Journal articles on the topic 'Breast Tumors Classification'
To see the other types of publications on this topic, follow the link: Breast Tumors Classification.
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Breast Tumors Classification.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Houseman, Eugene Andrέs, and Tan A. Ince. "Normal Cell-Type Epigenetics and Breast Cancer Classification: A Case Study of Cell Mixture–Adjusted Analysis of DNA Methylation Data from Tumors." Cancer Informatics 13s4 (January 2014): CIN.S13980. http://dx.doi.org/10.4137/cin.s13980.
Full textAbstract:
Historically, breast cancer classification has relied on prognostic subtypes. Thus, unlike hematopoietic cancers, breast tumor classification lacks phylogenetic rationale. The feasibility of phylogenetic classification of breast tumors has recently been demonstrated based on estrogen receptor (ER), androgen receptor (AR), vitamin D receptor (VDR) and Keratin 5 expression. Four hormonal states (HR0–3) comprising 11 cellular subtypes of breast cells have been proposed. This classification scheme has been shown to have relevance to clinical prognosis. We examine the implications of such phylogenetic classification on DNA methylation of both breast tumors and normal breast tissues by applying recently developed deconvolution algorithms to three DNA methylation data sets archived on Gene Expression Omnibus. We propose that breast tumors arising from a particular cell-of-origin essentially magnify the epigenetic state of their original cell type. We demonstrate that DNA methylation of tumors manifests patterns consistent with cell-specific epigenetic states, that these states correspond roughly to previously posited normal breast cell types, and that estimates of proportions of the underlying cell types are predictive of tumor phenotypes. Taken together, these findings suggest that the epigenetics of breast tumors is ultimately based on the underlying phylogeny of normal breast tissue.
2
STEPONAVIČIENĖ, Laura, Daiva GUDAVIČIENĖ, and Raimundas MEŠKAUSKAS. "Rare types of breast carcinoma." Acta medica Lituanica 19, no. 2 (June 1, 2012): 81–91. http://dx.doi.org/10.6001/actamedica.v19i2.2314.
Full textAbstract:
Background. Breast cancer is a heterogeneous disease that encompasses several distinct entities with remarkably different characteristics. One of very important cancer characteristics is its histological type. Materials and methods. We used Pubmed and Medscape databases and analyzed original articles and literature reviews about rare histological types of breast cancer. Results and discussion. World Health Organization (WHO) presents a detailed classification of breast cancers. According to this classification, cancers are divided into epithelial, mesenchymal, fibroepithelial tumors. Malignant lymphoma, metastatic tumors can also be found in the breast. WHO also marks tumors of the nipple, male breast cancer and myoepithelial lesions. In this paper, only the invasive epithelial tumors are discussed. Most tumors are derived from mammary ductal epithelium, and up to 75% of the breast cancers are ductal carcinomas. The second most common epithelial tumor type is invasive lobular carcinoma which comprises 5–15% of the group. There are more than a dozen variants which are less common. They comprise less than 10% of breast tumors. Their clinical behavior can differ greatly. So, it is important to know their main characteristics in order to make the best treatment choice and to foresee prognosis. We shortly describe the epidemiology, diagnostics, clinical and immunophenotypic features, prognosis and predictive factors of rare epithelial breast tumors.
3
Zhuang, Zhemin, Zengbiao Yang, Shuxin Zhuang, Alex Noel Joseph Raj, Ye Yuan, and Ruban Nersisson. "Multi-Features-Based Automated Breast Tumor Diagnosis Using Ultrasound Image and Support Vector Machine." Computational Intelligence and Neuroscience 2021 (May 19, 2021): 1–12. http://dx.doi.org/10.1155/2021/9980326.
Full textAbstract:
Breast ultrasound examination is a routine, fast, and safe method for clinical diagnosis of breast tumors. In this paper, a classification method based on multi-features and support vector machines was proposed for breast tumor diagnosis. Multi-features are composed of characteristic features and deep learning features of breast tumor images. Initially, an improved level set algorithm was used to segment the lesion in breast ultrasound images, which provided an accurate calculation of characteristic features, such as orientation, edge indistinctness, characteristics of posterior shadowing region, and shape complexity. Simultaneously, we used transfer learning to construct a pretrained model as a feature extractor to extract the deep learning features of breast ultrasound images. Finally, the multi-features were fused and fed to support vector machine for the further classification of breast ultrasound images. The proposed model, when tested on unknown samples, provided a classification accuracy of 92.5% for cancerous and noncancerous tumors.
4
MENUT, OLIVIER, RANGARAJ M. RANGAYYAN, and J. E. LEO DESAUTELS. "PARABOLIC MODELING AND CLASSIFICATION OF BREAST TUMORS." International Journal of Shape Modeling 03, no. 03n04 (September 1997): 155–66. http://dx.doi.org/10.1142/s0218654397000124.
Full text
5
Ng, Shun Leung, and Walter F. Bischof. "Automated detection and classification of breast tumors." Computers and Biomedical Research 25, no. 3 (June 1992): 218–37. http://dx.doi.org/10.1016/0010-4809(92)90040-h.
Full text
6
Guseynov, Arif, T. Guseynov, and V. Odincov. "BENIGN TUMORS BREAST GLASS." Clinical Medicine and Pharmacology 7, no. 2 (November 9, 2021): 2–11. http://dx.doi.org/10.12737/2409-3750-2021-7-2-2-11.
Full textAbstract:
The lecture provides relevant information for doctors of various specialties: oncologists, surgeons, mammologists, general practitioners on the problems of diagnosis and treatment of benign breast formations. The issues of etiology and pathogenesis, classification and clinical picture of various formations are highlighted, diagnostic methods, differential diagnostics, treatment tactics and methods of surgical treatment are described in detail.
7
Krasnoslobodtsev, Nikolay, Evgeny Shapiro, Tatyana Alymova, and Natalya Kuharenko. "Some etiopathogenetic features of dogs’ breast tumors." E3S Web of Conferences 203 (2020): 01014. http://dx.doi.org/10.1051/e3sconf/202020301014.
Full textAbstract:
The article presents data on the epidemiological features of breast cancer in dogs, namely: the frequency of oncological pathologies in dogs in 2017-2019, the structure of oncological diseases, studied the age-sex characteristics of breast tumors, the frequency of certain risk factors leading to the development of breast neoplasms in dogs. From this study, it was found that neoplasms occurred in 4.8% of the dogs admitted to the clinic. In dogs, a mammary gland tumor was recorded in 153 individuals оf which, 150 females and 3 males. Breast tumors ranked first in localization (28% of all tumors), and skin tumors ranked second (8.5% of all tumors). In females, the first place is a breast tumor (45%), in males - skin tumors (13%). In most animals, the size of a breast tumor at its primary detection corresponded to the T2-T4 stage according to the TNM classification, and in some cases T4a-d. Out of 150 females with breast tumors, only 40 were castrated. This justifies the need for the formation of approaches to early diagnosis of breast tumors in domestic dogs, as well as to the study and prevention of risk factors.
8
Ouyang, Yali, Po-Hsiang Tsui, Shuicai Wu, Weiwei Wu, and Zhuhuang Zhou. "Classification of Benign and Malignant Breast Tumors Using H-Scan Ultrasound Imaging." Diagnostics 9, no. 4 (November 8, 2019): 182. http://dx.doi.org/10.3390/diagnostics9040182.
Full textAbstract:
Breast cancer is one of the most common cancers among women worldwide. Ultrasound imaging has been widely used in the detection and diagnosis of breast tumors. However, due to factors such as limited spatial resolution and speckle noise, classification of benign and malignant breast tumors using conventional B-mode ultrasound still remains a challenging task. H-scan is a new ultrasound technique that images the relative size of acoustic scatterers. However, the feasibility of H-scan ultrasound imaging in the classification of benign and malignant breast tumors has not been investigated. In this paper, we proposed a new method based on H-scan ultrasound imaging to classify benign and malignant breast tumors. Backscattered ultrasound radiofrequency signals of 100 breast tumors were used (48 benign and 52 malignant cases). H-scan ultrasound images were constructed with the radiofrequency signals by matched filtering using Gaussian-weighted Hermite polynomials. Experimental results showed that benign breast tumors had more red components, while malignant breast tumors had more blue components in H-scan ultrasound images. There were significant differences between the RGB channels of H-scan ultrasound images of benign and malignant breast tumors. We conclude H-scan ultrasound imaging can be used as a new method for classifying benign and malignant breast tumors.
9
Tran Thi Song, Huong, Yen Vo Thi Kim, and Quan Nguyen Phuoc Bao. "APPLICATION OF ELASTOGRAPHY FOR DIAGNOSIS BREAST TUMORS." Volume 8 Issue 6 8, no. 6 (December 2018): 8–14. http://dx.doi.org/10.34071/jmp.2018.6.1.
Full textAbstract:
Breast tumor is common in women. Benign tumors account for 80%, malignant tumors account for 20%. Breast cancer is the most common and deadly cancer among women, including Vietnam. Elastography, evaluates the stiffness of the tissue, helps to distinguish soft or hard tumors, which can help distinguish benign or malignant. Benign lesions tend to be softer than malignant lesions. There are two types of elastography: SE (Strain Elastography) and Shear Ware Elastography (SWE). In examining breast lesions, the maligne tumor tends to be stronger and the higher the velocity. Studies have shown that the SWE features should be combined with 2D ultrasound to complement the BIRADS classification. Elastography is a new technique that has emerged in the past few years, promising good diagnostic prospects, more and more research and application of elastography in diagnostics breast lesions. Breast elastogarphy, survey of hardness of breast cancer showed 4 times higher than that of benign tumor and 7 folds of normal breast tissue.
10
Muhtadi, Sabiq. "Breast Tumor Classification Using Intratumoral Quantitative Ultrasound Descriptors." Computational and Mathematical Methods in Medicine 2022 (March 7, 2022): 1–18. http://dx.doi.org/10.1155/2022/1633858.
Full textAbstract:
Breast cancer is a global epidemic, responsible for one of the highest mortality rates among women. Ultrasound imaging is becoming a popular tool for breast cancer screening, and quantitative ultrasound (QUS) techniques are being increasingly applied by researchers in an attempt to characterize breast tissue. Several different quantitative descriptors for breast cancer have been explored by researchers. This study proposes a breast tumor classification system using the three major types of intratumoral QUS descriptors which can be extracted from ultrasound radiofrequency (RF) data: spectral features, envelope statistics features, and texture features. A total of 16 features were extracted from ultrasound RF data across two different datasets, of which one is balanced and the other is severely imbalanced. The balanced dataset contains RF data of 100 patients with breast tumors, of which 48 are benign and 52 are malignant. The imbalanced dataset contains RF data of 130 patients with breast tumors, of which 104 are benign and 26 are malignant. Holdout validation was used to split the balanced dataset into 60% training and 40% testing sets. Feature selection was applied on the training set to identify the most relevant subset for the classification of benign and malignant breast tumors, and the performance of the features was evaluated on the test set. A maximum classification accuracy of 95% and an area under the receiver operating characteristic curve (AUC) of 0.968 was obtained on the test set. The performance of the identified relevant features was further validated on the imbalanced dataset, where a hybrid resampling strategy was firstly utilized to create an optimal balance between benign and malignant samples. A maximum classification accuracy of 93.01%, sensitivity of 94.62%, specificity of 91.4%, and AUC of 0.966 were obtained. The results indicate that the identified features are able to distinguish between benign and malignant breast lesions very effectively, and the combination of the features identified in this research has the potential to be a significant tool in the noninvasive rapid and accurate diagnosis of breast cancer.
11
Schlechter, Benjamin L., Qiong Yang, Pamela S. Larson, Arina Golubeva, Rita A. Blanchard, Antonio de Las Morenas, and Carol L. Rosenberg. "Quantitative DNA Fingerprinting May Distinguish New Primary Breast Cancer From Disease Recurrence." Journal of Clinical Oncology 22, no. 10 (May 15, 2004): 1830–38. http://dx.doi.org/10.1200/jco.2004.05.123.
Full textAbstract:
Purpose Approximately 10% of women with breast cancer develop a second breast tumor, either a new primary or a recurrence. Differentiating between these entities using standard clinical and pathologic criteria remains challenging. Ambiguous cases arise, and misclassifications may occur. We investigated whether quantitative DNA fingerprinting, based on allele imbalance (AI) or loss of heterozygosity (LOH), could evaluate clonality and distinguish second primary breast cancer from recurrence. Methods We developed a scoring system based on the AI/LOH fingerprints of 20 independent breast tumors and generated a decision rule to classify any breast tumor pair as related or unrelated. We validated this approach on eight related tumors (cancers and synchronous positive lymph nodes). Finally, we analyzed paired tumors from 13 women (bilateral cancers, primary tumors and contralateral positive axillary lymph nodes, or two ipsilateral tumors). Each pair's genetic classification was compared with their clinical diagnosis and outcome. Results Each independent cancer had a unique fingerprint. Every tumor pair's relationship was quantifiable. Six of eight related tumor pairs were genetically classified correctly, two were indeterminate, and none were misclassified. Among the 13 women with two cancers, four of five clinically indeterminate pairs could be classified genetically. In three of 13 women, the pair's classification contradicted the clinical diagnosis. These women had bilateral cancers genetically classified as related and disease progression. This challenges the paradigm that bilateral cancers represent independent tumors. Overall, women with tumors genetically classified as related had poorer outcomes. Conclusion Quantitative AI/LOH fingerprinting is a potentially valuable tool to improve diagnosis and optimize treatment for the growing number of second breast malignancies.
12
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.
Full textAbstract:
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.
13
Richard, Vinitha, Matthew G. Davey, Heidi Annuk, Nicola Miller, Róisín M. Dwyer, Aoife Lowery, and Michael J. Kerin. "MicroRNAs in Molecular Classification and Pathogenesis of Breast Tumors." Cancers 13, no. 21 (October 23, 2021): 5332. http://dx.doi.org/10.3390/cancers13215332.
Full textAbstract:
The current clinical practice of breast tumor classification relies on the routine immunohistochemistry-based expression analysis of hormone receptors, which is inadequate in addressing breast tumor heterogeneity and drug resistance. MicroRNA expression profiling in tumor tissue and in the circulation is an efficient alternative to intrinsic molecular subtyping that enables precise molecular classification of breast tumor variants, the prediction of tumor progression, risk stratification and also identifies critical regulators of the tumor microenvironment. This review integrates data from protein, gene and miRNA expression studies to elaborate on a unique miRNA-based 10-subtype taxonomy, which we propose as the current gold standard to allow appropriate classification and separation of breast cancer into a targetable strategy for therapy.
14
Cuk, Mirjana, Radoslav Gajanin, Milos Malis, Drazan Eric, Nenad Lalovic, and Helena Maric. "The importance of cytology in diagnosing rare breast carcinoma: Two case reports." Medical review 66, no. 1-2 (2013): 86–91. http://dx.doi.org/10.2298/mpns1302086c.
Full textAbstract:
Introduction. This paper presents two cases of very rare tumors of breast: breast sebaceos carcinoma, which has rarely been described in medical literature, and breast carcinosarcoma. Morphological characteristics and biological behavior of sebaceos carcinoma are still rather vague. Carcinosarcoma of the breast is a rare malignancy with distinct cell lines described as a breast carcinoma of ductal type with a sarcoma-like component. Case report. The first presented case is a 73-year-old female referred to our hospital in January 2008 with tumor of the right breast in the upper outer region of the breast and enlarged lymph nodes in the right axillary region. The second presented case is a 51-year-old female with carcinosarcoma, also a very rare primary breast tumor. She was admitted to our hospital in June 2011 with history of lump in the upper and lower outer quadrant of the left breast. In both cases, biopsy of tumor tissue was carried out with a thin needle, i.e. the aspiration cytology was applied as a diagnostic method, and during the operation the fast diagnostics of frozen sections and cytologic diagnostics were done. Although this methodology is important in diagnosis, in both cases it showed certain limitations in diagnosing such rare tumors. The final diagnosis was made after carefully synthesizing the histological findings and immunohistochemical phenotype. Conclusion. An accurate classification of breast tumors on cytological preparations is not possible in case of poorly differentiated and rare tumors. A careful and accurate classification of these tumors is necessary.
15
Behar, N., and M. Shrivastava. "A Novel Model for Breast Cancer Detection and Classification." Engineering, Technology & Applied Science Research 12, no. 6 (December 15, 2022): 9496–502. http://dx.doi.org/10.48084/etasr.5115.
Full textAbstract:
Breast cancer is a dreadful disease that affects women globally. The occurrences of masses in the breast region are the main cause of breast cancer development. It is important to detect breast cancer as early as possible as this might increase the survival rate. The existing research methodologies have the problems of increased computation complexity and low detection accuracy. To overcome such problems, this paper proposes an efficient breast cancer detection and classification system based on mammogram images. Initially, the mammogram images are preprocessed so unwanted regions and noise are removed and the contrast of the images is enhanced using Homo Morphic Adaptive Histogram Equalization (HMAHE). Then, the breast boundaries are identified with the use of the canny edge detector. After that, the pectoral muscles present in the images are detected and removed using the Global Pixel Intensity-based Thresholding (GPIT) method. Then, the tumors are identified and segmented by the Centroid-based Region Growing Segmentation (CRGS) algorithm. Next, the tumors are segmented and clustered and feature extraction is carried out from the clustered tumors. After that, the necessary features are selected by using the Chaotic Function-based Black Widow Optimization Algorithm (CBWOA). The selected features are utilized by the Convolutional Squared Deviation Neural Network Classifier (CSDNN) which classifies the tumors into six different categories. The proposed model effectively detects and classifies breast tumors and its efficiency is experimentally proved by comparison with the existing techniques.
16
Ardakani, Ali Abbasian, Akbar Gharbali, and Afshin Mohammadi. "Classification of Breast Tumors Using Sonographic Texture Analysis." Journal of Ultrasound in Medicine 34, no. 2 (February 2015): 225–31. http://dx.doi.org/10.7863/ultra.34.2.225.
Full text
17
Zhang, Dao-Hai, Manuel Salto-Tellez, Lily-Lily Chiu, Liang Shen, and Evelyn Siew-Chuan Koay. "Tissue microarray study for classification of breast tumors." Life Sciences 73, no. 25 (November 2003): 3189–99. http://dx.doi.org/10.1016/j.lfs.2003.05.006.
Full text
18
Pohlman, Scott, Kimerly A. Powell, Nancy A. Obuchowski, William A. Chilcote, and Sharon Grundfest-Broniatowski. "Quantitative classification of breast tumors in digitized mammograms." Medical Physics 23, no. 8 (August 1996): 1337–45. http://dx.doi.org/10.1118/1.597707.
Full text
19
Ozaki, Yukinori, Sakiko Miura, Ryosuke Oki, Teppei Morikawa, and Keita Uchino. "Neuroendocrine Neoplasms of the Breast: The Latest WHO Classification and Review of the Literature." Cancers 14, no. 1 (December 31, 2021): 196. http://dx.doi.org/10.3390/cancers14010196.
Full textAbstract:
Breast tumors with neuroendocrine (NE) differentiation comprise an uncommon and heterogeneous group of tumors, including invasive breast cancer of no special type (IBC-NST) with NE features, neuroendocrine tumors (NETs), and neuroendocrine carcinoma (NEC). The most recent World Health Organization (WHO) classification in 2019 defined neuroendocrine neoplasms (NENs) of the breast (Br-NENs) as tumors in which >90% of cells show histological evidence of NE differentiation, including NETs (low-grade tumors) and NEC (high-grade). Due to the low prevalence of these tumors and successive changes in their diagnostic criteria over the years, only limited evidence of these tumors exists, derived mainly from case reports and retrospective case series. Breast tumors with NE differentiation are usually treated like the more commonly occurring IBC-NSTs. Immunohistochemistry (IHC) of breast tumors with NE differentiation usually shows a hormone receptor (HR)-positive and human epidermal growth factor type 2 (HER2)-negative profile, so that hormonal therapy with cyclin-dependent kinase (CDK)4/6 inhibitors or other targeted agents would be reasonable treatment options. Herein, we present a review of the literature on breast tumors with NE differentiation as defined in the latest WHO 2019 classification, and discuss the clinical management of these tumors.
20
He, Jianbo, Stephen A. Whelan, Ming Lu, Dejun Shen, Debra U. Chung, Romaine E. Saxton, Kym F. Faull, Julian P. Whitelegge, and Helena R. Chang. "Proteomic-Based Biosignatures in Breast Cancer Classification and Prediction of Therapeutic Response." International Journal of Proteomics 2011 (October 24, 2011): 1–16. http://dx.doi.org/10.1155/2011/896476.
Full textAbstract:
Protein-based markers that classify tumor subtypes and predict therapeutic response would be clinically useful in guiding patient treatment. We investigated the LC-MS/MS-identified protein biosignatures in 39 baseline breast cancer specimens including 28 HER2-positive and 11 triple-negative (TNBC) tumors. Twenty proteins were found to correctly classify all HER2 positive and 7 of the 11 TNBC tumors. Among them, galectin-3-binding protein and ALDH1A1 were found preferentially elevated in TNBC, whereas CK19, transferrin, transketolase, and thymosin 4 and 10 were elevated in HER2-positive cancers. In addition, several proteins such as enolase, vimentin, peroxiredoxin 5, Hsp 70, periostin precursor, RhoA, cathepsin D preproprotein, and annexin 1 were found to be associated with the tumor responses to treatment within each subtype. The MS-based proteomic findings appear promising in guiding tumor classification and predicting response. When sufficiently validated, some of these candidate protein markers could have great potential in improving breast cancer treatment.
21
Jansen, Maurice P. H. M., John A. Foekens, Iris L. van Staveren, Maaike M. Dirkzwager-Kiel, Kirsten Ritstier, Maxime P. Look, Marion E. Meijer-van Gelder, et al. "Molecular Classification of Tamoxifen-Resistant Breast Carcinomas by Gene Expression Profiling." Journal of Clinical Oncology 23, no. 4 (February 1, 2005): 732–40. http://dx.doi.org/10.1200/jco.2005.05.145.
Full textAbstract:
Purpose To discover a set of markers predictive for the type of response to endocrine therapy with the antiestrogen tamoxifen using gene expression profiling. Patients and Methods The study was performed on 112 estrogen receptor–positive primary breast carcinomas from patients with advanced disease and clearly defined types of response (ie, 52 patients with objective response v 60 patients with progressive disease) from start of first-line treatment with tamoxifen. Main clinical end points are the effects of therapy on tumor size and time until tumor progression (progression-free survival [PFS]). RNA isolated from tumor samples was amplified and hybridized to 18,000 human cDNA microarrays. Results Using a training set of 46 breast tumors, 81 genes were found to be differentially expressed (P ≤ .05) between tamoxifen-responsive and -resistant tumors. These genes were involved in estrogen action, apoptosis, extracellular matrix formation, and immune response. From the 81 genes, a predictive signature of 44 genes was extracted and validated on an independent set of 66 tumors. This 44-gene signature is significantly superior (odds ratio, 3.16; 95% CI, 1.10 to 9.11; P = .03) to traditional predictive factors in univariate analysis and also significantly related with a longer PFS in univariate (hazard ratio, 0.54; 95% CI, 0.31 to 0.94; P = .03) as well as in multivariate analyses (P = .03). Conclusion Our data show that gene expression profiling can be used to discriminate between breast cancer patients with progressive disease and objective response to tamoxifen. Additional studies are needed to confirm if the predictive signature might allow identification of individual patients who could benefit from other (adjuvant) endocrine therapies.
22
López-Ruiz, José A., Jon A. Mieza, Ignacio Zabalza, and María d. M. Vivanco. "Comparison of Genomic Profiling Data with Clinical Parameters: Implications for Breast Cancer Prognosis." Cancers 14, no. 17 (August 30, 2022): 4197. http://dx.doi.org/10.3390/cancers14174197.
Full textAbstract:
Precise prognosis is crucial for selection of adjuvant therapy in breast cancer. Molecular subtyping is increasingly used to complement immunohistochemical and pathological classification and to predict recurrence. This study compares both outcomes in a clinical setting. Molecular subtyping (MammaPrint®, TargetPrint®, and BluePrint®) and pathological classification data were compared in a cohort of 143 breast cancer patients. High risk clinical factors were defined by a value of the proliferation factor Ki67 equal or higher than 14% and/or high histological grade. The results from molecular classification were considered as reference. Core needle biopsies were found to be comparable to surgery samples for molecular classification. Discrepancies were found between molecular and pathological subtyping of the samples, including misclassification of HER2-positive tumors and the identification of a significant percentage of genomic high risk T1N0 tumors. In addition, 20% of clinical low-risk tumors showed genomic high risk, while clinical high-risk samples included 42% of cases with genomic low risk. According to pathological subtyping, a considerable number of breast cancer patients would not receive the appropriate systemic therapy. Our findings support the need to determine the molecular subtype of invasive breast tumors to improve breast cancer management.
23
Mohammed, Sahar Haj Ali A., and Zeinab Adam Mustafa. "Breast Tumors Classification Using Adaptive Neuro-Fuzzy Inference System." Journal of Clinical Engineering 42, no. 2 (2017): 68–72. http://dx.doi.org/10.1097/jce.0000000000000205.
Full text
24
Sawaki, Masataka, Tadahiko Shien, and Hiroji Iwata. "TNM classification of malignant tumors (Breast Cancer Study Group)." Japanese Journal of Clinical Oncology 49, no. 3 (December 12, 2018): 228–31. http://dx.doi.org/10.1093/jjco/hyy182.
Full text
25
Balasundaram, Shanmugham, Revathi Balasundaram, Ganesan Rasuthevar, Christeena Joseph, Annie Grace Vimala, Nanmaran Rajendiran, and Baskaran Kaliyamurthy. "Automated Detection and Classification of Breast Cancer Nuclei with Deep Convolutional Neural Network." Journal of ICT Research and Applications 15, no. 2 (October 7, 2021): 139–51. http://dx.doi.org/10.5614/itbj.ict.res.appl.2021.15.2.3.
Full textAbstract:
Heterogeneous regions present in tissue with respect to cancer cells are of various types. This study aimed to analyze and classify the morphological features of the nucleus and cytoplasm regions of tumor cells. This tissue morphology study was established through invasive ductal breast cancer histopathology images accessed from the Databiox public dataset. Automatic detection and classification was carried out by means of the computer analytical tool of deep learning algorithm. Residual blocks with short skip were employed with hidden layers of preserved spatial information. A ResNet-based convolutional neural network was adapted to perform end-to-end segmentation of breast cancer nuclei. Nuclei regions were identified through color and tubular structure morphological features. Based on the segmented and extracted images, classification of benign and malignant breast cancer cells was done to identify tumors. The results indicated that the proposed method could successfully segment and classify breast tumors with an average Dice score of 90.68%, sensitivity = 98.64, specificity = 98.68, and accuracy = 98.82.
26
Mikhailov, M. K., E. A. Romanycheva, V. V. Sevastyanov, and Ya A. Furman. "PERSPECTIVE METHODS FOR CONTOUR ANALYSIS OF RADIOGRAPHIC IMAGES OF MALIGNANTBREAST TUMORS." Diagnostic radiology and radiotherapy, no. 2 (July 18, 2018): 40–45. http://dx.doi.org/10.22328/2079-5343-2018-9-2-40-45.
Full textAbstract:
X-ray mammography is considered one of the primary diagnostic methods for malignant breast tumors. Since the conclusion is mainly based on the visual analysis of analog or digital X-ray images of the breast, the objectivity of the method is highly dependent on the professional experience of the radiologist. Therefore, the automation of the process of analyzing X-ray mammograms is a relevant task. The present study aims to develop a method for the automatic classification of the types of tumors on x-ray mammograms. To this end, the boundaries of breast tissue densities were described analytically using the mathematical apparatus of contour analysis. It has been found that malignant tumors are characterized by rough contours, which enables the determination of the tumor type by calculating the straightness coefficient of its contour. The straightness coefficient values for malignant and benign tumors have been found. Based on a representative sample from the patients with a previously known diagnosis, consistent classification results have been obtained which is an indication of the feasibility of the proposed method.
27
Canadas, Ana, Miguel França, Cristina Pereira, Raquel Vilaça, Hugo Vilhena, Flora Tinoco, Maria João Silva, et al. "Canine Mammary Tumors: Comparison of Classification and Grading Methods in a Survival Study." Veterinary Pathology 56, no. 2 (October 31, 2018): 208–19. http://dx.doi.org/10.1177/0300985818806968.
Full textAbstract:
Histopathology remains the cornerstone for diagnosing canine mammary tumors (CMTs). Recently, 2 classification systems (the World Health Organization [WHO] classification of 1999 and the proposal of 2011) and 2 grading methods based on the human Nottingham grade have been used by pathologists. Despite some evidence that the histological subtype and grade are prognostic factors, there is no comprehensive comparative study of these classification and grading systems in the same series of CMTs. In this study, the 2 classifications and the 2 grading methods were simultaneously applied to a cohort of 134 female dogs with CMTs. In 85 animals with malignant tumors, univariable and multivariable survival analyses were performed. Using the 2 systems, the proportion of benign (161/305, 53%) and malignant (144/305, 47%) tumors was similar and no significant differences existed in categorization of benign tumors. However, the 2011 classification subdivided malignant tumors in more categories—namely, those classified as complex, solid, and tubulopapillary carcinomas by the WHO system. Histological subtype according to both systems was significantly associated with survival. Carcinomas arising in benign tumors, complex carcinomas, and mixed carcinomas were associated with a better prognosis. In contrast, carcinosarcomas and comedocarcinomas had a high risk of tumor-related death. Slight differences existed between the 2 grading methods, and grade was related to survival only in univariable analysis. In this cohort, age, completeness of surgical margins, and 2 index formulas adapted from human breast cancer studies (including tumor size, grade, and vascular/lymph node invasion) were independent prognostic factors.
28
Shareef, Bryar, Aleksandar Vakanski, Phoebe E. Freer, and Min Xian. "ESTAN: Enhanced Small Tumor-Aware Network for Breast Ultrasound Image Segmentation." Healthcare 10, no. 11 (November 11, 2022): 2262. http://dx.doi.org/10.3390/healthcare10112262.
Full textAbstract:
Breast tumor segmentation is a critical task in computer-aided diagnosis (CAD) systems for breast cancer detection because accurate tumor size, shape, and location are important for further tumor quantification and classification. However, segmenting small tumors in ultrasound images is challenging due to the speckle noise, varying tumor shapes and sizes among patients, and the existence of tumor-like image regions. Recently, deep learning-based approaches have achieved great success in biomedical image analysis, but current state-of-the-art approaches achieve poor performance for segmenting small breast tumors. In this paper, we propose a novel deep neural network architecture, namely the Enhanced Small Tumor-Aware Network (ESTAN), to accurately and robustly segment breast tumors. The Enhanced Small Tumor-Aware Network introduces two encoders to extract and fuse image context information at different scales, and utilizes row-column-wise kernels to adapt to the breast anatomy. We compare ESTAN and nine state-of-the-art approaches using seven quantitative metrics on three public breast ultrasound datasets, i.e., BUSIS, Dataset B, and BUSI. The results demonstrate that the proposed approach achieves the best overall performance and outperforms all other approaches on small tumor segmentation. Specifically, the Dice similarity coefficient (DSC) of ESTAN on the three datasets is 0.92, 0.82, and 0.78, respectively; and the DSC of ESTAN on the three datasets of small tumors is 0.89, 0.80, and 0.81, respectively.
29
Shafatujjahan, Ifatujjahan, and Rajat Sanker Roy Biswas. "Molecular Subtypes of Breast Cancer Patients According to St Gallen Classification." Chattagram Maa-O-Shishu Hospital Medical College Journal 19, no. 1 (August 28, 2020): 55–58. http://dx.doi.org/10.3329/cmoshmcj.v19i1.48805.
Full textAbstract:
Introduction: Breast cancer is a common malignancy among female in Bangladesh.But its molecular subtypes are not evaluated due to lack of expert investigationsupport. So objectives of the present study are to evaluate the molecular subtypesof breast cancer patients according to St Gallen classification in our contest.
Materials and methods: It is retrospective study done among histopathologicallyproved 40 breast cancer patients visiting Medical Oncology and Radiotherapydepartment of Chattogram Maa-O-Shishu Hospital. Molecular subtypes wasevaluated by immunohistochemistry according to St Gallen Classification.
Results: In this study a total of 40 cases of invasive female breast cancers wereincluded. Age of the patients ranged from 31-62 years, with a mean age of 41 ±13.5 years. ER expression was seen in 60% and PR in 55% of cases and Her-2/neupositivity in 16%. Majority (52.5%) of the tumors were located in the left breast. Thepercentage of ER but not PR positivity increased with age, though this differencewas not statistically significant. Majority of the cases were diagnosed at stage IIwith a percentage of 42.5%. Stage II tumors showed more ER and PR positivity.Among all 57.9% of ER positive and 49.5% of PR positive tumors were present while72.2% of tumors were negative for Her-2/neu. The triple-negative breast tumorswere more commonly found at grade 2. Regarding luminal status 14(35%) wasLuminal A, 5(12.5%) was Luminal B, 9(22.5%) was TNBC and 12(30%) was HER 2positive.
Conclusion: In this study luminal A was the commonest molecular subtypes. LuminalA subtypes tumors had a long term risk of distant matastatic disease which can bereduced by hormonal treatment.
Chatt Maa Shi Hosp Med Coll J; Vol.19 (1); January 2020; Page 55-58
30
Giskeødegård, Guro Fanneløb, Marina Perea Badia, Anna Bofin, Torfinn Støve Madssen, Steinar Lundgren, Hans Fjøsne, and Tone Frost Bathen. "Metabolic intratumor heterogeneity of breast cancer." Journal of Clinical Oncology 35, no. 15_suppl (May 20, 2017): e23095-e23095. http://dx.doi.org/10.1200/jco.2017.35.15_suppl.e23095.
Full textAbstract:
e23095 Background: Breast tumors are highly heterogeneous due to subpopulations of cancer cells that differ in genetic and phenotypic characteristics. Tumor heterogeneity has been associated with treatment resistance and relapse. Therefore, it can be questioned how representative one biopsy is for the whole tumor. Tumor phenotypic heterogeneity cannot be solely attributed to genetic differences, as epigenetics and interaction with the tumor microenvironment also contribute. In this study we have examined intra-tumor heterogeneity by measuring metabolite expression in breast cancer tissue compared with fibroadenomas. Methods: Fresh frozen tissue slices from surgically removed breast tumors were used. Five cores from different areas of the slices were drilled out from 10 tumors; 6 invasive ductal carcinomas grade 2-3, and 4 fibroadenomas. Histological examination of HES-stained sections from each core was done, and metabolic profiling was performed by magnetic resonance spectroscopy (MRS). The relative concentrations of 23 metabolites were quantified. Metabolic heterogeneity was assessed by coefficient of variation (CoV) and PLSDA classification was used for prediction of tumor origin. Results: Cancer tissue showed significantly higher heterogeneity than fibroadenomas for 16/23 metabolites (mean CoV range: 0.15-0.94 for cancer samples, 0.09-0.37 for fibroadenomas, p < 0.05). However, 23/50 samples did not contain tumor tissue on histological examination. After exclusion of tumor-free samples, the heterogeneity of 3 metabolites (glycine, glycerophosphocholine (GPC) and phosphocholine (PCho) remained significantly different between cancer and fibroadenomas (mean CoV range: 0.12-0.65 for cancer, 0.07-0.42 for fibroadenomas, p < 0.05). GPC and PCho are involved in building of cell membranes and may reflect cell-turnover. Multivariate classification could correctly predict which patient a sample belonged to with 78% accuracy. Conclusions: Metabolic heterogeneity could partly be explained by differences in tumor cell and stromal content, and the origin of an unknown sample could be successfully predicted, showing that metabolic intratumor heterogeneity is smaller than the heterogeneity between patients.
31
Ross, Douglas T., and Charles M. Perou. "A Comparison of Gene Expression Signatures from Breast Tumors and Breast Tissue Derived Cell Lines." Disease Markers 17, no. 2 (2001): 99–109. http://dx.doi.org/10.1155/2001/850531.
Full textAbstract:
Cell lines derived from human tumors have historically served as the primary experimental model system for exploration of tumor cell biology and pharmacology. Cell line studies, however, must be interpreted in the context of artifacts introduced by selection and establishment of cell linesin vitro. This complication has led to difficulty in the extrapolation of biology observed in cell lines to tumor biologyin vivo. Modern genomic analysis tool like DNA microarrays and gene expression profiling now provide a platform for the systematic characterization and classification of both cell lines and tumor samples. Studies using clinical samples have begun to identify classes of tumors that appear both biologically and clinically unique as inferred from their distinctive patterns of expressed genes. In this review, we explore the relationships between patterns of gene expression in breast tumor derived cell lines to those from clinical tumor specimens. This analysis demonstrates that cell lines and tumor samples have distinctive gene expression patterns in common and underscores the need for careful assessment of the appropriateness of any given cell line as a model for a given tumor subtype.
32
Awad, Barbara, Agni Chandora, Ben Bassett, Brittany Hermecz, and Stefanie Woodard. "Classifying Breast Cancer Metastasis Based on Imaging of Tumor Primary and Tumor Biology." Diagnostics 13, no. 3 (January 25, 2023): 437. http://dx.doi.org/10.3390/diagnostics13030437.
Full textAbstract:
The molecular classification of breast cancer has allowed for a better understanding of both prognosis and treatment of breast cancer. Imaging of the different molecular subtypes has revealed that biologically different tumors often exhibit typical features in mammography, ultrasound, and MRI. Here, we introduce the molecular classification of breast cancer and review the typical imaging features of each subtype, examining the predictive value of imaging with respect to distant metastases.
33
Keatmanee, Chadaporn, Saowapak S. Thongvigitmanee, Utairat Chaumrattanakul, and Stanislav S. Makhanov. "A Breast Cancer Contour Detection With Level Sets and SVM Model." International Journal of Knowledge and Systems Science 13, no. 1 (January 1, 2022): 1–14. http://dx.doi.org/10.4018/ijkss.305477.
Full textAbstract:
Level sets have been widely used to isolate features of breast tumors in ultrasound images. However, region-based methods always produce multiple contours. Since tumors are regularly undefined from the shadows and muscular regions in breast ultrasound images, computerized tumors location and arrangement is significantly difficult. Therefore, the authors introduce a breast cancer contour detection model using support vector machine (SVM) as a binary classification. Features of the binary SVM model were extracted from level sets and FM method (the fusion of ultrasound, elasticity, and Doppler images). The model was accurately able to predict a correct breast tumor contour from false contours which were segmented by region-based level sets. The proposed method was evaluated on 60 datasets collected by professional radiologists at the Thammasat University Hospital of Thailand. From the experimental results, the breast cancer contours were detected correctly with high accuracy. The percentage of correct detection was 93%.
34
Gomathi, S., K. Malarvizhi, and M. S. Kavitha. "Detection of Mammogram Using Improved Watershed Segmentation Algorithm and Classifying with Feed Forward Neural Network (FNN)." Journal of Medical Imaging and Health Informatics 12, no. 3 (March 1, 2022): 212–20. http://dx.doi.org/10.1166/jmihi.2022.3939.
Full textAbstract:
Segmentation of breast tumors with more accuracy using computerized methods is essential for breast cancer monitoring and quantification. Both segmentation and classification of breast tumors using a fully automated or Computer-Aided Diagnosis system poses various problems in terms of imaging properties. In this work, a new hybrid algorithm is proposed for segmentation with a two-step process. Initially, a watershed transformation is applied to separate all basins based on pixel density variation from the mass present in tumors, since it has been quite booming in the presence of tumors in all circumstances. Though this is very perceptive to tiny fluctuations in the size of the image, large numbers of areas are produced unacceptably, and the boundaries after segmentations are also quite hard. The second level set is an effective method of segmenting all types of medical images because; it easily flows with, cavities, folds, splits, and merges. To make the recognition step easier and more accurate, the result of segmentation is considered the beginning position of the curve, and the same will be used at the next step of the level set. This produces a closed, smooth, and accurately placed contour or surface. As a result, the present research uses watershed segmentation to isolate tumor regions and performs classification using Feed Forward Neural Network (FNN) to extract features for classification. Experimental results are evaluated based on performance and quality analysis. In the classification process, the study obtained an accuracy rate of 91.2% in the learning model and 71.8% in a testing model.
35
Yang, Xiaofang, Dina Kandil, Ediz F. Cosar, and Ashraf Khan. "Fibroepithelial Tumors of the Breast: Pathologic and Immunohistochemical Features and Molecular Mechanisms." Archives of Pathology & Laboratory Medicine 138, no. 1 (January 1, 2014): 25–36. http://dx.doi.org/10.5858/arpa.2012-0443-ra.
Full textAbstract:
Context.—The 2 main prototypes of fibroepithelial tumors of the breast include fibroadenoma and phyllodes tumor (PT). Although both tumors share some overlapping histologic features, there are significant differences in their clinical behavior and management. Phyllodes tumors have been further divided into clinically relevant subtypes, and there is more than one classification scheme for PT currently in use, suggesting a lack of consistency within different practices. Accurate differentiation between fibroadenoma and PT, as well as the grading of PT, may sometimes be challenging on preoperative core needle biopsy. Some immunohistochemical markers have been suggested to aid in the pathologic classification of these lesions. Objective.—To discuss the salient histopathologic features of fibroepithelial tumors and review the molecular pathways proposed for the initiation, progression, and metastasis of PTs. Also, to provide an update on immunohistochemical markers that may be useful in their differential diagnosis and outline the practice and experience at our institution from a pathologic perspective. Data Sources.—Sources included published articles from peer-reviewed journals in PubMed (US National Library of Medicine). Conclusions.—Fibroepithelial tumor of the breast is a heterogenous group of lesions ranging from fibroadenoma at the benign end of the spectrum to malignant PT. There are overlapping histologic features among various subtypes, and transformation and progression to a more malignant phenotype may also occur. Given the significant clinical differences within various subtypes, accurate pathologic classification is important for appropriate management. Although some immunohistochemical markers may be useful in this differential diagnosis, histomorphology still remains the gold standard.
36
Su, Yanni, Yuanyuan Wang, Jing Jiao, and Yi Guo. "Automatic Detection and Classification of Breast Tumors in Ultrasonic Images Using Texture and Morphological Features." Open Medical Informatics Journal 5, no. 1 (July 27, 2011): 26–37. http://dx.doi.org/10.2174/1874431101105010026.
Full textAbstract:
Due to severe presence of speckle noise, poor image contrast and irregular lesion shape, it is challenging to build a fully automatic detection and classification system for breast ultrasonic images. In this paper, a novel and effective computer-aided method including generation of a region of interest (ROI), segmentation and classification of breast tumor is proposed without any manual intervention. By incorporating local features of texture and position, a ROI is firstly detected using a self-organizing map neural network. Then a modified Normalized Cut approach considering the weighted neighborhood gray values is proposed to partition the ROI into clusters and get the initial boundary. In addition, a regional-fitting active contour model is used to adjust the few inaccurate initial boundaries for the final segmentation. Finally, three textures and five morphologic features are extracted from each breast tumor; whereby a highly efficient Affinity Propagation clustering is used to fulfill the malignancy and benign classification for an existing database without any training process. The proposed system is validated by 132 cases (67 benignancies and 65 malignancies) with its performance compared to traditional methods such as level set segmentation, artificial neural network classifiers, and so forth. Experiment results show that the proposed system, which needs no training procedure or manual interference, performs best in detection and classification of ultrasonic breast tumors, while having the lowest computation complexity.
37
Chowdary, Jignesh, Pratheepan Yogarajah, Priyanka Chaurasia, and Velmathi Guruviah. "A Multi-Task Learning Framework for Automated Segmentation and Classification of Breast Tumors From Ultrasound Images." Ultrasonic Imaging 44, no. 1 (January 2022): 3–12. http://dx.doi.org/10.1177/01617346221075769.
Full textAbstract:
Breast cancer is one of the most fatal diseases leading to the death of several women across the world. But early diagnosis of breast cancer can help to reduce the mortality rate. So an efficient multi-task learning approach is proposed in this work for the automatic segmentation and classification of breast tumors from ultrasound images. The proposed learning approach consists of an encoder, decoder, and bridge blocks for segmentation and a dense branch for the classification of tumors. For efficient classification, multi-scale features from different levels of the network are used. Experimental results show that the proposed approach is able to enhance the accuracy and recall of segmentation by [Formula: see text], [Formula: see text], and classification by [Formula: see text], [Formula: see text], respectively than the methods available in the literature.
38
Wang, Lulu. "Holographic Microwave Image Classification Using a Convolutional Neural Network." Micromachines 13, no. 12 (November 23, 2022): 2049. http://dx.doi.org/10.3390/mi13122049.
Full textAbstract:
Holographic microwave imaging (HMI) has been proposed for early breast cancer diagnosis. Automatically classifying benign and malignant tumors in microwave images is challenging. Convolutional neural networks (CNN) have demonstrated excellent image classification and tumor detection performance. This study investigates the feasibility of using the CNN architecture to identify and classify HMI images. A modified AlexNet with transfer learning was investigated to automatically identify, classify, and quantify four and five different HMI breast images. Various pre-trained networks, including ResNet18, GoogLeNet, ResNet101, VGG19, ResNet50, DenseNet201, SqueezeNet, Inception v3, AlexNet, and Inception-ResNet-v2, were investigated to evaluate the proposed network. The proposed network achieved high classification accuracy using small training datasets (966 images) and fast training times.
39
Ahn, Sung Gwe, Seung Joon Kim, Hak Woo Lee, Hyo Jeong Yoon, Chungyeul Kim, and Joon Jeong. "Molecular classification with NanoString nCounter system in triple-negative breast cancer." Journal of Clinical Oncology 35, no. 15_suppl (May 20, 2017): e23198-e23198. http://dx.doi.org/10.1200/jco.2017.35.15_suppl.e23198.
Full textAbstract:
e23198 Background: Previous studies have shown that several distinct subtypes identified by gene expression profiling (GEP) consisted of triple-negative breast cancer (TNBC). Compared with the subtypes defined by GEP, we developed molecular classification with NanoString nCounter system in TNBC. Methods: GEP was conducted on 188 FFPE containing chemotherapy-naïve TNBC tumors collected at Gangnam Severance Hospital. To select core genes for classification, other 120 samples from public GEP database were used. Correlation between nCounter system and GEP using identical RNA was done. In a part of tumors, BRCA1 methylation, homologous recombination deficiency (HRD) assay, and drug response assay with ATP was comprehensively assessed. Results: To classify TNBC into 4 major subtypes (Basal-like: BL, Luminal androgen receptor: LAR, Mesenchymal: M, and Immune-modulatory: IM) according to the Vanderbilt classification, we selected 110 genes in 220 samples with GEP (100 from Gangnam Severance Hospital and 120 from public database). In other 88 samples, the classification with the 110 genes were validated. In 149 tumors excluding UNS subtype by the Vanderbilt, a correlation between 110 genes-classification and the Vanderbilt system was 74.4% (111 of 149, Pearson’s R = 0.726). In 180 tumors with GEP and nCounter assay, a correlation between them was 85.0% (153 of 180, Pearson’s R = 0.827). Compared with tumors of the IM type by 110-genes, the recurrence-free survival was significantly reduced in tumors of the non-IM type by 110-genes ( P= 0.049). In cases with BRCA1 methylation test (n = 147), a significant higher rate of BRCA1 promoter methylation was found in BL type by nCounter system (BL: 41.0% vs. non-BL: 19.4%). In cases with HRD test, a significant lower rate of HRD was found in nCounter-identified LAR type (LAR: 0% vs. non-LAR: 79.3%). In patients with in vitro drug response assay with cisplatin (n = 36), tumors with nCounter-BL had a significant higher responsiveness than tumors with others ( P= 0.028). Conclusions: Our work shows the feasibility of molecular classification with nCounter system in TNBC. Future study warrants the clinical utility of this classification to guide the subtype-specific therapy in patients with TNBC.
40
Rangayyan, R. M., N. M. El-Faramawy, J. E. L. Desautels, and O. A. Alim. "Measures of acutance and shape for classification of breast tumors." IEEE Transactions on Medical Imaging 16, no. 6 (1997): 799–810. http://dx.doi.org/10.1109/42.650876.
Full text
41
Iwao, K. "Molecular classification of primary breast tumors possessing distinct prognostic properties." Human Molecular Genetics 11, no. 2 (January 1, 2002): 199–206. http://dx.doi.org/10.1093/hmg/11.2.199.
Full text
42
Appati, Justice Kwame, Franklin Iron Badzi, Michael Agbo Tettey Soli, Stephane Jnr Nwolley, and Ismail Wafaa Denwar. "Validation of Performance Homogeneity of Chan-Vese Model on Selected Tumour Cells." International Journal of E-Health and Medical Communications 12, no. 6 (November 2021): 1–17. http://dx.doi.org/10.4018/ijehmc.20211101.oa7.
Full textAbstract:
This study aims to analyze the Chan-Vese model's performance using a variety of tumor images. The processes involve the tumors' segmentation, detecting the tumors, identifying the segmented tumor region, and extracting the features before classification occurs. In the findings, the Chan-Vese model performed well with brain and breast tumor segmentation. The model on the skin performed poorly. The brain recorded DSC 0.6949903, Jaccard 0.532558; the time elapsed 7.389940 with an iteration of 100. The breast recorded a DSC of 0.554107, Jaccard 0.383228; the time elapsed 9.577161 with an iteration of 100. According to this study, a higher DSC does not signify a well-segmented image, as the breast had a lower DSC than the skin. The skin recorded a DSC of 0.620420, Jaccard 0.449717; the time elapsed 17.566681 with an iteration of 200.
43
van Beers, Erik H., Tibor van Welsem, Lodewyk F. A. Wessels, Yunlei Li, Rogier A. Oldenburg, Peter Devilee, Cees J. Cornelisse, et al. "Comparative Genomic Hybridization Profiles in Human BRCA1 and BRCA2 Breast Tumors Highlight Differential Sets of Genomic Aberrations." Cancer Research 65, no. 3 (February 1, 2005): 822–27. http://dx.doi.org/10.1158/0008-5472.822.65.3.
Full textAbstract:
Abstract BRCA1 or BRCA2 germline mutations cause ∼30% of breast cancers within high-risk families. This represents 5% of total breast cancer incidence. Although BRCA1 and BRCA2 are both implicated in DNA repair and genome stability, it is unknown whether BRCA1 and BRCA2 are associated with similar or distinct diseases. In a previous study we reported that BRCA1-related breast carcinomas show a distinct genomic profile as determined by comparative genomic hybridization (CGH). We now hypothesize that, if functionally equivalent, mutations in BRCA1 and BRCA2 would result in similar genomic profiles in tumors. Here we report the chromosomal gains and losses as measured by CGH in 25 BRCA2-associated breast tumors and compared them with our existing 36 BRCA1 and 30 control profiles. We compared all chromosomal regions and determined the regions of differential gain or loss between tumor classes and controls. BRCA2 and control tumors have very similar genomic profiles. As a consequence, and in contrast to BRCA1-associated tumors, CGH profiles from BRCA2-associated tumors could not be distinguished from control tumors using the classification methodology as we have developed before. The largest number of significant differences existed between BRCA1 and controls, followed by BRCA1 compared with BRCA2, suggesting different tumor development pathways for BRCA1 and BRCA2.
44
Zhao, Tianyu, and Hang Dai. "Tumor Region Location and Classification Based on Fuzzy Logic and Region Merging Image Segmentation Algorithm." Journal of Healthcare Engineering 2021 (October 20, 2021): 1–6. http://dx.doi.org/10.1155/2021/1141619.
Full textAbstract:
Early diagnosis of tumor plays an important role in the improvement of treatment and survival rate of patients. However, breast tumors are difficult to be diagnosed by invasive examination, so medical imaging has become the most intuitive auxiliary method for breast tumor diagnosis. Although there is no universal perfect method for image segmentation so far, the consensus on the general law of image segmentation has produced considerable research results and methods. In this context, this paper focuses on the breast tumor image segmentation method based on CNN and proposes an improved DCNN method combined with CRF. This method can obtain the information of multiscale and pixels better. The experimental results show that, compared with DCNN without these methods, the segmentation accuracy is significantly improved.
45
Tsang, Patricia C., and Guoli Chen. "What’s new in molecular genetic pathology 2022: immune checkpoint inhibitor biomarkers and select solid tumors." Journal of Pathology and Translational Medicine 56, no. 2 (March 15, 2022): 113–14. http://dx.doi.org/10.4132/jptm.2022.01.25.
Full textAbstract:
Predictive biomarker testing plays a critical role in targeted immuno-oncology, including the use of immune checkpoint inhibitors (ICI) for various solid tumors. Molecular advancements in cancers of the breast, kidney and brain have continued to propel tumor classification and precision therapy.
46
Smolarz, Beata, Anna Zadrożna Nowak, and Hanna Romanowicz. "Breast Cancer—Epidemiology, Classification, Pathogenesis and Treatment (Review of Literature)." Cancers 14, no. 10 (May 23, 2022): 2569. http://dx.doi.org/10.3390/cancers14102569.
Full textAbstract:
Breast cancer is the most-commonly diagnosed malignant tumor in women in the world, as well as the first cause of death from malignant tumors. The incidence of breast cancer is constantly increasing in all regions of the world. For this reason, despite the progress in its detection and treatment, which translates into improved mortality rates, it seems necessary to look for new therapeutic methods, and predictive and prognostic factors. Treatment strategies vary depending on the molecular subtype. Breast cancer treatment is multidisciplinary; it includes approaches to locoregional therapy (surgery and radiation therapy) and systemic therapy. Systemic therapies include hormone therapy for hormone-positive disease, chemotherapy, anti-HER2 therapy for HER2-positive disease, and quite recently, immunotherapy. Triple negative breast cancer is responsible for more than 15–20% of all breast cancers. It is of particular research interest as it presents a therapeutic challenge, mainly due to its low response to treatment and its highly invasive nature. Future therapeutic concepts for breast cancer aim to individualize therapy and de-escalate and escalate treatment based on cancer biology and early response to therapy. The article presents a review of the literature on breast carcinoma—a disease affecting women in the world.
47
Liu, Wei, Minghui Guo, Peizhong Liu, and Yongzhao Du. "MfdcModel: A Novel Classification Model for Classification of Benign and Malignant Breast Tumors in Ultrasound Images." Electronics 11, no. 16 (August 18, 2022): 2583. http://dx.doi.org/10.3390/electronics11162583.
Full textAbstract:
Automatic classification of benign and malignant breast ultrasound images is an important and challenging task to improve the efficiency and accuracy of clinical diagnosis of breast tumors and reduce the rate of missed and misdiagnosis. The task often requires a large amount of data to train. However, it is difficult to obtain medical images, which contradicts the large amount of data needed to obtain good diagnostic models for training. In this paper, a novel classification model for the classification of breast tumors is proposed to improve the performance of diagnosis models trained by small datasets. The method integrates three features from medical features extracted from segmented images, features selected from the pre-trained ResNet101 output by principal component analysis (PCA), and texture features. Among the medical features that are used to train the naive Bayes (NB) classifier, and the PCA-selected features are used to train the support vector machine (SVM) classifier. Subsequently, the final results of boosting are obtained by weighting the classifiers. A five-fold cross-validation experiment yields an average accuracy of 89.17%, an average precision of 90.00%, and an average AUC value of 0.95. According to the experimental results, the proposed method has better classification accuracy compared to the accuracy obtained by other models trained on only small datasets. This approach can serve as a reliable second opinion for radiologists, and it can also provide useful advice for junior radiologists who do not have sufficient clinical experience.
48
Daoud, Mohammad I., Samir Abdel-Rahman, Tariq M. Bdair, Mahasen S. Al-Najar, Feras H. Al-Hawari, and Rami Alazrai. "Breast Tumor Classification in Ultrasound Images Using Combined Deep and Handcrafted Features." Sensors 20, no. 23 (November 30, 2020): 6838. http://dx.doi.org/10.3390/s20236838.
Full textAbstract:
This study aims to enable effective breast ultrasound image classification by combining deep features with conventional handcrafted features to classify the tumors. In particular, the deep features are extracted from a pre-trained convolutional neural network model, namely the VGG19 model, at six different extraction levels. The deep features extracted at each level are analyzed using a features selection algorithm to identify the deep feature combination that achieves the highest classification performance. Furthermore, the extracted deep features are combined with handcrafted texture and morphological features and processed using features selection to investigate the possibility of improving the classification performance. The cross-validation analysis, which is performed using 380 breast ultrasound images, shows that the best combination of deep features is obtained using a feature set, denoted by CONV features that include convolution features extracted from all convolution blocks of the VGG19 model. In particular, the CONV features achieved mean accuracy, sensitivity, and specificity values of 94.2%, 93.3%, and 94.9%, respectively. The analysis also shows that the performance of the CONV features degrades substantially when the features selection algorithm is not applied. The classification performance of the CONV features is improved by combining these features with handcrafted morphological features to achieve mean accuracy, sensitivity, and specificity values of 96.1%, 95.7%, and 96.3%, respectively. Furthermore, the cross-validation analysis demonstrates that the CONV features and the combined CONV and morphological features outperform the handcrafted texture and morphological features as well as the fine-tuned VGG19 model. The generalization performance of the CONV features and the combined CONV and morphological features is demonstrated by performing the training using the 380 breast ultrasound images and the testing using another dataset that includes 163 images. The results suggest that the combined CONV and morphological features can achieve effective breast ultrasound image classifications that increase the capability of detecting malignant tumors and reduce the potential of misclassifying benign tumors.
49
Zahedi, Farahnaz, and Mohammad Karimi Moridani. "Classification of Breast Cancer Tumors Using Mammography Images Processing Based on Machine Learning." International Journal of Online and Biomedical Engineering (iJOE) 18, no. 05 (April 12, 2022): 31–42. http://dx.doi.org/10.3991/ijoe.v18i05.29197.
Full textAbstract:
Abstract— Using intelligent methods to identify and classify a variety of diseases, in particular cancer, has gained tremendous attention today. Tumor classification plays an important role in medical diagnosis. This study's goal was to classify breast cancer (BC) tumors using software-based numerical techniques. To determine whether breast cancer masses are benign or malignant, we used MATLAB version 2020b to build a neural network. In the first step, the features of the training images and their output classes were used to train the network. Optimal weights were obtained after several repetitions, and the network was trained to produce the best result in the test phase after several repetitions.
Because of using effective and accurate features, the method suggested here, which was based on an artificial neural network, delivered the diagnostic accuracy, specificity, and sensitivity of 100%, 100%, and 100%, respectively, to discern benign from malignant BC tumors, showing a better performance compared to previously proposed methods. One of the challenges for imaging-based diagnostic techniques in medicine is the difficulty of processing dense tissues. Breast cancer is one of the most common progressive diseases among females. Early diagnosis makes treatment easier and more effective. Using AI-based methods for automated diagnosis purposes can be valuable and have a reduced error rate because accurate diagnosis by manual means is time-consuming and error-prone.
50
Dubray, P., X. Durando, C. Abrial, M. Mouret-Reynier, B. Nayl, E. Thivat, P. Gimbergues, J. Achard, P. Chollet, and F. Penault-Llorca. "Preferential pathologic complete response (pCR) in HER-2 positive and triple-negative breast cancer to sequential FEC 100- docetaxel (T) neoadjuvant chemotherapy (NCT) in stage II-III operable breast cancer." Journal of Clinical Oncology 27, no. 15_suppl (May 20, 2009): e11502-e11502. http://dx.doi.org/10.1200/jco.2009.27.15_suppl.e11502.
Full textAbstract:
e11502 Background: NCT is increasingly used for operable breast cancer to allow breast conservation. Our objective was to evaluate clinical and pathological response after sequential NCT of FEC 100-T. This chemotherapy is currently the reference in the adjuvant setting in France.In PACS 01 trial (Roche et al. J Clin Oncol, 2006) FEC followed by T significantly improved 5 years overall survival rates (90.7%) compared to 6 FEC 100 in node-positive breast cancer. However this combination has not yet been evaluated in NCT. Methods: 101 patients (pts) from February 2005 to September 2008 with stage II-III operable breast cancer received 3 cycles (c) of FEC 100 (epirubicin 100 mg/m2 + 5-fluorouracil and cyclophosphamide 500mg/m2) followed by 3 c of T (100mg/m2) every 3 weeks. pCR was defined according to Chevallier's (Am J Clin Oncol, 1993) as level 1 and 2 and to Sataloff's classification (J Am Coll Surg, 1995) as grade A. A clinical, mammography and ultrasound breast evaluation was performed at baseline, after 3 or 4 c and before surgery. Results: Median age was 52.3 years [32–71]. Median diameter of the tumor was 42 mm [15–100]. 83 pts had a ductal, 14 a lobular, 3 ductal and lobular, 1 another carcinoma. 8.9% were grade I SBR, 58.4% grade II SBR, 28.7% grade III SBR and 4% unspecified. 74 (73.3%) tumors were HR+, 9(8.9%) Her-2 + and 18(17.8%) triple negative. Ultrasound objective response rate (ORR) was 66.3%: 6 pts had a complete response and 53 pts had a partial response. 77pts (76.2%) underwent breast-conserving surgery After the completion of NCT, complete histologic data were available for 92 pts. 10 (10.8%) achieved pCR using Chevallier's classification and 9 (9.8%) using Sataloff's classification. The pCR rate was superior in triple negative (4/12) and Her2+ (2/6) tumors than in patients with HR+/Her-2- according to Chevallier's classification (p=0.034) and to Sataloff's classification (p=0.014). Conclusions: Sequential NCT with FEC followed by T was active and significantly improved pCR in patients with triple negative and Her-2+ tumors without an anti-Her2 specific biological agent. Breast-conserving rate appeared satisfactory. No significant financial relationships to disclose.