammogram in 2010. After adjustment, subjects who had any cost-sharing (OR=0.96 vs. free screening, 95%CI: 0.93-0.99) or who lived in lower SES neighborhoods (OR=0.94 vs. higher SES, 95%CI: 0.90-0.97) were less likely to receive a screening mammogram in 2010. Subjects with frequent earlier screening (e.g., OR=4.44 for four mammograms vs. none, 95%CI:3.99-4.94) and who were Asian (OR=1.05 vs. White, 95%CI: 1.01-1.09) were more likely to receive a 2010 mammogram; Black and Hispanic subjects had similar odds as Whites. Conclusions: In this integrated system, mammography screening among women 40-49 years old decreased slightly after the 2009 USPSTF recommendation against routine screening. Better alignment of insurance benefits with screening guidelines could improve adherence with recommendations.
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Allison, Kimberly H., Linn A. Abraham, Donald L. Weaver, Anna NA Tosteson, Tracy Onega, Berta M. Geller, Karla Kerlikowske, et al. "Tissue sampling frequency and breast pathology diagnoses following mammography: Time trends and age group analysis from the Breast Cancer Surveillance Consortium (BCSC)." Journal of Clinical Oncology 31, no. 15_suppl (May 20, 2013): 559. http://dx.doi.org/10.1200/jco.2013.31.15_suppl.559.
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559 Background: Pathology diagnoses in a well-characterized population of women can be used to identify tissue sampling and diagnosis trends following mammography. Methods: Screening and diagnostic mammography, patient characteristics, and pathology reports from the BCSC performed from 1996-2008 were identified. Diagnosis was based on the most severe pathology interpretation in the same breast within 60 days of a post-mammogram tissue sample. Age, mammogram year and type, breast density, and family history of breast cancer were evaluated for associations with tissue sampling and most severe pathology diagnosis. Results: 4,022,506 mammograms (88.5% screening; 11.5% diagnostic) were performed in 1,288,886 women; 76,567 (1.9%) were followed by tissue sampling (1.2% screening; 7.1% diagnostic). Tissue sampling frequency following diagnostic mammography increased over time in women over 50 but remained stable following screening mammography. The frequency of invasive cancer increased with age and was more common following a diagnostic (29.3%) vs screening (19.8%) mammogram; the frequency of high risk lesions (ADH; lobular neoplasia) was highest in women aged 50-59. For tissue sampling following screening mammograms, the frequency of DCIS increased over time while benign diagnoses decreased. No significant time trends were noted for diagnoses associated with diagnostic mammograms. Women aged 40-59 with dense breasts and a tissue sampling following screening mammogram had a significantly higher frequency of DCIS (40-49: 4.8% vs 3.2%, P< 0.001; 50-59: 7.0% vs 5.7%, P=0.007). Women aged 40-59 with > 1first degree relative with breast cancer vs none that had a tissue sampling following screening mammogram had a significantly higher frequency of invasive cancer (40-49: 11.4% vs 9.4%, p=0.008; 50-59: 19.8% vs 18.2%, p =0.086) and DCIS (40-49: 6.2% vs 4.0%, p< 0.001; 50-59: 8.2% vs 6.2%, p< 0.001). Conclusions: There was an increase in DCIS and a decrease in benign diagnoses in tissues samples after screening mammography over time. No trends were seen following diagnostic mammography. DCIS was also more frequent in women with dense breasts.
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Gopal, Annapoorani, Lathaselvi Gandhimaruthian, and Javid Ali. "Role of General Adversarial Networks in Mammogram Analysis: A Review." Current Medical Imaging Formerly Current Medical Imaging Reviews 16, no. 7 (September 9, 2020): 863–77. http://dx.doi.org/10.2174/1573405614666191115102318.
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The Deep Neural Networks have gained prominence in the biomedical domain, becoming the most commonly used networks after machine learning technology. Mammograms can be used to detect breast cancers with high precision with the help of Convolutional Neural Network (CNN) which is deep learning technology. An exhaustive labeled data is required to train the CNN from scratch. This can be overcome by deploying Generative Adversarial Network (GAN) which comparatively needs lesser training data during a mammogram screening. In the proposed study, the application of GANs in estimating breast density, high-resolution mammogram synthesis for clustered microcalcification analysis, effective segmentation of breast tumor, analysis of the shape of breast tumor, extraction of features and augmentation of the image during mammogram classification have been extensively reviewed.
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Rehman, Khalil ur, Jianqiang Li, Yan Pei, Anaa Yasin, Saqib Ali, and Yousaf Saeed. "Architectural Distortion-Based Digital Mammograms Classification Using Depth Wise Convolutional Neural Network." Biology 11, no. 1 (December 23, 2021): 15. http://dx.doi.org/10.3390/biology11010015.
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Architectural distortion is the third most suspicious appearance on a mammogram representing abnormal regions. Architectural distortion (AD) detection from mammograms is challenging due to its subtle and varying asymmetry on breast mass and small size. Automatic detection of abnormal ADs regions in mammograms using computer algorithms at initial stages could help radiologists and doctors. The architectural distortion star shapes ROIs detection, noise removal, and object location, affecting the classification performance, reducing accuracy. The computer vision-based technique automatically removes the noise and detects the location of objects from varying patterns. The current study investigated the gap to detect architectural distortion ROIs (region of interest) from mammograms using computer vision techniques. Proposed an automated computer-aided diagnostic system based on architectural distortion using computer vision and deep learning to predict breast cancer from digital mammograms. The proposed mammogram classification framework pertains to four steps such as image preprocessing, augmentation and image pixel-wise segmentation. Architectural distortion ROI’s detection, training deep learning, and machine learning networks to classify AD’s ROIs into malignant and benign classes. The proposed method has been evaluated on three databases, the PINUM, the CBIS-DDSM, and the DDSM mammogram images, using computer vision and depth-wise 2D V-net 64 convolutional neural networks and achieved 0.95, 0.97, and 0.98 accuracies, respectively. Experimental results reveal that our proposed method outperforms as compared with the ShuffelNet, MobileNet, SVM, K-NN, RF, and previous studies.
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Alkhenizan, Abdullah, Aneela Hussain, and Adher Alsayed. "The sensitivity and specificity of screening mammography in primary care setting in Saudi Arabia." Journal of Clinical Oncology 31, no. 15_suppl (May 20, 2013): e12551-e12551. http://dx.doi.org/10.1200/jco.2013.31.15_suppl.e12551.
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e12551 Background: Breast cancer is the leading cancer diagnosed in women in Saudi Arabia, accounting for 25% of all cancers diagnosed in women. The mammogram screening program at King Faisal Specialist Hospital and Research Center (KFSHRC) is the only structured screening program in the country. KFSHRC provides primary care services for a catchment population of 30,000 patients. This program covers all women above the age of 40 within this catchment population. Methods: A retrospective review of electronic and paper records were reviewed for mammograms done between January 2002- January 2012. Summary statistics were used to describe patient and examination characteristics. Results from mammograms were reported using the Breast Imaging Reporting and Data System (BI-RADS) of the American College of Radiology. The stage of diagnosis was reported using the American Joint Committee on Cancer (AJCC) system using stages one through four. ACR BIRADS classification and cancer status definitions mammograms were linked with cancer outcomes to identify true-positive, true-negative, false-positive, and false-negative examinations. On the basis of these classifications, sensitivity, specificity, positive predictive value, and negative predictive value were estimated. All mammograms and tissue biopsies were read by board certified specialists. Results: During the first round of screening 1694 mammograms were analyzed, and 12 cases of cancer were diagnosed. Cancer detection rate (per 1000 examination) was 7.1. Biopsy rate was 3.7 per 100 mammograms. Follow up ultrasounds rate was 2.7 per 100 mammograms. Sensitivity of mammogram screening was 80%, and specificity was 76%. Conclusions: The yield of a structured mammogram screening program in Saudi Arabia is high. There is a need to implement a national program for breast cancer screening in Arabian world in general and within Saudi Arabia in particular.
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Stojic, Tomislav. "A fast and simple method for the visual enhancement of microcalcifications in digital mammograms based on mathematical morphology." Nuclear Technology and Radiation Protection 29, no. 2 (2014): 108–15. http://dx.doi.org/10.2298/ntrp1402108s.
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A fast and simple method for the visual enhancement of small bright details in digital mam- mograms based on mathematical morphology is proposed. By a proper choice of the shape and size of the structuring element, an algorithm for a particular processing task - in this case, for the visual enhancement of microcalcifications in digital mammograms - was designed. The efficiency of the proposed algorithm was tested on publicly available mammograms from the mammographic image analysis society database. In all tested cases (23 mammograms), the proposed method successfully segmented and enhanced the existing microcalcifications, in- dependently verified by medical experts. The proposed procedure may be used both as a visual aid in clinical mammogram analysis or as a preprocessing step for further processing, such as segmentation, classification and detection of microcalcifications. Moreover, the algorithm is very fast and robust, thus applicable to real-time mammogram processing.
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Popli, Manju Bala, Rahul Teotia, Meenakshi Narang, and Hare Krishna. "Breast Positioning during Mammography: Mistakes to be Avoided." Breast Cancer: Basic and Clinical Research 8 (January 2014): BCBCR.S17617. http://dx.doi.org/10.4137/bcbcr.s17617.
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Aims and Objectives Breast positioning is the key factor affecting a mammogram. If care is taken during positioning, it maximizes the amount of breast tissue being imaged, eliminates most of the artifacts, and increases sensitivity of the mammogram. This retrospective study was carried out in our department to assess correctness, and also the incorrectness of breast positioning, which need to be avoided to obtain an ideal mammogram. Material and Methods A total of 1369 female patients were included in this study. Mammography was performed on full field detector digital mammography equipment. Craniocaudal (CC) view and mediolateral oblique (MLO) view were carried out for each breast. Four views were done for 1322 patients. The remaining 47 patients had undergone a mastectomy and underwent two views for the other breast. Mistakes in improperly positioned mammogram were assessed with respect to proper visualization of nipple, position of pectoralis major, pectoral–nipple distance (PND), inframammary fold, and adequate coverage of all breast quadrants. Results As per prescribed guidelines, mistakes in positioning were recognized in 2.879% of total mammograms. Improper positioning of the nipple was the commonest problem, seen in 3.827% of mammograms, CC view. On MLO view, bilaterally, pectoralis shadow was not seen in 0.520% mammograms, its margin was not straight/convex in 0.706%, lower edge of pectoralis was above pectoralis–nipple line in 2.081%, and inframammary fold was not seen in 1.189%. There was inadequate coverage of lower quadrants in 2.787%, and mismatch in PND was seen in 3.864%. In few of the patients, the shortcomings as a result of improper positioning were noted on one view, the rest being normal. Conclusion Positioning is the most important factor affecting the resultant mammography image. During mammography, many cases are improperly positioned and as a result the examination is inconclusive, which reduces the sensitivity of mammography.
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Liu, Chen Chung, Shyr Shen Yu, Chung Yen Tsai, and Ta Shan Tsui. "Pectoral Muscle Segmentation for Digital Mammograms Based on Otsu Thresholding." Applied Mechanics and Materials 121-126 (October 2011): 4537–41. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.4537.
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The appearance of pectoral muscle in medio-lateral oblique (MLO) views of mammograms can increase the false positive in computer aided detection (CAD) of breast cancer detection. Pectoral muscle has to be identified and segmented from the breast region in a mammogram before further analysis. The main goal of this paper is to propose an accurate and efficient algorithm of pectoral muscle extraction on MLO mammograms. The proposed algorithm bases on the positional characteristic of pectoral muscle in a breast region to combine the iterative Otsu thresholding scheme and the mathematic morphological processing to find the rough border of the pectoral muscle. The multiple regression analysis is then employed on the rough border to obtain the accurate segmentation of the pectoral muscle. The presented algorithm is tested on the digital mammograms from the Mammogram Image Analysis Society (MIAS) database. The experimental results show that the pectoral muscle extracted by the presented algorithm approximately follows that extracted by an expert radiologist.
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Jiang, Shu, and Graham A. Colditz. "Abstract LB161: Whole mammogram image improves breast cancer prediction." Cancer Research 82, no. 12_Supplement (June 15, 2022): LB161. http://dx.doi.org/10.1158/1538-7445.am2022-lb161.
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Abstract To efficiently capture data from mammographic breast images and classify long term risk of breast cancer, we developed methods that use the extensive existing data that are currently ignored in the context of breast cancer risk stratification. More than 20 studies support texture features add value to risk prediction beyond breast density. However, the entire mammogram imaging data has a high dimension of pixels (~13 million per image), greatly exceeding the number of women in a cohort. We apply functional principal component analysis methods to predict 5-years breast cancer incidence using baseline mammograms. We applied these methods onto women participating in the Joanne Knight Breast Health Cohort which is comprised of over 10,000 women undergoing repeated mammography screening at Siteman Cancer Center and followed since 2010. All women had baseline mammogram at entry, provided a blood sample and completed a risk factor questionnaire. Mammograms are all using the same technology (Hologic). During follow-up through October 2020, we identified 246 incident breast cancer cases (pathology confirmed) and matched them to controls from the perspective cohort based on month of mammogram and age at entry. In a baseline model we controlled for age, menopause, BMI, and mammographic breast density (BIRADs). We then added the full image (characterized by the FPC) to the base model and further compared the AUC of the new model vs the base model using the likelihood ratio test. AUC is validated with internal 10-fold cross validation. The AUC for 5-year breast cancer risk classification increased significantly from a median of 0.61 (sd 0.09 for estimated AUCs across 10-fold internal validation) for the baseline model to 0.70 (0.10) when the full image is added, p < 0.001. We conclude that using full mammogram images for breast cancer risk prediction captures additional information on breast tissue characteristics that relate to cancer risk, and improves prediction classification. This prediction algorithm can run efficiently in real time (in seconds) with processing of digital mammograms. Thus, this model can be easily implemented in mammography screening services and other clinical settings to guide real-time risk stratification to improve precision prevention of the leading cancer in women world-wide. Further analysis will quantify the value of adding other breast cancer risk factors, including polygenic risk scores. Addition of repeated mammogram images over time should further increase classification performance. This approach has the potential to improve risk classification by using data already available for the vast majority of women already having repeated screening mammograms. Citation Format: Shu Jiang, Graham A. Colditz. Whole mammogram image improves breast cancer prediction [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB161.
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Pairawan, Seyed S., Luis Olmedo Temich, Sebastian de Armas, Andrew Folkerts, Naveen Solomon, Cherie Cora, Kirithiga Ramalingam, and Sharon S. Lum. "Recovery of Screening Mammogram Cancellations During COVID-19." American Surgeon 87, no. 10 (October 2021): 1651–55. http://dx.doi.org/10.1177/00031348211051695.
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Background In response to the COVID-19 pandemic, the American Society of Breast Surgeons and American College of Radiology released a joint statement recommending that all breast screening studies be postponed effective March 26, 2020. Study Design A retrospective review of all canceled mammograms at a single tertiary care institution from January 1-August 31, 2020 was performed to evaluate the effect of this recommendation by quantifying both the number and reason for mammogram cancellations before and after March 26, 2020. Utilization of the electronic patient portal for appointment cancellation as a surrogate for telehealth uptake was noted. Results During the study period, 5340 mammogram appointments were kept and 2784 mammogram appointments were canceled. From a baseline of 30 (10.8%) canceled mammograms in January, cancellations peaked in March (576, 20.6%) and gradually decreased to a low in August (197, 7%). Reasons for cancellations varied significantly by month ( P < .0001) and included COVID-19 related (236, 8.5%), unspecified patient reasons (1,210, 43.5%), administrative issues (147, 5.3%), provider requests (46, 1.7%), sooner appointments available (31, 1.1%), and reasons not given (486, 17.5%). In addition, compared to a baseline in January (51, 16.5%), electronic patient portal access peaked in August (67, 34.0%). Conclusion Screening mammogram cancellations have gradually recovered after early COVID-19 restrictions were lifted and increasing use of electronic patient access appears to be sustained. Consequences for future staging at the time of diagnosis remain unknown. Understanding to what extent the pandemic affected screening may help surgeons plan for post-pandemic breast cancer care.
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Loizidou, Kosmia, Galateia Skouroumouni, Christos Nikolaou, and Costas Pitris. "A Review of Computer-Aided Breast Cancer Diagnosis Using Sequential Mammograms." Tomography 8, no. 6 (December 6, 2022): 2874–92. http://dx.doi.org/10.3390/tomography8060241.
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Radiologists assess the results of mammography, the key screening tool for the detection of breast cancer, to determine the presence of malignancy. They, routinely, compare recent and prior mammographic views to identify changes between the screenings. In case a new lesion appears in a mammogram, or a region is changing rapidly, it is more likely to be suspicious, compared to a lesion that remains unchanged and it is usually benign. However, visual evaluation of mammograms is challenging even for expert radiologists. For this reason, various Computer-Aided Diagnosis (CAD) algorithms are being developed to assist in the diagnosis of abnormal breast findings using mammograms. Most of the current CAD systems do so using only the most recent mammogram. This paper provides a review of the development of methods to emulate the radiological approach and perform automatic segmentation and/or classification of breast abnormalities using sequential mammogram pairs. It begins with demonstrating the importance of utilizing prior views in mammography, through the review of studies where the performance of expert and less-trained radiologists was compared. Following, image registration techniques and their application to mammography are presented. Subsequently, studies that implemented temporal analysis or subtraction of temporally sequential mammograms are summarized. Finally, a description of the open access mammography datasets is provided. This comprehensive review can serve as a thorough introduction to the use of prior information in breast cancer CAD systems but also provides indicative directions to guide future applications.
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Eberl, M. M., C. H. Fox, G. Broffman, J. Pomerantz, J. Serghany, M. Reinhardt, N. Watroba, and S. B. Edge. "An automated practice-based intervention for case management of abnormal mammograms." Journal of Clinical Oncology 25, no. 18_suppl (June 20, 2007): 6563. http://dx.doi.org/10.1200/jco.2007.25.18_suppl.6563.
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6563 Background: Failure to obtain follow-up (f/u) of abnormal mammograms may delay cancer diagnosis and impact outcome; studies show up to 20% of women do not get recommended f/u. Our group previously established that administrative claims linked to medical records accurately identify care for management of abnormal mammograms. We are testing a case management system linking claims data to mammogram information to assist physicians in assuring appropriate f/u for abnormal mammograms. Methods: Electronic medical records in a staff model practice affiliated with a single payer are scanned to identify the BI-RADS code for all mammograms. The practice performs about 8,000 mammograms with about 500 abnormal mammograms annually. Physicians are notified of each abnormal mammogram and provided with a standardized care pathway for management. For each BI-RADS 0, 3, 4 or 5 mammogram, claims are searched for f/u breast procedures (imaging, biopsy, surgery). If recommended f/u (additional imaging for BI-RADS 0, biopsy by 2 months for BI-RADS 4 and 5, f/u imaging by 8 months for BI-RADS 3) is not observed, the ordering physician is alerted. Results: From 01/01/2001 to 12/31/2003, we observed that among women eligible for f/u, 189 (16%) did not receive BI-RADS recommended f/u care. In the prospective intervention from 1/1/06 - 6/30/06, 166 BI-RADS 0, 21 BI-RADS 4, and 3 BI-RADS 5 mammograms had 98.2%, 76.2%, and 100% appropriate f/u, respectively. From the alerts, all but one patient who refused biopsy received appropriate care. Conclusions: A practice intervention utilizing BI- RADS data linked to claims accurately identified missed f/u in a time frame when appropriate care can be delivered. This practice-based intervention has potential to improve the management of other cancers and chronic diseases. This intervention is being expanded on a community wide level. No significant financial relationships to disclose.
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Khehra, Baljit Singh, and Amar Partap Singh Pharwaha. "DIGITAL MAMMOGRAM ENHANCEMENT USING KAPUR MEASURE OF ENTROPY AND MATHEMATICAL MORPHOLOGY." Biomedical Engineering: Applications, Basis and Communications 25, no. 03 (May 30, 2013): 1350029. http://dx.doi.org/10.4015/s1016237213500294.
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Mammography is the most reliable, effective, low cost and highly sensitive method for early detection of breast cancer. Mammogram analysis usually refers to the processing of mammograms with the goal of finding abnormality presented in the mammogram. Mammogram enhancement is one of the most critical tasks in automatic mammogram image analysis. Main purpose of mammogram enhancement is to enhance the contrast of details and subtle features while suppressing the background heavily. In this paper, a hybrid approach is proposed to enhance the contrast of microcalcifications while suppressing the background heavily, using fuzzy logic and mathematical morphology. First, mammogram is fuzzified using Gaussian fuzzy membership function whose bandwidth is computed using Kapur measure of entropy. After this, mathematical morphology is applied on fuzzified mammogram. Mathematical morphology provides tools for the extraction of microcalcifications even if the microcalcifications are located on a nonuniform background. Main advantage of Kapur measure of entropy over Shannon entropy is that Kapur measure of entropy has α and β parameters that can be used as adjustable values. These parameters can play an important role as tuning parameters in the image processing chain for the same class of images. Experiments have been conducted on images of mini-Mammogram Image Analysis Society (MIAS) database (UK). Experiment results of the proposed approach are compared with histogram equalization (HE), contrast limited adaptive histogram equalization (CLAHE) and fuzzy histogram hyperbolization (FHH) which are well-established image enhancement techniques. In order to validate the results, several different kinds of standard test images (fatty, fatty-glandular and dense-glandular) of mini-MIAS database are considered. Objective image quality assessment parameters: Target-to-background contrast enhancement measurement based on standard deviation (TBCSD), target-to-background contrast enhancement measurement based on entropy (TBCE), contrast improvement index (CII), peak signal-to-noise ratio (PSNR) and average signal-to-noise ratio (ASNR) are used to evaluate the performance of proposed approach. The experimental results show that the proposed approach performs well. This study can be a part of developing a computer-aided diagnosis (CAD) system for early detection of breast cancer.
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Bhuvaneswari, E., and T. Ravi. "Privacy Preserving with M-SVM Classifier of Tumor Classification in Mammography Images Using Multiple Otsu'S Thresholding Technique." Journal of Computational and Theoretical Nanoscience 15, no. 2 (February 1, 2018): 697–705. http://dx.doi.org/10.1166/jctn.2018.7146.
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Breast Cancer is formed by an abnormal development of cells in breast. The cells of body separate in an incessant method and occupy to surrounding tissues. It is the important reason of death amongst women and after lung cancer breast cancer is second cause of women deaths. Early breast cancer detection can lead to death rate decrease. The mammography is executed to discover the breast cancer tumor at earlier stages. Early breast cancer tumors detection based on the both the radiologists capability to read mammogram images and image quality. The tumors classification is a medical application that set a huge issue for in the breast cancer recognition area. Therefore, in this paper, a multiple otsu's thresholding method is presented with Mutlti-class SVM (M-SVM) classifier to enhance the tumor classification in mammogram images for cancer tumor detection. In this process, elimination of artifacts, noise and surplus parts that are presented in mammogram images by employing preprocessing tasks and after that it improves the mammogram image contrast utilizing CLAHE (Contrast Limited Adaptive Histrogram Equalization) technique for simpler recognition of tumors in breast. We segment the images using Multiple Otsu's thresholding technique to identify the region of interest in mammogram image after preprocessing and image enhancement. The GLDM (Gray Level Difference Method) is exploited to extract the features from the mammogram image. Feature extraction has been employed to with hindsight examine screening mammograms in use prior to the malignant mass discovery for early breast cancer tumor detection. The extracted features can be given to the M-SVM Classifier to classify the tumor in mammogram image into malignant, benign or normal based on the features. The classification accurateness based on the stage of feature extraction. Results of mammogram image is planned by classification and lastly image categorized into Normal, malignant or Benign. Experimental results of proposed method can show that this presented technique executes well with the accurateness of classification reaching almost 84% in evaluation with existing algorithms.
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Manik, Ritika, Connor B. Grady, Sara Ginzberg, Christine E. Edmonds, Leisha C. Elmore, Julia T. Lewandowski, Rebecca A. Hubbard, and Oluwadamilola Motunrayo Fayanju. "Mammographic follow-up before and during the COVID-19 pandemic." Journal of Clinical Oncology 40, no. 28_suppl (October 1, 2022): 122. http://dx.doi.org/10.1200/jco.2022.40.28_suppl.122.
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122 Background: Mammography adapted during the COVID-19 pandemic to accommodate social-distancing guidelines and minimize risk of exposure, but it is unclear how these accommodations potentially provoked existing inefficiencies or illuminated opportunities to redress them. The goal of this study was to compare rates of (1) diagnostic follow-up after a BIRADS 0 (i.e., incomplete) screening mammogram and (2) biopsy following a BIRADS 4 or 5 (i.e., biopsy recommended) diagnostic mammogram before and after onset of the pandemic. Methods: We included women ≥18y who underwent a BIRADS 0 screening mammogram and/or BIRADS 4-5 diagnostic mammogram at our institution from 3/15/19-3/15/21. Given seasonal variation in care receipt, pre-COVID (3/15/2019-3/15/20) and COVID (3/15/20-3/15/21) time periods were compared at a quarterly level. Case-mix adjusted associations between time-to-follow-up and COVID vs pre-COVID quarters (Q1-4) were estimated using multivariate Cox proportional hazards models. Results: We identified 17,918 women (Asian: 985, Black: 4054, Hispanic: 840, White: 11,302) who received a total of 14,388 BIRADS 0 screening and 6410 BIRADS 4 or 5 diagnostic mammograms. There were far fewer diagnostic mammograms in COVID Q1 vs pre-COVID Q1 (Table), and they were more likely to be followed up with biopsy (HR 1.21 [95% CI 1.03-1.44], p = 0.023). COVID Q3 (HR 0.92 [95% CI 0.86-0.98], p = 0.002) and Q4 (HR 0.88 [95% CI 0.83-0.95], p < 0.001) screens were less likely to be followed up with diagnostic mammograms but volumes were higher vs the respective pre-COVID quarters (Table). However, COVID Q3 patients with BIRADS 4 or 5 mammograms were 18% more likely to undergo biopsy than their pre-COVID Q3 counterparts (HR 1.18 [95% CI 1.07-1.31], p < 0.0001, Table) despite higher COVID volumes. Conclusions: Early in the pandemic, patients were more likely to receive mammographic follow-up, potentially due to lower patient volumes and enforced strategies for more efficient, less time-intensive care delivery. These gains were lost with regards to diagnostic follow-up for screening mammograms but maintained with regards to performing biopsies. As volumes return to or surpass pre-pandemic levels, health systems must work to identify and preserve operational efficiencies gained during the early pandemic.[Table: see text]
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Bobo, Janet Kay, Denita Dean, Christina Stovall, Margaret Mendez, and Lee Caplan. "Factors That May Discourage Annual Mammography among Low-Income Women with Access to Free Mammograms: A Study Using Multi-Ethnic, Multiracial Focus Groups." Psychological Reports 85, no. 2 (October 1999): 405–16. http://dx.doi.org/10.2466/pr0.1999.85.2.405.
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Age-eligible women enrolled in the National Breast and Cervical Cancer Early Detection Program can obtain free or low-cost mammograms annually, but many do not routinely complete rescreening. This study investigated the rescreening behavior of low-income women by conducting 8 focus groups in Texas with enrollees who had access to free mammograms. Concerns mentioned in the focus groups included fear of radiation, anxiety that the test might not find a cancer that was there, and worries that cancer might be detected. In all groups, some women mentioned the embarrassment, discomfort, or pain they experienced during a prior mammogram, although no one indicated they would refuse to have another mammogram because of these concerns. Findings highlight the useful insights that can be obtained from focus groups and underscore the need for more research on aspects of the experience of mammography that may affect rescreening.
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Suresh, Attili V. S., Rakesh Sharma, Praveen K. Dadireddy, Rajkumar Bunga, Krishna M. Lalukota, Pradeep K. Reddy, and Guru N. Reddy. "Vascular calcifications in mammogram and correlate them with coronary artery diseases: a retrospective study." International Journal Of Community Medicine And Public Health 9, no. 7 (June 28, 2022): 2961. http://dx.doi.org/10.18203/2394-6040.ijcmph20221766.
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Background: The aim was to evaluate the incidental findings of vascular calcifications in mammogram and correlate them with coronary artery diseases clinically and radiologically in retrospective manner from multiple institutes.Methods: It was a retrospective analysis of all mammograms done in past one year for routine screening from three institutes during 2020-2021. Those with positive calcifications were interviewed and for the willing population we evaluated the coronary artery disease status clinically and radiologically.Results: There were 335 subjects whose complete case records were available. Out of these patients 38 had some form of calcifications found on mammograms with varying severity. Out of the 38 subjects 31 had coronary angiogram testing (either conventional or CT). We could observe 26 of them harbouring moderate to severe atherosclerotic changes and varying degree of coronary artery stenosis.Conclusions: Observation of calcifications on mammogram should prompt the screening physician to encourage the patients for their cardiac risk assessment.
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Mohamed, Basma A., and Heba M. Afify. "MAMMOGRAM COMPRESSION TECHNIQUES USING HAAR WAVELET AND QUADTREE DECOMPOSITION-BASED IMAGE ENHANCEMENT." Biomedical Engineering: Applications, Basis and Communications 29, no. 05 (October 2017): 1750038. http://dx.doi.org/10.4015/s1016237217500387.
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Biomedical image compression plays an important role in the medical field. Mammograms are medical images used in the early detection of breast cancer. Mammogram image compression is a challenging task because these images contain information that occupies huge size for storage. The aim of image compression is to reduce the image size and the time taken for recovering the original image without any loss. In this paper, two different techniques of mammogram compression are introduced. The proposed algorithm includes two main steps. First, a preprocessing step is applied to enhance the image, and then a compression algorithm is applied to the enhanced image. The algorithm is tested using 322 mammogram images from the online MIAS database. Three parameters are used to evaluate the performance of the compression techniques; compression ratio (CR), Peak Signal to Noise Ratio (PSNR) and processing time. According to the results, Haar wavelet-based compression for enhanced images is better in terms of CR of 26.25% and PSNR of 47.27[Formula: see text]dB.
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Cox-Joseph, Terry. "Mammogram." Chest 142, no. 4 (October 2012): 1070. http://dx.doi.org/10.1378/chest.11-2847.
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Don, S., Duckwon Chung, K. Revathy, Eunmi Choi, and Dugki Min. "A New Approach for Mammogram Image Classification Using Fractal Properties." Cybernetics and Information Technologies 12, no. 2 (June 1, 2012): 69–83. http://dx.doi.org/10.2478/cait-2012-0013.
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Abstract Accurate classification of images is essential for the analysis of mammograms in computer aided diagnosis of breast cancer. We propose a new approach to classify mammogram images based on fractal features. Given a mammogram image, we first eliminate all the artifacts and extract the salient features such as Fractal Dimension (FD) and Fractal Signature (FS). These features provide good descriptive values of the region. Second, a trainable multilayer feed forward neural network has been designed for the classification purposes and we compared the classification test results with K-Means. The result reveals that the proposed approach can classify with a good performance rate of 98%.
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D, Saraswathi, and Srinivasan E. "Mammogram Analysis using League Championship Algorithm Optimized Ensembled FCRN Classifier." Indonesian Journal of Electrical Engineering and Computer Science 5, no. 2 (February 1, 2017): 451. http://dx.doi.org/10.11591/ijeecs.v5.i2.pp451-461.
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An intelligent mammogram diagnosis system can be very helpful for radiologist in detecting the abnormalities earlier than typical screening techniques. This paper investigates a new classification approach for detection of breast abnormalities in digital mammograms using League Championship Algorithm Optimized Ensembled Fully Complex valued Relaxation Network (LCA-FCRN). The proposed algorithm is based on extracting curvelet fractal texture features from the mammograms and classifying the suspicious regions by applying a pattern classifier. The whole system includes steps for pre-processing, feature extraction, feature selection and classification to classify whether the given input mammogram image is normal or abnormal. The method is applied to MIAS database of 322 film mammograms. The performance of the CAD system is analysed using Receiver Operating Characteristic (ROC) curve. This curve indicates the trade-offs between sensitivity and specificity that is available from a diagnostic system, and thus describes the inherent discrimination capacity of the proposed system. The result shows that the area under the ROC curve of the proposed algorithm is 0.985 with a sensitivity of 98.1% and specificity of 92.105%. Experimental results demonstrate that the proposed method can form an effective CAD system, and achieve good classification accuracy.
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Branch, Fallon, Isabella Santana, and Jay Hegdé. "Biasing Influence of ‘Mental Shortcuts’ on Diagnostic Decision-Making: Radiologists Can Overlook Breast Cancer in Mammograms When Prior Diagnostic Information Is Available." Diagnostics 12, no. 1 (January 4, 2022): 105. http://dx.doi.org/10.3390/diagnostics12010105.
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When making decisions under uncertainty, people in all walks of life, including highly trained medical professionals, tend to resort to using ‘mental shortcuts’, or heuristics. Anchoring-and-adjustment (AAA) is a well-known heuristic in which subjects reach a judgment by starting from an initial internal judgment (‘anchored position’) based on available external information (‘anchoring information’) and adjusting it until they are satisfied. We studied the effects of the AAA heuristic during diagnostic decision-making in mammography. We provided practicing radiologists (N = 27 across two studies) a random number that we told them was the estimate of a previous radiologist of the probability that a mammogram they were about to see was positive for breast cancer. We then showed them the actual mammogram. We found that the radiologists’ own estimates of cancer in the mammogram reflected the random information they were provided and ignored the actual evidence in the mammogram. However, when the heuristic information was not provided, the same radiologists detected breast cancer in the same set of mammograms highly accurately, indicating that the effect was solely attributable to the availability of heuristic information. Thus, the effects of the AAA heuristic can sometimes be so strong as to override the actual clinical evidence in diagnostic tasks.
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Ganguly, Anisha, Kelsey K. Baker, Mary Weber Redman, Adelaide McClintock, and Rachel Lynn Yung. "Racial disparities in the screening mammography continuum within a diverse healthcare system." Journal of Clinical Oncology 40, no. 16_suppl (June 1, 2022): e18580-e18580. http://dx.doi.org/10.1200/jco.2022.40.16_suppl.e18580.
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e18580 Background: Racial disparities in breast cancer have been extensively characterized in the literature. Access to screening mammography is a significant contributor to breast cancer disparities. Decreased access to mammography translates into disparities in breast cancer through delayed presentation to care. The USPSTF recommends biannual screening mammography of women 50-74 with average risk for breast cancer. Methods: We conducted a cross-sectional analysis of encounter-level data for Black and white female primary care patients aged 50-74 in the University of Washington Medicine system who were due for mammogram in 2019. Completion of steps of the mammography continuum (referral, scheduling, and completion of mammogram) were compared among Black and white women. Multivariable logistic regression was used to explore race and mammogram completion, adjusting for age, language, referral, insurance, clinical site, wellness visit utilization, and history of prior mammogram. Results: The study population comprised 18,156 women of whom 2,059 (11.3%) were Black and 16,097 (88.7%) were white. Among Black women, 26.8% were referred to screening mammogram, 12.9% self-referred, 39.3% were scheduled, and 21.4% completed their mammogram, compared to 21.1%, 20.6%, 41.4%, and 26.9% among white women respectively. The greatest attrition among Black women was in the step of completing a mammogram after it was scheduled, which was higher seen in white women at the same step. Adjusted analyses demonstrated an association between Black race and lower rates of screening mammography completion (OR 0.85, [95% CI 0.78-0.98], p = 0.02). Conclusions: Our analysis assessed racial disparities in steps of the screening mammography continuum in a large, diverse health system. Black race was associated with lower screening mammography completion after adjustment for several covariates. Provider-initiated referral was higher for Black women, while self-referral was higher for white women. Both Black and white women experienced highest attrition from no-show rates for scheduled mammograms, though attrition was greater for Black women. These findings have systems implications for future interventions, such as patient navigators or system-driven nudges, to mitigate disparities in breast cancer screening. [Table: see text]
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Ganguly, Anisha, Kelsey K. Baker, Mary Weber Redman, Adelaide McClintock, and Rachel Lynn Yung. "Racial disparities in the screening mammography continuum within a diverse healthcare system." Journal of Clinical Oncology 40, no. 16_suppl (June 1, 2022): e18580-e18580. http://dx.doi.org/10.1200/jco.2022.40.16_suppl.e18580.
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e18580 Background: Racial disparities in breast cancer have been extensively characterized in the literature. Access to screening mammography is a significant contributor to breast cancer disparities. Decreased access to mammography translates into disparities in breast cancer through delayed presentation to care. The USPSTF recommends biannual screening mammography of women 50-74 with average risk for breast cancer. Methods: We conducted a cross-sectional analysis of encounter-level data for Black and white female primary care patients aged 50-74 in the University of Washington Medicine system who were due for mammogram in 2019. Completion of steps of the mammography continuum (referral, scheduling, and completion of mammogram) were compared among Black and white women. Multivariable logistic regression was used to explore race and mammogram completion, adjusting for age, language, referral, insurance, clinical site, wellness visit utilization, and history of prior mammogram. Results: The study population comprised 18,156 women of whom 2,059 (11.3%) were Black and 16,097 (88.7%) were white. Among Black women, 26.8% were referred to screening mammogram, 12.9% self-referred, 39.3% were scheduled, and 21.4% completed their mammogram, compared to 21.1%, 20.6%, 41.4%, and 26.9% among white women respectively. The greatest attrition among Black women was in the step of completing a mammogram after it was scheduled, which was higher seen in white women at the same step. Adjusted analyses demonstrated an association between Black race and lower rates of screening mammography completion (OR 0.85, [95% CI 0.78-0.98], p = 0.02). Conclusions: Our analysis assessed racial disparities in steps of the screening mammography continuum in a large, diverse health system. Black race was associated with lower screening mammography completion after adjustment for several covariates. Provider-initiated referral was higher for Black women, while self-referral was higher for white women. Both Black and white women experienced highest attrition from no-show rates for scheduled mammograms, though attrition was greater for Black women. These findings have systems implications for future interventions, such as patient navigators or system-driven nudges, to mitigate disparities in breast cancer screening. [Table: see text]
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Zyout, Imad, Ikhlas Abdel-Qader, and Christina Jacobs. "Bayesian Classifier with Simplified Learning Phase for Detecting Microcalcifications in Digital Mammograms." International Journal of Biomedical Imaging 2009 (2009): 1–13. http://dx.doi.org/10.1155/2009/767805.
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Detection of clustered microcalcifications (MCs) in mammograms represents a significant step towards successful detection of breast cancer since their existence is one of the early signs of cancer. In this paper, a new framework that integrates Bayesian classifier and a pattern synthesizing scheme for detecting microcalcification clusters is proposed. This proposed work extracts textural, spectral, and statistical features of each input mammogram and generates models of real MCs to be used as training samples through a simplified learning phase of the Bayesian classifier. Followed by an estimation of the classifier's decision function parameters, a mammogram is segmented into the identified targets (MCs) against background (healthy tissue). The proposed algorithm has been tested using 23 mammograms from the mini-MIAS database. Experimental results achieved MCs detection with average true positive (sensitivity) and false positive (specificity) of 91.3% and 98.6%, respectively. Results also indicate that the modeling of the real MCs plays a significant role in the performance of the classifier and thus should be given further investigation.
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Padela, Aasim I., Sana Malik, Syeda Akila Ally, Michael Quinn, Stephen Hall, and Monica Peek. "Reducing Muslim Mammography Disparities: Outcomes From a Religiously Tailored Mosque-Based Intervention." Health Education & Behavior 45, no. 6 (April 19, 2018): 1025–35. http://dx.doi.org/10.1177/1090198118769371.
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Objective. To describe the design of, and participant-level outcomes related to, a religiously tailored, peer-led group education program aimed at enhancing Muslim women’s mammography intention. Method. Using a community-engaged approach and mixed methods, we identified and addressed barrier beliefs impeding mammography screening among Muslim American women. Our religiously tailored, mosque-based, peer-led intervention involved facilitated discussions and expert-led didactics conveying health-related religious teachings, and information about the benefits and process of mammography. Barrier beliefs were addressed through reframing, reprioritizing, or reforming such beliefs. Participant surveys were collected preintervention, postintervention, 6 months postintervention, and 1 year postintervention. These measured changes in mammography intention, likelihood, confidence, and resonance with barrier and facilitator beliefs. Results. A total of 58 Muslim women (mean age = 50 years) that had not had a mammogram in the past 2 years participated in the two-session program. Self-reported likelihood of obtaining a mammogram increased significantly ( p = .01) and coincided with a positive trend in confidence ( p = .08). Individuals with higher agreement with barrier beliefs preintervention had lower odds for positive change in likelihood (odds ratio = 0.80, p = .03), while those who were married had higher odds for positive change in likelihood (odds ratio = 37.69, p = .02). At 1-year follow-up, 22 participants had obtained a mammogram. Conclusion. Our pilot mosque-based intervention demonstrated efficacy in improving Muslim women’s self-reported likelihood of obtaining mammograms, and increased their mammography utilization, with nearly 40% obtaining a mammogram within 12 months of the intervention. Impact. Our conceptual model for religiously tailoring messages, along with its implementation curriculum, proved effective in enhancing the likelihood and receipt of mammograms among Muslim American women. Accordingly, our work advances both the theory and practice of faith-based interventions and provides a model for addressing Muslim women’s cancer screening disparities.
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Ojo, Ayotomiwa, Shawn Johnson, Parsa Erfani, Ruby Guo, Andrea Garmilla, Arushi Saini, Brian Benitez, et al. "A high-touch outreach model to re-engage patients in mammogram screening." Journal of Clinical Oncology 40, no. 16_suppl (June 1, 2022): e18555-e18555. http://dx.doi.org/10.1200/jco.2022.40.16_suppl.e18555.
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e18555 Background: Disparities in cancer screening have been well documented during the Covid-19 pandemic. However, there are limited patient-reported data describing the prevalence and drivers of patient hesitancy towards cancer screening and willingness to resume screening. As health systems continue to experience pandemic-related capacity strain, there is an urgent need for innovative models of re-engaging patients in preventive screening. To address this issue, we developed a medical student-led, high-touch outreach model to re-engage primary care patients at Brookside Community Health Center in cancer screening. Methods: We iteratively optimized semi-structured call scripts and surveys in English and Spanish to contact patients overdue for mammography screening. Student callers included medical and pre-medical students with native Spanish fluency. Using the call script, students identified patient-reported barriers and facilitated mammogram scheduling for consenting patients. For consenting patients, student callers placed a telephone encounter with a pended screening mammogram order in the electronic medical record. PCP confirmation of the order triggered outreach by the radiology department for mammogram scheduling. Patients also received reminder calls from students the week of their appointment. Primary outcomes include screening consent rates, mammogram scheduling and completion rates, and screening results. Patient survey responses were securely recorded using the REDCap survey platform. Results: 198 patients were eligible for the intervention. 60% are primarily Spanish-speaking and 81% are insured by Medicaid. 145 patients (73%) have successfully been contacted, of which 129 (89%) consented for mammogram screening. 74 (57%) of the consenting patients have scheduled their mammogram and 38 (29%) have completed their mammogram. 36% of consenting Spanish-speaking patients with active mammogram orders did not have a mammogram scheduled, compared to 9% of consenting English-speaking. To date, 6 patients had abnormal mammograms requiring subsequent diagnostic imaging, and 1 patient was diagnosed with ductal carcinoma in situ requiring oncologic care. Qualitative analysis of patient surveys found that primary barriers to screening included factors associated with the Covid-19 pandemic (32.9% of contacted patients), lack of awareness of overdue status (25.9%) and patient unavailability (e.g. outside of country) (20%). Conclusions: In this single-center quality improvement study, we found that patients had a high willingness to engage in cancer screening during the pandemic and that trainees can play a vital role in re-engaging patients in preventative care. The disparity between Spanish and English-speaking patients’ ability to schedule a mammogram after the consent process suggests that patients with limited English proficiency face additional challenges in accessing screenings.
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Susilawati, Indah. "KLASIFIKASI CITRA MAMOGRAFI MENGGUNAKAN JARINGAN SYARAF TIRUAN." Elektron : Jurnal Ilmiah 1, no. 2 (December 18, 2009): 97–103. http://dx.doi.org/10.30630/eji.1.2.25.
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There are abnormalities in breast tissue which can be detected by mammogram images analysis. One of those abnormalities is microcalcification. Microcalcifications are small calcium deposits in the breast tissue that can be seen only on a mammogram and can be an indicator of breast cancer. The main objective of this research is to automatically recognize the pattern of two types of breast tissues, i.e. normal tissue and breast tissue which contain microcalcifications in digital mammograms using Matlab software tools. In this research, pattern recognition is carried out using Artificial Neural Network (ANN), i.e. LVQ (Learning Vector Quantization). The pattern recognition is formulated as a supervised-learning problem and classification was based on six-feature input given to the ANN. The system recognizes the pattern in three steps. Firstly, a tophat transformation is applied on the images, and then features of the images are extracted based on images pixel values. Finally, image classification is carried out in recognizing the pattern. The research uses 26 digital mammograms, consist of 16 normal mammograms and 10 mammograms which contain microcalcifications. The results show that the LVQ best performance in recognizing the pattern is 97%, using learning rate and decrement of learning rate equal to 0.1.