Journal articles on the topic 'Multiparametric Magnetic Resonance Imaging (mp-MRI)'
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1
Kapur, Savinay, Chandan J. Das, and Sanjay Sharma. "Multiparametric Magnetic Resonance Imaging of the Prostate: An Update." Annals of the National Academy of Medical Sciences (India) 55, no. 02 (April 2019): 074–83. http://dx.doi.org/10.1055/s-0039-1694077.
Abstract:
AbstractMultiparametric magnetic resonance imaging (mp-MRI) has emerged as an important tool for the detection and characterization of prostatic lesions. It now plays a quintessential role in the surveillance, diagnosis, and staging of prostate cancer (PCa), as well as for the detection of local recurrence. As reliance on serum prostate-specific antigen has declined in the recent times, mp-MRI has emerged as the go-to tool for urologists all over the world. Hence, for the clinician, it has become necessary to be well versed with the technique, image interpretation, and fallacies of mp-MRI. Since mp-MRI has the advantage of better contrast resolution, combining PSMA PET (prostate-specific membrane antigen-positron emission tomography) with MRI could provide additional functional information. However, due to the absence of enough evidence supporting its routine use, mp-MRI still has the unsurpassed role in the initial diagnosis and local staging of PCa.
2
Yadav, Kuldeep, Binit Sureka, Poonam Elhence, Gautam Ram Choudhary, and Himanshu Pandey. "Pitfalls in Prostate Cancer Magnetic Resonance Imaging." Indian Journal of Medical and Paediatric Oncology 42, no. 01 (March 2021): 080–88. http://dx.doi.org/10.1055/s-0041-1730757.
Abstract:
AbstractImage-guided prostate biopsies are changing the outlook of prostate cancer (PCa) diagnosis, with the degree of suspicion on multiparametric magnetic resonance imaging (mp-MRI) being a strong predictor of targeted biopsy outcome. It is important not only to detect these suspicious lesions but also to be aware of the potential pitfalls in mp-MRI prostate imaging. The aim of this pictorial essay is to show a wide spectrum of representative cases, which are frequently misdiagnosed as PIRADS ⅘ while reporting mp-MRI of the prostate. We provide some valuable recommendations to avoid these fallacies and improve mp-MRI of prostate evaluation.
3
Sankineni, Sandeep, Murat Osman, and Peter L. Choyke. "Functional MRI in Prostate Cancer Detection." BioMed Research International 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/590638.
Abstract:
Multiparametric magnetic resonance imaging (MP-MRI) has emerged as a promising method for the detection of prostate cancer. The functional MRI components of the MP-MRI consist of the diffusion weighted MRI, dynamic contrast enhanced MRI, and magnetic resonance spectroscopic imaging. The purpose of this paper is to review the existing literature about the use of functional MRI in prostate cancer detection.
4
Popita, Cristian, Anca Raluca Popita, Adela Sitar-Taut, Bogdan Petrut, Bogdan Fetica, and Ioan Coman. "1.5-Tesla Multiparametric-Magnetic Resonance Imaging for the detection of clinically significant prostate cancer." Medicine and Pharmacy Reports 90, no. 1 (January 30, 2017): 40–48. http://dx.doi.org/10.15386/cjmed-690.
Abstract:
Background and aims. Multiparametric-magnetic resonance imaging (mp-MRI) is the main imaging modality used for prostate cancer detection. The aim of this study is to evaluate the diagnostic performance of mp-MRI at 1.5-Tesla (1.5-T) for the detection of clinically significant prostate cancer.Methods. In this ethical board approved prospective study, 39 patients with suspected prostate cancer were included. Patients with a history of positive prostate biopsy and patients treated for prostate cancer were excluded. All patients were examined at 1.5-T MRI, before standard transrectal ultrasonography–guided biopsy.Results. The overall sensitivity, specificity, positive predictive value and negative predictive value for mp-MRI were 100%, 73.68%, 80% and 100%, respectively.Conclusion. Our results showed that 1.5 T mp-MRI has a high sensitivity for detection of clinically significant prostate cancer and high negative predictive value in order to rule out significant disease.
5
Sardari, Al, John V. Thomas, Jeffrey W. Nix, Jason A. Pietryga, Rupan Sanyal, Jennifer B. Gordetsky, and Soroush Rais-Bahrami. "Incidental Bladder Cancer Detected on Multiparametric Magnetic Resonance Imaging of the Prostate Gland." Case Reports in Urology 2015 (2015): 1–4. http://dx.doi.org/10.1155/2015/503154.
Abstract:
The increased use of axial imaging in various fields of medicine has led to an increased frequency of incidental findings, specifically incidental cancer lesions. Hence, as the use of multiparametric magnetic resonance imaging (MP-MRI) for prostate cancer detection, staging, and management becomes more widespread, the potential for additional incidental findings in the pelvis increases. Herein, we report the case of a man on active surveillance for low-grade, early-staged prostate cancer who underwent MP-MRI and was incidentally found to have a high-grade bladder cancer lesion.
6
Ullrich, T., C. Arsov, M. Quentin, F. Mones, A. C. Westphalen, D. Mally, A. Hiester, P. Albers, G. Antoch, and L. Schimmöller. "Multiparametric magnetic resonance imaging can exclude prostate cancer progression in patients on active surveillance: a retrospective cohort study." European Radiology 30, no. 11 (June 26, 2020): 6042–51. http://dx.doi.org/10.1007/s00330-020-06997-1.
Abstract:
Abstract Objectives To assess the ability of multiparametric MRI (mp-MRI) of the prostate to exclude prostate cancer (PCa) progression during monitoring patients on active surveillance (AS). Methods One hundred forty-seven consecutive patients on AS with mp-MRI (T2WI, DWI, DCE-MRI) at 3T were initially enrolled. Fifty-five received follow-up mp-MRI after a minimum interval of 12 months and subsequent targeted MR/US fusion-guided biopsy (FUS-GB) plus concurrent systematic transrectal ultrasound-guided (TRUS-GB) biopsy as reference standard. Primary endpoint was the negative predictive value (NPV) of the follow-up mp-MRI to exclude histopathologic tumor progression using PRECISE recommendations. Secondary endpoints were the positive predictive value (PPV), sensitivity, specificity, Gleason score (GS) upgrades, and comparison of biopsy method. Results Of 55 patients, 29 (53%) had a GS upgrade on re-biopsy. All 29 patients showed a tumor progression on follow-up mp-MRI. Fifteen of 55 patients (27%) displayed signs of tumor progression, but had stable GS on re-biopsy. None of the 11 patients (20%) without signs of progression on follow-up mp-MRI had a GS upgrade on re-biopsy. The NPV was 100%, PPV was 66%, sensitivity was 100%, and specificity 42%. FUS-GB resulted in GS upgrade significantly more often (n = 28; 51%) compared with TRUS-GB (n = 12; 22%; p < 0.001). Conclusions (Follow-up) Mp-MRI can reliably exclude PCa progression in patients on AS. Standard serial re-biopsies might be waived if follow-up mp-MRIs are stable. Over 60% of patients with signs of tumor progression on mp-MRI during AS had a GS upgrade on re-biopsy. Targeted re-biopsies should be performed if cancer progression or higher-grade PCa is suspected on mp-MRI. Key Points • None of the patients with unsuspicious mp-MRI had a GS upgrade in re-biopsy and mp-MRI might replace serial biopsies in these cases • More than 60% of patients with mp-MRI signs of tumor progression had subsequent Gleason score (GS) upgrades • Targeted re-biopsies should be performed in case of higher GS cancer suspicion on mp-MRI
7
Obino, Mariah Kerubo, Edward Ng’ang’a Chege, Sudhir Vinayak, and Samuel Gitau Nguk. "Utility of Multiparametric Magnetic Resonance Imaging as a Predictor of Clinically Significant Prostate Cancer in a Sub-Saharan African Population." Annals of African Surgery 19, no. 2 (May 30, 2022): 108–15. http://dx.doi.org/10.4314/aas.v19i2.8.
Abstract:
Background: Traditionally, the diagnosis of prostate cancer was based on increased prostate-specific antigen level or an abnormal digital rectal examination and confirmed histologically following biopsy. Consequently, a proportion of men without cancer or with clinically insignificant disease undergo unwarranted prostate biopsies and experience resultant complications. Pre-biopsy multiparametric magnetic resonance imaging (MP-MRI) is vital in determining those with clinically significant cancer who need biopsy and those with a negative MRI who can safely avoid unnecessary biopsy.
Methods: The diagnostic accuracy of MP-MRI using transrectal ultrasound-guided biopsy as the reference test was established for 133 men who had undergone MRI and biopsy. The MRI images were reviewed and reported by two independent consultant radiologists. Clinically significant cancer was defined as Prostate Imaging Reporting and Data System score ≥3 on multiparametric MRI and Gleason score ≥3 + 4 (grade group ≥2) on histology.
Results: MP-MRI of the prostate was found to have 92% sensitivity, 47.8% specificity, 86.8% negative predictive value (NPV) and 62% positive predictive value for the diagnosis of prostate cancer.
Conclusion: MP-MRI has a high sensitivity and a high NPV, validating its use in pre-biopsy evaluation of men at risk of prostate cancer to safely avoid unnecessary prostate biopsy and to guide biopsy of suspicious lesions.
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Doykov, Mladen, Lyubomir Chervenkov, Silvia Tsvetkova-Trichkova, Katya Doykova, and Aleksandar Georgiev. "Assessment of the Utility of Multiparametric Magnetic Resonance Imaging for Initial Detection of Prostate Cancer." Open Access Macedonian Journal of Medical Sciences 10, B (July 10, 2022): 1840–45. http://dx.doi.org/10.3889/oamjms.2022.10401.
Abstract:
BACKGROUND: An accurate diagnosis is essential for the effective treatment of prostate cancer (PCa) and for the patients’ well-being. AIM: Thе main purpose of this study was to assess the utility of multiparametric magnetic resonance imaging (mp-MRI) for initial detection of PCa among the Bulgarian population of men with prostate diseases. MATERIALS AND METHODS: Fifty-three patients, aged 44 to 82 years, were evaluated for clinically significant PCa. Assessment methods included prostate-specific antigen (PSA) serum levels, transrectal ultrasonography (TRUS), GE Discovery 3T MRI, and 12-core TRUS biopsy. RESULTS: mp-MRI showed 83.20% concordance with TRUS biopsy: sensitivity of 91.43% (76.90–98.20), specificity of 75.00% (34.90–96.80), positive predictive values 94.10% (82.80–98.20) and negative predictive values 66.70% (38.70–86.40). Of the patients classified in prostate imaging–reporting and data system (PI-RADS) levels 4 and 5, 94.12% had positive TRUS biopsy, as well as 44.40% of PI-RADS had level 3. Irrespective of the patients’ age and PSA, PI-RADS was found to be a significant predictor of a positive TRUS biopsy (p = 0.009). PSA serum levels showed a low concordance with TRUS biopsy (area under the curve = 0.539; 95% confidence interval [CI]: 0.363–0.712) and a low, although significant, correlation with PI-RADS (rs = 0.416; 95% CI: 0.164–0.617). CONCLUSION: According to our findings, mp-MRI and TRUS biopsy have a high level of concordance for the initial detection of PCa. The incorporation of mp-MRI into the diagnostic pathway for PCa can significantly reduce the number of incorrect diagnoses based on PSA serum levels and/or suspicious physical and digital examinations.
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Kowa, Jie-Ying, Neil Soneji, S. Aslam Sohaib, Erik Mayer, Stephen Hazell, Nicholas Butterfield, Joshua Shur, and Derfel ap Dafydd. "Detection and staging of radio-recurrent prostate cancer using multiparametric MRI." British Journal of Radiology 94, no. 1120 (April 1, 2021): 20201423. http://dx.doi.org/10.1259/bjr.20201423.
Abstract:
Objective: We determined the sensitivity and specificity of multiparametric magnetic resonance imaging (MP-MRI) in detection of locally recurrent prostate cancer and extra prostatic extension in the post-radical radiotherapy setting. Histopathological reference standard was whole-mount prostatectomy specimens. We also assessed for any added value of the dynamic contrast enhancement (DCE) sequence in detection and staging of local recurrence. Methods: This was a single centre retrospective study. Participants were selected from a database of males treated with salvage prostatectomy for locally recurrent prostate cancer following radiotherapy. All underwent pre-operative prostate-specific antigen assay, positron emission tomography CT, MP-MRI and transperineal template prostate mapping biopsy prior to salvage prostatectomy. MP-MRI performance was assessed using both Prostate Imaging-Reporting and Data System v. 2 and a modified scoring system for the post-treatment setting. Results: 24 patients were enrolled. Using Prostate Imaging-Reporting and Data System v. 2, sensitivity, specificity, positive predictive value and negative predictive value was 64%, 94%, 98% and 36%. MP-MRI under staged recurrent cancer in 63%. A modified scoring system in which DCE was used as a co-dominant sequence resulted in improved diagnostic sensitivity (61%–76%) following subgroup analysis. Conclusion: Our results show MP-MRI has moderate sensitivity (64%) and high specificity (94%) in detecting radio-recurrent intraprostatic disease, though disease tends to be under quantified and under staged. Greater emphasis on dynamic contrast images in overall scoring can improve diagnostic sensitivity. Advances in knowledge: MP-MRI tends to under quantify and under stage radio-recurrent prostate cancer. DCE has a potentially augmented role in detecting recurrent tumour compared with the de novo setting. This has relevance in the event of any future modified MP-MRI scoring system for the irradiated gland.
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Faccioli, Niccolò, Elena Santi, Giovanni Foti, Pierpaolo Curti, and Mirko D'Onofrio. "Cost-effectiveness analysis of short biparametric magnetic resonance imaging protocol in men at risk of prostate cancer." Archivio Italiano di Urologia e Andrologia 94, no. 2 (June 29, 2022): 160–65. http://dx.doi.org/10.4081/aiua.2022.2.160.
Abstract:
Objectives: To compare the cost-effectiveness of a short biparametric MRI (BP-MRI) with that of contrast-enhanced multiparametric MRI (MP-MRI) for the detection of prostate cancer in men with elevated prostatespecific antigen (PSA) levels. Materials and methods: We compared two diagnostic procedures for detection of prostate cancer (Pca), BP-MRI and MP-MRI, in terms of quality-adjusted life years (QALY), incremental costeffectiveness ratio (ICER) and net monetary benefit (NMB) for a hypothetical cohort of 10,000 patients. We compared two scenarios in which different protocols would be used for the early diagnosis of prostate cancer in relation to PSA values. Scenario 1. BP-MRI/MP-MRI yearly if > 3.0 ng/ml, every 2 years otherwise; Scenario 2. BP-MRI/MP-MRI yearly with age-dependent threshold 3.5 ng/ml (50-59 years), 4.5 ng/ml (60-69 years), 6.5 ng/ml (70-79 years). Results: BP-MRI was more effective than the comparator in terms of cost (160.10 € vs 249.99€) QALYs (a mean of 9.12 vs 8.46), ICER (a mean of 232.45) and NMB (a mean of 273.439 vs 251.863). BP-MRI was dominant, being more effective and less expensive, with a lower social cost. Scenario 2 was more cost-effective compared to scenario 1. Conclusions: Our results confirmed the hypothesis that a short bi-parametric MRI protocol represents a cost-efficient procedure, optimizing resources in a policy perspective.
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Alanezi, Saleh T., Frank Sullivan, Christoph Kleefeld, John F. Greally, Marcin J. Kraśny, Peter Woulfe, Declan Sheppard, and Niall Colgan. "Quantifying Tumor Heterogeneity from Multiparametric Magnetic Resonance Imaging of Prostate Using Texture Analysis." Cancers 14, no. 7 (March 23, 2022): 1631. http://dx.doi.org/10.3390/cancers14071631.
Abstract:
(1) Background: Multiparametric MRI (mp-MRI) is used to manage patients with PCa. Tumor identification via irregular sampling or biopsy is problematic and does not allow the comprehensive detection of the phenotypic and genetic alterations in a tumor. A non-invasive technique to clinically assess tumor heterogeneity is also in demand. We aimed to identify tumor heterogeneity from multiparametric magnetic resonance images using texture analysis (TA). (2) Methods: Eighteen patients with prostate cancer underwent mp-MRI scans before prostatectomy. A single radiologist matched the histopathology report to single axial slices that best depicted tumor and non-tumor regions to generate regions of interest (ROIs). First-order statistics based on the histogram analysis, including skewness, kurtosis, and entropy, were used to quantify tumor heterogeneity. We compared non-tumor regions with significant tumors, employing the two-tailed Mann–Whitney U test. Analysis of the area under the receiver operating characteristic curve (ROC-AUC) was used to determine diagnostic accuracy. (3) Results: ADC skewness for a 6 × 6 px filter was significantly lower with an ROC-AUC of 0.82 (p = 0.001). The skewness of the ADC for a 9 × 9 px filter had the second-highest result, with an ROC-AUC of 0.66; however, this was not statistically significant (p = 0.08). Furthermore, there were no substantial distinctions between pixel filter size groups from the histogram analysis, including entropy and kurtosis. (4) Conclusions: For all filter sizes, there was poor performance in terms of entropy and kurtosis histogram analyses for cancer diagnosis. Significant prostate cancer may be distinguished using a textural feature derived from ADC skewness with a 6 × 6 px filter size.
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Savchenkov, Yu N., G. E. Trufanov, V. A. Fokin, A. Yu Efimtsev, S. E. Arakelov, I. Yu Titova, and A. R. Meltonyan. "Magnetic resonance imaging technique to quantify biomarkers for chronic liver diseases." Bulletin of the Medical Institute "REAVIZ" (REHABILITATION, DOCTOR AND HEALTH) 14, no. 1 (January 18, 2024): 159–67. http://dx.doi.org/10.20340/vmi-rvz.2024.1.mim.2.
Abstract:
Background. Recently, multiparametric magnetic resonance imaging (MRI) techniques have been developed to stratify clinically significant changes in chronic liver diseases (CLD). The advantage of multiparametric MRI is the visualization of the entire organ to eliminate the variability of the results during biopsy and the assessment of the quantitative characteristics of individual parameters of the liver parenchyma. A relatively new direction is the use of multiparametric MRI for the diagnosis of CLD with quantitative determination of fat, iron and fibrous changes in the liver parenchyma.Aim. To develop a multiparametric MRI technique for the quantitative assessment of biomarkers in CLD.Object and methods. A multiparametric MR study was performed in 57 patients with CLD using various pulse sequences.Conclusion. The article reflects the developed multiparametric MRI technique for quantifying biomarkers in CLD, based on data of which it is possible to abandon invasive interventions in the process of diagnosis and monitoring the response to treatment.
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Soni, Mukesh, Ihtiram Raza Khan, K. Suresh Babu, Syed Nasrullah, Abhishek Madduri, and Saima Ahmed Rahin. "Light Weighted Healthcare CNN Model to Detect Prostate Cancer on Multiparametric MRI." Computational Intelligence and Neuroscience 2022 (May 28, 2022): 1–11. http://dx.doi.org/10.1155/2022/5497120.
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The SEMRCNN model is proposed for autonomously extracting prostate cancer locations from regions of multiparametric magnetic resonance imaging (MP-MRI). Feature maps are explored in order to provide fine segmentation based on the candidate regions. Two parallel convolutional networks retrieve these maps of apparent diffusion coefficient (ADC) and T2W images, which are then integrated to use the complimentary information in MP-MRI. By utilizing extrusion and excitation blocks, it is feasible to automatically increase the number of relevant features in the fusion feature map. The aim of this study is to study the current scenario of the SE Mask-RCNN and deep convolutional network segmentation model that can automatically identify prostate cancer in the MP-MRI prostatic region. Experiments are conducted using 140 instances. SEMRCNN segmentation of prostate cancer lesions has a Dice coefficient of 0.654, a sensitivity of 0.695, a specificity of 0.970, and a positive predictive value of 0.685. SEMRCNN outperforms other models like as V net, Resnet50-U-net, Mask-RCNN, and U network model for prostate cancer MP-MRI segmentation. This approach accomplishes fine segmentation of lesions by recognizing and finding potential locations of prostate cancer lesions, eliminating interference from surrounding areas, and improving the learning of the lesions’ features.
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Duenweg, Savannah R., Xi Fang, Samuel A. Bobholz, Allison K. Lowman, Michael Brehler, Fitzgerald Kyereme, Kenneth A. Iczkowski, Kenneth M. Jacobsohn, Anjishnu Banerjee, and Peter S. LaViolette. "Diffusion Restriction Comparison between Gleason 4 Fused Glands and Cribriform Glands within Patient Using Whole-Mount Prostate Pathology as Ground Truth." Tomography 8, no. 2 (March 2, 2022): 635–43. http://dx.doi.org/10.3390/tomography8020053.
Abstract:
The presence and extent of cribriform patterned Gleason 4 (G4) glands are associated with poor prognosis following radical prostatectomy. This study used whole-mount prostate histology and multiparametric magnetic resonance imaging (MP-MRI) to evaluate diffusion differences in G4 gland morphology. Fourty-eight patients underwent MP-MRI prior to prostatectomy, of whom 22 patients had regions of both G4 cribriform glands and G4 fused glands (G4CG and G4FG, respectively). After surgery, the prostate was sliced using custom, patient-specific 3D-printed slicing jigs modeled according to the T2-weighted MR image, processed, and embedded in paraffin. Whole-mount hematoxylin and eosin-stained slides were annotated by our urologic pathologist and digitally contoured to differentiate the lumen, epithelium, and stroma. Digitized slides were co-registered to the T2-weighted MRI scan. Linear mixed models were fitted to the MP-MRI data to consider the different hierarchical structures at the patient and slide level. We found that Gleason 4 cribriform glands were more diffusion-restricted than fused glands.
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Singla, Amit, Nerbadyswari Deep, Suprava Naik, Sudipta Mohakud, Prasant Nayak, and Mukund Sable. "Correlation of multiparametric MRI with histopathological grade of peripheral zone prostate carcinoma." Journal of Cancer Research and Therapeutics 19, Suppl 2 (2023): S569—S576. http://dx.doi.org/10.4103/jcrt.jcrt_280_22.
Abstract:
ABSTRACTS Background: Prostatic cancer is the second most common malignant tumor in men. Preoperative grading of prostate cancer is important for its management. Our objective is to compare individual and combined detection rates of T2-weighted imaging (T2WI), diffusion weighted imaging (DWI), dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI), and magnetic resonance spectroscopy (MRS) for prostate cancer with histopathological diagnosis as its golden standard. Methods: Forty-four patients with positive digital rectal examination (DRE) findings and elevated prostate specific antigen (PSA), underwent multiparametric MRI (Mp-MRI). T2WI, DWI, DCE-MRI and MRS were done in all the patients. Cognitive magnetic resonance-transrectal ultrasound (MR-TRUS) fusion biopsy was done in all the patients. Sensitivity and specificity of T2WI, DWI, DCE-MRI, and Prostate Imaging – Reporting and Data System PIRADS version 2 was obtained. Apparent diffusion coefficient (ADC) value and choline/citrate ratio were obtained for each lesion and correlated with histopathological grade. Results: The mean age of the patients was 68.7 ± 10.1 years, and the mean serum PSA level was 58.1 ± 22.4 ng/dL. Of the 38 lesions in peripheral zone, 33 (87%) had histopathologically proven prostate cancer. T2WI had a sensitivity and specificity of 75.8% and 80% and DWI had a sensitivity and specificity of 90.9% and 80%, respectively, for detection of malignant prostatic lesion. The mean ADC values for prostate cancer, prostatitis, and normal prostatic parenchyma were 0.702 ± 0.094 × 10-3 mm2/sec, 0.959 ± 0.171 × 10-3 mm2/sec, and 1.31 ± 0.223 × 10-3 mm2/sec, respectively. Type 3 curve has lower sensitivity (45.5%) but high specificity (80%) for diagnosing prostate cancer. Conclusion: DWI can be useful to differentiate benign from malignant prostatic lesions, and low-grade from high-grade prostate carcinoma. ADC value has a positive correlation with histopathological grade of prostate cancer.
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Popat, Palak B., Sharad Maheshwari, Nilesh P. Sable, Meenakshi Thakur, and Aparna Katdare. "Multiparametric MRI Approach to Prostate Cancer with a Pictorial Essay on PI-RADS." Journal of Gastrointestinal and Abdominal Radiology 02, no. 01 (June 2019): 004–17. http://dx.doi.org/10.1055/s-0039-1683454.
Abstract:
AbstractThe biology of prostate cancer is indolent, and incidence does not reflect mortality. This has led to reframed screening guidelines pivoting around serum prostate-specific antigen (PSA) and conceptualizing clinically significant prostate cancer (CSC), triaging active surveillance and intervention. A resultant paradigm shift in magnetic resonance imaging (MRI) from diagnosing cancer to focusing on detecting CSC led to the establishment of PI-RADS v2 (prostate imaging reporting and data systems, version 2). In this article, we present an approach to analyzing suspicious prostate lesions on multiparametric MRI (mp-MRI) and assigning them a PI-RADS assessment score based on the current version 2 for standardized reporting, strengthening diagnostic accuracy, and improving clinical acceptance. We also present pitfalls and challenges that a radiologist should be aware of, for increasing diagnostic accuracy.
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Harvey, Hugh, and Nandita M. deSouza. "The role of imaging in the diagnosis of primary prostate cancer." Journal of Clinical Urology 9, no. 2_suppl (December 2016): 11–17. http://dx.doi.org/10.1177/2051415816656120.
Abstract:
Ultrasound and magnetic resonance imaging (MRI) are key imaging modalities in prostate cancer diagnosis. MRI offers a range of intrinsic contrast mechanisms (T2, diffusion-weighted imaging (DWI), MR spectroscopy (MRS)) and extrinsic contrast-generating options based on tumour vascular state following injection of weakly paramagnetic agents such as gadolinium. Together these parameters are referred to as multiparametric (mp)MRI and are used for detecting and guiding biopsy and staging prostate cancer. Although sensitivity of mpMRI is <75% for disease detection, specificity is >90% and a standardised reporting system together with MR-guided targeted biopsy is the optimal diagnostic pathway. Shear wave ultrasound elastography is a new technique which also holds promise for future studies. This article describes the developments in imaging the primary site of prostate cancer and reviews their current and future utility for screening, diagnosis and T-staging the disease.
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Lopes Dias, João, João Magalhães Pina, Raquel João, Joana Fialho, Sandra Carmo, Cecília Leal, Tiago Bilhim, Rui Mateus Marques, and Luís Campos Pinheiro. "Prostate Cancer: The Role of Multiparametric Magnetic Resonance Imaging." Acta Médica Portuguesa 28, no. 2 (April 30, 2015): 240. http://dx.doi.org/10.20344/amp.5370.
Abstract:
<p>Multiparametric Magnetic Resonance Imaging has been increasingly used for detection, localization and staging of prostate cancer over the last years. It combines high-resolution T2 Weighted-Imaging and at least two functional techniques, which include Dynamic Contrast–Enhanced Magnetic Resonance Imaging, Diffusion-Weighted Imaging, and Magnetic Resonance Imaging Spectroscopy. Although the combined use of a pelvic phased-array and an Endorectal Coil is considered the state-of-the-art for Magnetic Resonance Imaging evaluation of prostate cancer, Endorectal Coil is only absolute mandatory for Magnetic Resonance Imaging Spectroscopy at 1.5 T. Sensitivity and specificity levels in cancer detection and localization have been improving with functional technique implementation, compared to T2 Weighted-Imaging alone. It has been particularly useful to evaluate patients with abnormal PSA and negative biopsy. Moreover, the information added by the functional techniques may correlate to cancer aggressiveness and therefore be useful to select patients for focal radiotherapy, prostate sparing surgery, focal ablative therapy and active surveillance. However, more studies are needed to compare the functional techniques and understand the advantages and disadvantages of each one. This article reviews the basic principles of prostatic mp-Magnetic Resonance Imaging, emphasizing its role on detection, staging and active surveillance of prostate cancer.</p>
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Platzek, Ivan, Hagen H. Kitzler, Volker Gudziol, Michael Laniado, and Gabriele Hahn. "Magnetic resonance imaging in acute mastoiditis." Acta Radiologica Short Reports 3, no. 2 (February 1, 2014): 204798161452341. http://dx.doi.org/10.1177/2047981614523415.
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Background In cases of suspected mastoiditis, imaging is used to evaluate the extent of mastoid destruction and possible complications. The role of magnetic resonance imaging (MRI) in mastoiditis has not been systematically evaluated. Purpose To assess the diagnostic performance of MRI in patients with suspected acute mastoiditis. Material and Methods Twenty-three patients with suspected acute mastoiditis were included in this retrospective study (15 boys, 8 girls; mean age, 2 years 11 months). All patients were examined on a 1.5 T MRI system. The MRI examination included both enhanced and non-enhanced turbo spin echo (TSE), diffusion-weighted images, and venous time-of-flight magnetic resonance angiography (TOF MRA) for the evaluation of the venous sinuses. Surgical findings, as well as clinical and imaging follow-up were used as the standard of reference. The sensitivity and accuracy of MRI for mastoiditis and subperiosteal abscesses was calculated. Results Twenty (87%) of 23 patients had mastoiditis, and 12 (52%) of 23 patients had a subperiosteal abscess in addition to mastoiditis. Mastoiditis and subperiosteal abscesses were identified by MRI in all cases. Sensitivity for mastoiditis was 100%, specificity was 66%, and accuracy was 86%. Sensitivity for subperiosteal abscesses was 100% and accuracy was 100%. Conclusion Multiparametric MRI has high accuracy for mastoiditis and subperiosteal abscesses.
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Stoia, Sebastian, Manuela Lenghel, Cristian Dinu, Tiberiu Tamaș, Simion Bran, Mihaela Băciuț, Emil Boțan, et al. "The Value of Multiparametric Magnetic Resonance Imaging in the Preoperative Differential Diagnosis of Parotid Gland Tumors." Cancers 15, no. 4 (February 19, 2023): 1325. http://dx.doi.org/10.3390/cancers15041325.
Abstract:
Background: The aim of the present study was to determine the value of multiparametric MRI in the preoperative differential diagnosis of parotid tumors, which is essential for therapeutic strategy selection. Methods: A three-year prospective study was conducted with 65 patients. Each patient was investigated preoperatively with multiparametric MRI and surgical excision of the tumor was performed. The preoperative imaging diagnosis was compared with the histopathological report. Several MRI parameters were analyzed, including T1 and T2 weighted image (WI), apparent diffusion coefficient (ADC), time to peak (TTP), and the time intensity curve (TIC). Results: In the differential diagnosis of benign from malignant tumors, T2WI and ADC showed statistically significant differences. Multiparametric MRI had a sensitivity, specificity, and accuracy of 81.8%, 88.6% and 92.3%, respectively. All of the studied parameters (T1, T2, TIC, TTP, ADC) were significantly different in the comparison between pleomorphic adenomas and Warthin tumors. With reference to the scope of this study, the conjunction of multiparametric and conventional MRI demonstrated a sensitivity, specificity, and accuracy of 94.1%, 100%, and 97.8%, respectively. Conclusions: Morphological analysis using conventional MRI combined with diffusion-weighted imaging (DW) and dynamic contrast–enhanced (DCE) multiparametric MRI improved the preoperative differential diagnosis of parotid gland tumors.
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Wang, Shijun, Karen Burtt, Baris Turkbey, Peter Choyke, and Ronald M. Summers. "Computer Aided-Diagnosis of Prostate Cancer on Multiparametric MRI: A Technical Review of Current Research." BioMed Research International 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/789561.
Abstract:
Prostate cancer (PCa) is the most commonly diagnosed cancer among men in the United States. In this paper, we survey computer aided-diagnosis (CADx) systems that use multiparametric magnetic resonance imaging (MP-MRI) for detection and diagnosis of prostate cancer. We review and list mainstream techniques that are commonly utilized in image segmentation, registration, feature extraction, and classification. The performances of 15 state-of-the-art prostate CADx systems are compared through the area under their receiver operating characteristic curves (AUC). Challenges and potential directions to further the research of prostate CADx are discussed in this paper. Further improvements should be investigated to make prostate CADx systems useful in clinical practice.
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Kufer, Jan, Christine Preibisch, Samira Epp, Jens Göttler, Lena Schmitzer, Claus Zimmer, Fahmeed Hyder, and Stephan Kaczmarz. "Imaging effective oxygen diffusivity in the human brain with multiparametric magnetic resonance imaging." Journal of Cerebral Blood Flow & Metabolism 42, no. 2 (September 30, 2021): 349–63. http://dx.doi.org/10.1177/0271678x211048412.
Abstract:
Cerebrovascular diseases can impair blood circulation and oxygen extraction from the blood. The effective oxygen diffusivity (EOD) of the capillary bed is a potential biomarker of microvascular function that has gained increasing interest, both for clinical diagnosis and for elucidating oxygen transport mechanisms. Models of capillary oxygen transport link EOD to measurable oxygen extraction fraction (OEF) and cerebral blood flow (CBF). In this work, we confirm that two well established mathematical models of oxygen transport yield nearly equivalent EOD maps. Furthermore, we propose an easy-to-implement and clinically applicable multiparametric magnetic resonance imaging (MRI) protocol for quantitative EOD mapping. Our approach is based on imaging OEF and CBF with multiparametric quantitative blood oxygenation level dependent (mq-BOLD) MRI and pseudo-continuous arterial spin labeling (pCASL), respectively. We evaluated the imaging protocol by comparing MRI-EOD maps of 12 young healthy volunteers to PET data from a published study in different individuals. Our results show comparably good correlation between MRI- and PET-derived cortical EOD, OEF and CBF. Importantly, absolute values of MRI and PET showed high accordance for all three parameters. In conclusion, our data indicates feasibility of the proposed MRI protocol for EOD mapping, rendering the method promising for future clinical evaluation of patients with cerebrovascular diseases.
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Citak-Er, Fusun, Metin Vural, Omer Acar, Tarik Esen, Aslihan Onay, and Esin Ozturk-Isik. "Final Gleason Score Prediction Using Discriminant Analysis and Support Vector Machine Based on Preoperative Multiparametric MR Imaging of Prostate Cancer at 3T." BioMed Research International 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/690787.
Abstract:
Objective.This study aimed at evaluating linear discriminant analysis (LDA) and support vector machine (SVM) classifiers for estimating final Gleason score preoperatively using multiparametric magnetic resonance imaging (mp-MRI) and clinical parameters.Materials and Methods.Thirty-three patients who underwent mp-MRI on a 3T clinical MR scanner and radical prostatectomy were enrolled in this study. The input features for classifiers were age, the presence of a palpable prostate abnormality, prostate specific antigen (PSA) level, index lesion size, and Likert scales of T2 weighted MRI (T2w-MRI), diffusion weighted MRI (DW-MRI), and dynamic contrast enhanced MRI (DCE-MRI) estimated by an experienced radiologist. SVM based recursive feature elimination (SVM-RFE) was used for eliminating features. Principal component analysis (PCA) was applied for data uncorrelation.Results.Using a standard PCA before final Gleason score classification resulted in mean sensitivities of 51.19% and 64.37% and mean specificities of 72.71% and 39.90% for LDA and SVM, respectively. Using a Gaussian kernel PCA resulted in mean sensitivities of 86.51% and 87.88% and mean specificities of 63.99% and 56.83% for LDA and SVM, respectively.Conclusion.SVM classifier resulted in a slightly higher sensitivity but a lower specificity than LDA method for final Gleason score prediction for prostate cancer for this limited patient population.
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Li, Bochong, Ryo Oka, Ping Xuan, Yuichiro Yoshimura, and Toshiya Nakaguchi. "Semi-Automatic Multiparametric MR Imaging Classification Using Novel Image Input Sequences and 3D Convolutional Neural Networks." Algorithms 15, no. 7 (July 18, 2022): 248. http://dx.doi.org/10.3390/a15070248.
Abstract:
The role of multi-parametric magnetic resonance imaging (mp-MRI) is becoming increasingly important in the diagnosis of the clinical severity of prostate cancer (PCa). However, mp-MRI images usually contain several unaligned 3D sequences, such as DWI image sequences and T2-weighted image sequences, and there are many images among the entirety of 3D sequence images that do not contain cancerous tissue, which affects the accuracy of large-scale prostate cancer detection. Therefore, there is a great need for a method that uses accurate computer-aided detection of mp-MRI images and minimizes the influence of useless features. Our proposed PCa detection method is divided into three stages: (i) multimodal image alignment, (ii) automatic cropping of the sequence images to the entire prostate region, and, finally, (iii) combining multiple modal images of each patient into novel 3D sequences and using 3D convolutional neural networks to learn the newly composed 3D sequences with different modal alignments. We arrange the different modal methods to make the model fully learn the cancerous tissue features; then, we predict the clinical severity of PCa and generate a 3D cancer response map for the 3D sequence images from the last convolution layer of the network. The prediction results and 3D response map help to understand the features that the model focuses on during the process of 3D-CNN feature learning. We applied our method to Toho hospital prostate cancer patient data; the AUC (=0.85) results were significantly higher than those of other methods.
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Elmokadem, Ali H., Ahmed M. Abdel Khalek, Rihame M. Abdel Wahab, Nehal Tharwat, Ghada M. Gaballa, Mohamed Abo Elata, and Talal Amer. "Diagnostic Accuracy of Multiparametric Magnetic Resonance Imaging for Differentiation between Parotid Neoplasms." Canadian Association of Radiologists Journal 70, no. 3 (August 2019): 264–72. http://dx.doi.org/10.1016/j.carj.2018.10.010.
Abstract:
Purpose This study was designed to evaluate the role of multiparametric magnetic resonance imaging (MRI) for differentiation of parotid gland neoplasms. Methods Prospective study was conducted upon 52 consecutive patients (30 men, 22 women; aged 24–78 years; mean, 51 years) with parotid tumours that underwent multiparametric MRI using combined static MRI, dynamic contrast enhanced (DCE) MRI, and diffusion-weighted imaging (DWI). The static MRI parameter, time signal intensity curves (TIC) derived from DCE-MRI, and apparent diffusion coefficient (ADC) values of parotid tumours were correlated with histopathological findings. Results Static MRI revealed a significant difference between both benign and malignant lesions in regards to margin definition ( P < .001) and T2 hypointensity ( P < .013), with a diagnostic accuracy 95% and 78.33% respectively. Study of the TIC type on DCE-MRI revealed statistically significant difference between benign and malignant lesions ( P < .001) and diagnostic accuracy 96.55%. There was no statistically significant difference ( P = .181) between the ADC values of benign and malignant lesions. ROC curve analysis revealed that by using ADC cut-off value of 1 × 10−3 mm2/s had accuracy of 84.62% respectively for differentiating Warthin from malignant tumours that could be modified to higher value (94.28%) by excluding lymphoma from malignant lesions. By using cutoff value of 1.3 × 10−3 mm2/s to differentiate pleomorphic adenoma from malignancy, ROC curve analysis had high accuracy of 97.06%. Conclusion Multiparametric MRI can be used for differentiation of malignant from benign parotid tumours and characterization of some benign parotid tumours.
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Robbins, Katherine, Miles Seidel, Deirdre Kiernan, John Hooper, Adrian Clubb, Rino Olivotto, Anna Salkeld, Bhuvana Srinivasan, and Admire Matsika. "Utility of multiparametric magnetic resonance imaging (mp-MRI) for screening and detection of high risk prostate cancer." Pathology 50 (February 2018): S83. http://dx.doi.org/10.1016/j.pathol.2017.12.222.
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Switlyk, Marta D., Andreas Hopland, Edmund Reitan, Shivanthe Sivanesan, Bjørn Brennhovd, Ulrika Axcrona, and Knut H. Hole. "Multiparametric Magnetic Resonance Imaging of Penile Cancer: A Pictorial Review." Cancers 15, no. 22 (November 8, 2023): 5324. http://dx.doi.org/10.3390/cancers15225324.
Abstract:
The role of multiparametric magnetic resonance imaging (mpMRI) in assessing penile cancer is not well defined. However, this modality may be successfully applied for preoperative staging and patient selection; postoperative local and regional surveillance; and assessments of treatment response after oncological therapies. Previous studies have been mostly limited to a few small series evaluating the accuracy of MRI for the preoperative staging of penile cancer. This review discusses the principles of non-erectile mpMRI, including functional techniques and their applications in evaluating the male genital region, along with clinical protocols and technical considerations. The latest clinical classifications and guidelines are reviewed, focusing on imaging recommendations and discussing potential gaps and disadvantages. The development of functional MRI techniques and the extraction of quantitative parameters from these sequences enables the noninvasive assessment of phenotypic and genotypic tumor characteristics. The applications of advanced techniques in penile MRI are yet to be defined. There is a need for prospective trials and feasible multicenter trials due to the rarity of the disease, highlighting the importance of minimum technical requirements for MRI protocols, particularly image resolution, and finally determining the role of mpMRI in the assessment of penile cancer
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Dijkhuizen, Rick M., and Klaas Nicolay. "Magnetic Resonance Imaging in Experimental Models of Brain Disorders." Journal of Cerebral Blood Flow & Metabolism 23, no. 12 (December 2003): 1383–402. http://dx.doi.org/10.1097/01.wcb.0000100341.78607.eb.
Abstract:
This review gives an overview of the application of magnetic resonance imaging (MRI) in experimental models of brain disorders. MRI is a noninvasive and versatile imaging modality that allows longitudinal and three-dimensional assessment of tissue morphology, metabolism, physiology, and function. MRI can be sensitized to proton density, T1, T2, susceptibility contrast, magnetization transfer, diffusion, perfusion, and flow. The combination of different MRI approaches (e.g., diffusion-weighted MRI, perfusion MRI, functional MRI, cell-specific MRI, and molecular MRI) allows in vivo multiparametric assessment of the pathophysiology, recovery mechanisms, and treatment strategies in experimental models of stroke, brain tumors, multiple sclerosis, neurodegenerative diseases, traumatic brain injury, epilepsy, and other brain disorders. This report reviews established MRI methods as well as promising developments in MRI research that have advanced and continue to improve our understanding of neurologic diseases and that are believed to contribute to the development of recovery improving strategies.
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Tsili, Athina C., Olga N. Xiropotamou, Michael Nomikos, and Maria I. Argyropoulou. "Silicone-induced Penile Sclerosing Lipogranuloma: Magnetic Resonance Imaging Findings." Journal of Clinical Imaging Science 6 (January 28, 2016): 3. http://dx.doi.org/10.4103/2156-7514.175084.
Abstract:
Sclerosing lipogranuloma is a rare benign disease, representing a peculiar granulomatous reaction of fatty tissue. The majority of cases are secondary to injection of exogenous foreign bodies, such as silicone, paraffin, mineral, or vegetable oils. To the best of our knowledge, we present the first case of a silicone-induced penile lipogranuloma in a 52-year-old man evaluated with a multiparametric magnetic resonance imaging (MRI) protocol, including diffusion-weighted imaging, magnetization transfer imaging, and dynamic contrast-enhanced MRI. MRI of the penis by combining both conventional and functional information represents an important imaging tool in the preoperative workup of silicone-induced penile lipogranuloma.
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Pichiecchio, A., E. Tavazzi, G. Poloni, M. Ponzio, F. Palesi, M. Pasin, L. Piccolo, et al. "Advanced magnetic resonance imaging of neuromyelitis optica: a multiparametric approach." Multiple Sclerosis Journal 18, no. 6 (December 19, 2011): 817–24. http://dx.doi.org/10.1177/1352458511431072.
Abstract:
Background: Several authors have used advanced magnetic resonance imaging (MRI) techniques to investigate whether patients with neuromyelitis optica (NMO) have occult damage in normal-appearing brain tissue, similarly to multiple sclerosis (MS). To date, the literature contains no data derived from the combined use of several advanced MRI techniques in the same NMO subjects. Objective: We set out to determine whether occult damage could be detected in the normal-appearing brain tissue of a small group of patients with NMO using a multiparametric MRI approach. Methods: Eight female patients affected by NMO (age range 44–58 years) and seven sex- and age-matched healthy controls were included. The techniques used on a 1.5 T MRI imaging scanner were magnetization transfer imaging, diffusion tensor imaging, tract-based spatial statistics, spectroscopy and voxel-based morphometry in order to analyse normal-appearing white matter and normal-appearing grey matter. Results: Structural and metabolic parameters showed no abnormalities in normal-appearing white matter of patients with NMO. Conversely, tract-based spatial statistics demonstrated a selective alteration of the optic pathways and the lateral geniculate nuclei. Diffusion tensor imaging values in the normal-appearing grey matter were found to be significantly different in the patients with NMO versus the healthy controls. Moreover, voxel-based morphometry analysis demonstrated a significant density and volume reduction of the sensorimotor cortex and the visual cortex. Conclusions: Our data disclosed occult structural damage in the brain of patients with NMO, predominantly involving regions connected with motor and visual systems. This damage seems to be the direct consequence of transsynaptic degeneration triggered by lesions of the optic nerve and spine.
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Lee, Seunghyun, Seung Hong Choi, Hye Rim Cho, Jaemoon Koh, Chul-Kee Park, and Tomotsugu Ichikawa. "Multiparametric magnetic resonance imaging features of a canine glioblastoma model." PLOS ONE 16, no. 7 (July 9, 2021): e0254448. http://dx.doi.org/10.1371/journal.pone.0254448.
Abstract:
Purpose To assess glioblastoma multiforme (GBM) formation with similar imaging characteristics to human GBM using multiparametric magnetic resonance imaging (MRI) in an orthotopic xenograft canine GBM model. Materials and methods The canine GBM cell line J3T1 was subcutaneously injected into 6-week-old female BALB/c nude mice to obtain tumour fragments. Tumour fragments were implanted into adult male mongrel dog brains through surgery. Multiparametric MRI was performed with conventional MRI, diffusion-weighted imaging, and dynamic susceptibility contrast-enhanced perfusion-weighted imaging at one week and two weeks after surgery in a total of 15 surgical success cases. The presence of tumour cells, the necrotic area fraction, and the microvessel density (MVD) of the tumour on the histologic specimen were assessed. Tumour volume, diffusion, and perfusion parameters were compared at each time point using Wilcoxon signed-rank tests, and the differences between tumour and normal parenchyma were compared using unpaired t-tests. Spearman correlation analysis was performed between the imaging and histologic parameters. Results All animals showed a peripheral enhancing lesion on MRI and confirmed the presence of a tumour through histologic analysis (92.3%). The normalized perfusion values did not show significant decreases through at least 2 weeks after the surgery (P > 0.05). There was greater cerebral blood volume and flow in the GBM than in the normal-appearing white matter (1.46 ± 0.25 vs. 1.13 ± 0.16 and 1.30 ± 0.22 vs. 1.02 ± 0.14; P < 0.001 and P < 0.001, respectively). The MVD in the histologic specimens was correlated with the cerebral blood volume in the GBM tissue (r = 0.850, P = 0.004). Conclusion Our results suggest that the canine GBM model showed perfusion imaging characteristics similar to those of humans, and it might have potential as a model to assess novel technical developments for GBM treatment.
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Kocar, Thomas D., Hans-Peter Müller, Albert C. Ludolph, and Jan Kassubek. "Feature selection from magnetic resonance imaging data in ALS: a systematic review." Therapeutic Advances in Chronic Disease 12 (January 2021): 204062232110510. http://dx.doi.org/10.1177/20406223211051002.
Abstract:
Background: With the advances in neuroimaging in amyotrophic lateral sclerosis (ALS), it has been speculated that multiparametric magnetic resonance imaging (MRI) is capable to contribute to early diagnosis. Machine learning (ML) can be regarded as the missing piece that allows for the useful integration of multiparametric MRI data into a diagnostic classifier. The major challenges in developing ML classifiers for ALS are limited data quantity and a suboptimal sample to feature ratio which can be addressed by sound feature selection. Methods: We conducted a systematic review to collect MRI biomarkers that could be used as features by searching the online database PubMed for entries in the recent 4 years that contained cross-sectional neuroimaging data of subjects with ALS and an adequate control group. In addition to the qualitative synthesis, a semi-quantitative analysis was conducted for each MRI modality that indicated which brain regions were most commonly reported. Results: Our search resulted in 151 studies with a total of 221 datasets. In summary, our findings highly resembled generally accepted neuropathological patterns of ALS, with degeneration of the motor cortex and the corticospinal tract, but also in frontal, temporal, and subcortical structures, consistent with the neuropathological four-stage model of the propagation of pTDP-43 in ALS. Conclusions: These insights are discussed with respect to their potential for MRI feature selection for future ML-based neuroimaging classifiers in ALS. The integration of multiparametric MRI including DTI, volumetric, and texture data using ML may be the best approach to generate a diagnostic neuroimaging tool for ALS.
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Georgiev, Aleksandar, Lyubomir Chervenkov, Mladen Doykov, Katya Doykova, Petar Uchikov, and Silvia Tsvetkova. "Surveillance Value of Apparent Diffusion Coefficient Maps: Multiparametric MRI in Active Surveillance of Prostate Cancer." Cancers 15, no. 4 (February 10, 2023): 1128. http://dx.doi.org/10.3390/cancers15041128.
Abstract:
Background: This study aims to establish the value of apparent diffusion coefficient maps and other magnetic resonance sequences for active surveillance of prostate cancer. The study included 530 men with an average age of 66, who were under surveillance for prostate cancer. We have used multiparametric magnetic resonance imaging with subsequent transperineal biopsy (TPB) to verify the imaging findings. Results: We have observed a level of agreement of 67.30% between the apparent diffusion coefficient (ADC) maps, other magnetic resonance sequences, and the biopsy results. The sensitivity of the apparent diffusion coefficient is 97.14%, and the specificity is 37.50%. According to our data, apparent diffusion coefficient is the most accurate sequence, followed by diffusion imaging in prostate cancer detection. Conclusions: Based on our findings we advocate that the apparent diffusion coefficient should be included as an essential part of magnetic resonance scanning protocols for prostate cancer in at least bi-parametric settings. The best option will be apparent diffusion coefficient combined with diffusion imaging and T2 sequences. Further large-scale prospective controlled studies are required to define the precise role of multiparametric and bi-parametric magnetic resonance in the active surveillance of prostate cancer.
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Kardos, Steven V., Cayce Nawaf, Richard Fan, Daniel Cornfeld, Jeffrey Weinreb, Peter Schulam, and Preston Sprenkle. "Simplified prostate lesion grading for magnetic resonance imaging and improved cancer detection at fusion-targeted prostate biopsy." Journal of Clinical Oncology 33, no. 7_suppl (March 1, 2015): 69. http://dx.doi.org/10.1200/jco.2015.33.7_suppl.69.
Abstract:
69 Background: Prostate cancer (PCa) is the most common solid organ malignancy in men and the second leading cause of cancer related death; however, it is the only tumor that is diagnosed by a non-targeted sampling method. Fusion targeted prostate biopsy is emerging as a more accurate way to detect PCa. The use of a multiparametric MRI (MP-MRI) with an endorectal coil (ERC) has traditionally been used, though the benefit for detection with ERC is controversial. In addition, there is significant heterogeneity in classification of MRI-identified lesions. We provide an initial report with fusion biopsy without an ERC and utilizing a simplified 3-point Likert scale for grading prostatic lesions. Methods: Patients underwent MRI-USG fusion biopsy for elevated PSA, abnormal DRE, or prior negative biopsy. Lesions visible on MRI were outlined in 3D and assigned increasing cancer suspicion levels using a simplified 3-point Likert scale by dedicated pelvic radiologists. The Artemis biopsy tracking system was used to fuse the MRI with real-time ultrasound. Using the 3D model, a 12-core systematic biopsy, as well as a targeted biopsy of suspicious areas, was performed by a urologist (PS). Results: 190 patients underwent MRI and fusion biopsy between 12/2012 and 8/2014. The overall cancer detection rate (CDR) for systematic biopsy was 52.3% and the CDR for fusion biopsy was 55.0%. However, the CDR for clinically significant PCa with systematic biopsy was 28.7% and for targeted biopsy was 43.8% (p=0.02). Evaluation of cancer suspicion level for each ROI revealed that patients with high suspicion scores had a higher overall CDR (p<0.0001) and higher risk of detecting clinically significant cancer under the Cochran Armitage Trend test (p=0.0001). Conclusions: MRI-USG fusion prostate biopsy using MP-MRI without an ERC and a read using a simplified 3-point Likert scale demonstrate improved detection of clinically significant PCa compared to a systematic 12 core TRUS biopsy, and further demonstrate that lesion suspicion correlates with CDR. CDR and lesion stratification are comparable to the published literature when using methodologies that may be more practical in a larger number of medical centers.
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Fathi Kazerooni, Anahita, Hamed Akbari, Gaurav Shukla, Chaitra Badve, Jeffrey D. Rudie, Chiharu Sako, Saima Rathore, et al. "Cancer Imaging Phenomics via CaPTk: Multi-Institutional Prediction of Progression-Free Survival and Pattern of Recurrence in Glioblastoma." JCO Clinical Cancer Informatics, no. 4 (September 2020): 234–44. http://dx.doi.org/10.1200/cci.19.00121.
Abstract:
PURPOSE To construct a multi-institutional radiomic model that supports upfront prediction of progression-free survival (PFS) and recurrence pattern (RP) in patients diagnosed with glioblastoma multiforme (GBM) at the time of initial diagnosis. PATIENTS AND METHODS We retrospectively identified data for patients with newly diagnosed GBM from two institutions (institution 1, n = 65; institution 2, n = 15) who underwent gross total resection followed by standard adjuvant chemoradiation therapy, with pathologically confirmed recurrence, sufficient follow-up magnetic resonance imaging (MRI) scans to reliably determine PFS, and available presurgical multiparametric MRI (MP-MRI). The advanced software suite Cancer Imaging Phenomics Toolkit (CaPTk) was leveraged to analyze standard clinical brain MP-MRI scans. A rich set of imaging features was extracted from the MP-MRI scans acquired before the initial resection and was integrated into two distinct imaging signatures for predicting mean shorter or longer PFS and near or distant RP. The predictive signatures for PFS and RP were evaluated on the basis of different classification schemes: single-institutional analysis, multi-institutional analysis with random partitioning of the data into discovery and replication cohorts, and multi-institutional assessment with data from institution 1 as the discovery cohort and data from institution 2 as the replication cohort. RESULTS These predictors achieved cross-validated classification performance (ie, area under the receiver operating characteristic curve) of 0.88 (single-institution analysis) and 0.82 to 0.83 (multi-institution analysis) for prediction of PFS and 0.88 (single-institution analysis) and 0.56 to 0.71 (multi-institution analysis) for prediction of RP. CONCLUSION Imaging signatures of presurgical MP-MRI scans reveal relatively high predictability of time and location of GBM recurrence, subject to the patients receiving standard first-line chemoradiation therapy. Through its graphical user interface, CaPTk offers easy accessibility to advanced computational algorithms for deriving imaging signatures predictive of clinical outcome and could similarly be used for a variety of radiomic and radiogenomic analyses.
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Yu, Jinxing, Ann S. Fulcher, Sarah Winks, Mary A. Turner, William Behl, Anna Lee Ware, Nitai D. Mukhopadhyay, et al. "Utilization of Multiparametric MRI of Prostate in Patients under Consideration for or Already in Active Surveillance: Correlation with Imaging Guided Target Biopsy." Diagnostics 10, no. 7 (June 29, 2020): 441. http://dx.doi.org/10.3390/diagnostics10070441.
Abstract:
This study sought to assess the value of multiparametric magnetic resonance image (mp-MRI) in patients with a prostate cancer (PCa) Gleason score of 6 or less under consideration for or already in active surveillance and to determine the rate of upgrading by target biopsy. Three hundred and fifty-four consecutive men with an initial transrectal ultrasound-guided (TRUS) biopsy-confirmed PCa Gleason score of 6 or less under clinical consideration for or already in active surveillance underwent mp-MRI and were retrospectively reviewed. One hundred and nineteen of 354 patients had cancer-suspicious regions (CSRs) at mp-MRI. Each CSR was assigned a Prostate Imaging Reporting and Data System (PI-RADS) score based on PI-RADS v2. One hundred and eight of 119 patients underwent confirmatory imaging-guided biopsy for CSRs. Pathology results including Gleason score (GS) and percentage of specimens positive for PCa were recorded. Associations between PI-RADS scores and findings at target biopsy were evaluated using logistic regression. At target biopsy, 81 of 108 patients had PCa (75%). Among them, 77 patients had upgrading (22%, 77 of 354 patients). One hundred and forty-six CSRs in 108 patients had PI-RADS 3 n = 28, 4 n = 66, and 5 n = 52. The upgraded rate for each category of CSR was for PI-RADS 3 (5 of 28, 18%), 4 (47 of 66, 71%) and 5 (49 of 52, 94%). Using logistic regression analysis, differences in PI-RADS scores from 3 to 5 are significantly associated with the probability of disease upgrade (20%, 73%, and 96% for PI-RADS score of 3, 4, and 5, respectively). Adding mp-MRI to patients under consideration for or already in active surveillance helps to identify undiagnosed PCa of a higher GS or higher volume resulting in upgrading in 22%.
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Mazzacane, Federico, Valentina Mazzoleni, Elisa Scola, Sara Mancini, Ivano Lombardo, Giorgio Busto, Elisa Rognone, et al. "Vessel Wall Magnetic Resonance Imaging in Cerebrovascular Diseases." Diagnostics 12, no. 2 (January 20, 2022): 258. http://dx.doi.org/10.3390/diagnostics12020258.
Abstract:
Cerebrovascular diseases are a leading cause of disability and death worldwide. The definition of stroke etiology is mandatory to predict outcome and guide therapeutic decisions. The diagnosis of pathological processes involving intracranial arteries is especially challenging, and the visualization of intracranial arteries’ vessel walls is not possible with routine imaging techniques. Vessel wall magnetic resonance imaging (VW-MRI) uses high-resolution, multiparametric MRI sequences to directly visualize intracranial arteries walls and their pathological alterations, allowing a better characterization of their pathology. VW-MRI demonstrated a wide range of clinical applications in acute cerebrovascular disease. Above all, it can be of great utility in the differential diagnosis of atherosclerotic and non-atherosclerotic intracranial vasculopathies. Additionally, it can be useful in the risk stratification of intracranial atherosclerotic lesions and to assess the risk of rupture of intracranial aneurysms. Recent advances in MRI technology made it more available, but larger studies are still needed to maximize its use in daily clinical practice.
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Chen, Quan, Shiliang Hu, Peiran Long, Fang Lu, Yujie Shi, and Yunpeng Li. "A Transfer Learning Approach for Malignant Prostate Lesion Detection on Multiparametric MRI." Technology in Cancer Research & Treatment 18 (January 1, 2019): 153303381985836. http://dx.doi.org/10.1177/1533033819858363.
Abstract:
Purpose: In prostate focal therapy, it is important to accurately localize malignant lesions in order to increase biological effect of the tumor region while achieving a reduction in dose to noncancerous tissue. In this work, we proposed a transfer learning–based deep learning approach, for classification of prostate lesions in multiparametric magnetic resonance imaging images. Methods: Magnetic resonance imaging images were preprocessed to remove bias artifact and normalize the data. Two state-of-the-art deep convolutional neural network models, InceptionV3 and VGG-16, were pretrained on ImageNet data set and retuned on the multiparametric magnetic resonance imaging data set. As lesion appearances differ by the prostate zone that it resides in, separate models were trained. Ensembling was performed on each prostate zone to improve area under the curve. In addition, the predictions from lesions on each prostate zone were scaled separately to increase the area under the curve for all lesions combined. Results: The models were tuned to produce the highest area under the curve on validation data set. When it was applied to the unseen test data set, the transferred InceptionV3 model achieved an area under the curve of 0.81 and the transferred VGG-16 model achieved an area under the curve of 0.83. This was the third best score among the 72 methods from 33 participating groups in ProstateX competition. Conclusion: The transfer learning approach is a promising method for prostate cancer detection on multiparametric magnetic resonance imaging images. Features learned from ImageNet data set can be useful for medical images.
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Crisan, Nicolae, Iulia Andras, Corina Radu, David Andras, Radu-Tudor Coman, Paul Tucan, Doina Pisla, Dana Crisan, and Ioan Coman. "Prostate ultrasound: back in business!" Medical Ultrasonography 19, no. 4 (November 29, 2017): 423. http://dx.doi.org/10.11152/mu-1147.
Abstract:
The use of grey scale prostate ultrasound decreased after the implementation of magnetic resonance imaging (MRI) for the diagnosis and evaluation of prostate cancer. The new developments, such as multiparametric ultrasound and MRI-ultrasound fusion technology, renewed the interest for this imaging method in the assessment of prostate cancer. The purpose of this paper was to review the current role of prostate ultrasound in the setting of these new applications. A thorough reevaluation of the selection criteria of the patients is required to assess which patients would benefit from multiparametric ultrasound, who wouldbenefit from multiparametric MRI or the combination of both to assist prostate biopsy in order to ensure the balance between overdiagnosis and underdiagnosis of prostate cancer.
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Zhang, Li, Xia Zhe, Min Tang, Jing Zhang, Jialiang Ren, Xiaoling Zhang, and Longchao Li. "Predicting the Grade of Prostate Cancer Based on a Biparametric MRI Radiomics Signature." Contrast Media & Molecular Imaging 2021 (December 23, 2021): 1–10. http://dx.doi.org/10.1155/2021/7830909.
Abstract:
Purpose. This study aimed to investigate the value of biparametric magnetic resonance imaging (bp-MRI)-based radiomics signatures for the preoperative prediction of prostate cancer (PCa) grade compared with visual assessments by radiologists based on the Prostate Imaging Reporting and Data System Version 2.1 (PI-RADS V2.1) scores of multiparametric MRI (mp-MRI). Methods. This retrospective study included 142 consecutive patients with histologically confirmed PCa who were undergoing mp-MRI before surgery. MRI images were scored and evaluated by two independent radiologists using PI-RADS V2.1. The radiomics workflow was divided into five steps: (a) image selection and segmentation, (b) feature extraction, (c) feature selection, (d) model establishment, and (e) model evaluation. Three machine learning algorithms (random forest tree (RF), logistic regression, and support vector machine (SVM)) were constructed to differentiate high-grade from low-grade PCa. Receiver operating characteristic (ROC) analysis was used to compare the machine learning-based analysis of bp-MRI radiomics models with PI-RADS V2.1. Results. In all, 8 stable radiomics features out of 804 extracted features based on T2-weighted imaging (T2WI) and ADC sequences were selected. Radiomics signatures successfully categorized high-grade and low-grade PCa cases ( P < 0.05 ) in both the training and test datasets. The radiomics model-based RF method (area under the curve, AUC: 0.982; 0.918), logistic regression (AUC: 0.886; 0.886), and SVM (AUC: 0.943; 0.913) in both the training and test cohorts had better diagnostic performance than PI-RADS V2.1 (AUC: 0.767; 0.813) when predicting PCa grade. Conclusions. The results of this clinical study indicate that machine learning-based analysis of bp-MRI radiomic models may be helpful for distinguishing high-grade and low-grade PCa that outperformed the PI-RADS V2.1 scores based on mp-MRI. The machine learning algorithm RF model was slightly better.
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Jokivarsi, Kimmo T., Yrjö Hiltunen, Pasi I. Tuunanen, Risto A. Kauppinen, and Olli HJ Gröhn. "Correlating Tissue Outcome with Quantitative Multiparametric MRI of Acute Cerebral Ischemia in Rats." Journal of Cerebral Blood Flow & Metabolism 30, no. 2 (November 11, 2009): 415–27. http://dx.doi.org/10.1038/jcbfm.2009.236.
Abstract:
Predicting tissue outcome remains a challenge for stroke magnetic resonance imaging (MRI). In this study, we have acquired multiparametric MRI data sets (including absolute T1, T2, diffusion, T1ρ using continuous wave and adiabatic pulse approaches, cerebral blood flow (CBF), and amide proton transfer ratio (APTR) images) during and after 65 mins of middle cerebral artery occlusion (MCAo) in rats. The MRI scans were repeated 24 h after MCAo, when the animals were killed for quantitative histology. Magnetic resonance imaging parameters acquired at three acute time points were correlated with regionally matching cell count at 24 h. The results emphasize differences in the temporal profile of individual MRI contrasts during MCAo and especially during early reperfusion, and suggest that complementary information from CBF and tissue damage can be obtained with appropriate MRI contrasts. The data show that by using three to four MRI parameters, sensitive to both hemodynamic changes and different aspects of parenchymal changes, the fate of the tissue can be predicted with increased correlation compared with single-parameter techniques. Combined multiparametric MRI data and multiparametric analysis may provide an excellent tool for preclinical testing of new treatments and also has the potential to facilitate decision-making in the management of acute stroke patients.
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Kirlik, Gokhan, Rao Gullapalli, Warren D’Souza, Gazi Md Daud Iqbal, Michael Naslund, Jade Wong, John Papadimitrou, Steve Roys, Nilesh Mistry, and Hao Zhang. "A Supervised Learning Tool for Prostate Cancer Foci Detection and Aggressiveness Identification using Multiparametric magnetic resonance imaging/magnetic resonance spectroscopy imaging." Cancer Informatics 17 (January 1, 2018): 117693511878626. http://dx.doi.org/10.1177/1176935118786260.
Abstract:
Prostate cancer is the most frequently diagnosed cancer in men in the United States. The current main methods for diagnosing prostate cancer include prostate-specific antigen test and transrectal biopsy. Prostate-specific antigen screening has been criticized for overdiagnosis and unnecessary treatment, and transrectal biopsy is an invasive procedure with low sensitivity for diagnosis. We provided a quantitative tool using supervised learning with multiparametric imaging to be able to accurately detect cancer foci and its aggressiveness. A total of 223 specimens from patients who received magnetic resonance imaging (MRI) and magnetic resonance spectroscopy imaging prior to the surgery were studied. Multiparametric imaging included extracting T2-map, apparent diffusion coefficient (ADC) using diffusion-weighted MRI, [Formula: see text] using dynamic contrast-enhanced MRI, and 3-dimensional-MR spectroscopy. A pathologist reviewed all 223 specimens and marked cancerous regions on each and graded them with Gleason scores, which served as the ground truth to validate our prediction model. In cancer aggressiveness prediction, the average area under the receiver operating characteristic curve (AUC) value was 0.73 with 95% confidence interval (0.72-0.74) and the average sensitivity and specificity were 0.72 (0.71-0.73) and 0.73 (0.71-0.75), respectively. For the cancer detection model, the average AUC value was 0.68 (0.66-0.70) and the average sensitivity and specificity were 0.73 (0.70-0.77) and 0.62 (0.60-0.68), respectively. Our method included capability to handle class imbalance using adaptive boosting with random undersampling. In addition, our method was noninvasive and allowed for nonsubjective disease characterization, which provided physician information to make personalized treatment decision.
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Corradini, Daniele, Leonardo Brizi, Caterina Gaudiano, Lorenzo Bianchi, Emanuela Marcelli, Rita Golfieri, Riccardo Schiavina, Claudia Testa, and Daniel Remondini. "Challenges in the Use of Artificial Intelligence for Prostate Cancer Diagnosis from Multiparametric Imaging Data." Cancers 13, no. 16 (August 5, 2021): 3944. http://dx.doi.org/10.3390/cancers13163944.
Abstract:
Many efforts have been carried out for the standardization of multiparametric Magnetic Resonance (mp-MR) images evaluation to detect Prostate Cancer (PCa), and specifically to differentiate levels of aggressiveness, a crucial aspect for clinical decision-making. Prostate Imaging—Reporting and Data System (PI-RADS) has contributed noteworthily to this aim. Nevertheless, as pointed out by the European Association of Urology (EAU 2020), the PI-RADS still has limitations mainly due to the moderate inter-reader reproducibility of mp-MRI. In recent years, many aspects in the diagnosis of cancer have taken advantage of the use of Artificial Intelligence (AI) such as detection, segmentation of organs and/or lesions, and characterization. Here a focus on AI as a potentially important tool for the aim of standardization and reproducibility in the characterization of PCa by mp-MRI is reported. AI includes methods such as Machine Learning and Deep learning techniques that have shown to be successful in classifying mp-MR images, with similar performances obtained by radiologists. Nevertheless, they perform differently depending on the acquisition system and protocol used. Besides, these methods need a large number of samples that cover most of the variability of the lesion aspect and zone to avoid overfitting. The use of publicly available datasets could improve AI performance to achieve a higher level of generalizability, exploiting large numbers of cases and a big range of variability in the images. Here we explore the promise and the advantages, as well as emphasizing the pitfall and the warnings, outlined in some recent studies that attempted to classify clinically significant PCa and indolent lesions using AI methods. Specifically, we focus on the overfitting issue due to the scarcity of data and the lack of standardization and reproducibility in every step of the mp-MR image acquisition and the classifier implementation. In the end, we point out that a solution can be found in the use of publicly available datasets, whose usage has already been promoted by some important initiatives. Our future perspective is that AI models may become reliable tools for clinicians in PCa diagnosis, reducing inter-observer variability and evaluation time.
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Nelissen, Jules L., Willeke A. Traa, Hans H. de Boer, Larry de Graaf, Valentina Mazzoli, C. Dilara Savci-Heijink, Klaas Nicolay, et al. "An advanced magnetic resonance imaging perspective on the etiology of deep tissue injury." Journal of Applied Physiology 124, no. 6 (June 1, 2018): 1580–96. http://dx.doi.org/10.1152/japplphysiol.00891.2017.
Abstract:
Early diagnosis of deep tissue injury remains problematic due to the complicated and multifactorial nature of damage induction and the many processes involved in damage development and recovery. In this paper, we present a comprehensive assessment of deep tissue injury development and remodeling in a rat model by multiparametric magnetic resonance imaging (MRI) and histopathology. The tibialis anterior muscle of rats was subjected to mechanical deformation for 2 h. Multiparametric in vivo MRI, consisting of T2, T2*, mean diffusivity (MD), and angiography measurements, was applied before, during, and directly after indentation as well as at several time points during a 14-day follow-up. MRI readouts were linked to histological analyses of the damaged tissue. The results showed dynamic change in various MRI parameters, reflecting the histopathological status of the tissue during damage induction and repair. Increased T2 corresponded with edema, muscle cell damage, and inflammation. T2* was related to tissue perfusion, hemorrhage, and inflammation. MD increase and decrease was reported on the tissue’s microstructural integrity and reflected muscle degeneration and edema as well as fibrosis. Angiography provided information on blockage of blood flow during deformation. Our results indicate that the effects of a single damage-causing event of only 2 h of deformation were present up to 14 days. The initial tissue response to deformation, as observed by MRI, starts at the edge of the indentation. The quantitative MRI readouts provided distinct and complementary information on the extent, temporal evolution, and microstructural basis of deep tissue injury-related muscle damage. NEW & NOTEWORTHY We have applied a multiparametric MRI approach linked to histopathology to characterize damage development and remodeling in a rat model of deep tissue injury. Our approach provided several relevant insights in deep tissue injury. Response to damage, as observed by MRI, started at some distance from the deformation. Damage after a single indentation period persisted up to 14 days. The MRI parameters provided distinct and complementary information on the microstructural basis of the damage.
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Ferenc, Thomas, Jelena Popić, and Vinko Vidjak. "Magnetic resonance imaging in the diagnosis of malignant gynaecological tumours." Medicina Fluminensis 58, no. 2 (June 1, 2022): 100–112. http://dx.doi.org/10.21860/medflum2022_275127.
Abstract:
Magnetic resonance imaging (MRI) is a widely used imaging modality that depicts detailed information regarding morphological and functional characteristics of the human body. It is routinely used in gynaecologic oncology for female pelvis imaging because of the high spatial and soft-tissue contrast resolution. Furthermore, MRI is an important diagnostic tool for the assessment of common gynaecological malignancies - endometrial carcinoma, cervical carcinoma and malignant ovarian tumours. Novel technical developments enabled the multiparametric MRI approach in the diagnosis of respective tumours combining T1-weighted (T1W) sequences, T2-weighted (T2W) sequences, diffusion-weighted (DW) sequences with apparent diffusion coefficient (ADC) values and dynamic contrast-enhanced (DCE) sequences. With highlighted novelties, MRI importance ranges from tumour detection to treatment response monitoring and early recurrent disease evaluation. This review discusses the value of MRI in the diagnostic assessment of the common gynaecological malignancies with an¬¬ emphasis on tumour staging.
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Tarachkova, Е. V., E. V. Nikolaev, М. A. Shorikov, V. О. Panov, and I. Е. Tyurin. "Estimation of the Extent of Cervical Cancer Using Multiparametric Magnetic Resonance Imaging." Journal of radiology and nuclear medicine 100, no. 5 (November 4, 2019): 298–303. http://dx.doi.org/10.20862/0042-4676-2019-100-5-298-303.
Abstract:
Objective. To increase the efficiency of diagnosis and estimation of the local extent of a tumor process in cervical cancer (CC) using all modalities of multiparametric magnetic resonance imaging (mpMRI).Subjects and methods. Examinations were made in 31 patients (mean age 45±11 years) with histologically verified minimally invasive CC, who underwent surgical treatment. The investigators used the following modalities: T2 weighted imaging (T2WI); T2WI with fat signal suppression; diffusion-weighted image (DWI) with apparent diffusion coefficient (ADC) mapping; T1WI with dynamic contrast-enhanced MRI (DCE-MRI).Results. The measured distances significantly differed from the true ones obtained from the morphological findings (p<0.05). With allowance made for the built linear regression models, the investigators generated correction formulas. The best modality of MRI in establishing the presence of parametrial invasion in CC and in measuring the actual depth of invasion was T1WI with DCE-MRI (using the images obtained 100–125 seconds after MRI contrast medium administration); the slightly worse modality was DWI with ADC mapping (with a specificity of 91%, the sensitivities of DCE-MRI and DWI with ADC mapping were 95% and 90%, respectively), and T2WI with and without fat signal suppression.Conclusion. Quantitative analysis of the extent of CC in the parametrium according to the results of complex mpMRI seems to be a possible and highly accurate method.
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Garteiser, Philippe, Benjamin Leporq, Pierre-Emmanuel Rautou, Dominique Valla, and Bernard Van Beers. "Quantitative Imaging in Diffuse Liver Diseases." Seminars in Liver Disease 37, no. 03 (August 2017): 243–58. http://dx.doi.org/10.1055/s-0037-1603651.
Abstract:
AbstractCross-sectional imaging methods and more specifically ultrasonography and magnetic resonance imaging (MRI), have increasing roles in the quantitative evaluation of diffuse liver diseases. Particularly, ultrasound elastography is becoming the standard first-line examination for diagnosing severe liver fibrosis. Quantitative ultrasonography also brings information for staging portal hypertension in compensated cirrhosis and for grading liver steatosis. Quantitative MRI offers a multiparametric approach to assess the severity of liver steatosis, iron overload, fibrosis, inflammation, and portal hypertension. Regional liver transport function can be assessed with combined volumetric computed tomography and 99Tc mebrofenin single-photon emission computed tomography or with gadoxetic acid-enhanced MRI. It is concluded that multiparametric MRI complements the information brought with quantitative ultrasonography and has the potential to become a method of virtual liver biopsy that may decrease the need for invasive reference examinations in diffuse liver diseases.
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Sarkar, Saradwata, and Sudipta Das. "A Review of Imaging Methods for Prostate Cancer Detection." Biomedical Engineering and Computational Biology 7s1 (January 2016): BECB.S34255. http://dx.doi.org/10.4137/becb.s34255.
Abstract:
Imaging is playing an increasingly important role in the detection of prostate cancer (PCa). This review summarizes the key imaging modalities–multiparametric ultrasound (US), multiparametric magnetic resonance imaging (MRI), MRI-US fusion imaging, and positron emission tomography (PET) imaging–-used in the diagnosis and localization of PCa. Emphasis is laid on the biological and functional characteristics of tumors that rationalize the use of a specific imaging technique. Changes to anatomical architecture of tissue can be detected by anatomical grayscale US and T2-weighted MRI. Tumors are known to progress through angiogenesis–-a fact exploited by Doppler and contrast-enhanced US and dynamic contrast-enhanced MRI. The increased cellular density of tumors is targeted by elastography and diffusion-weighted MRI. PET imaging employs several different radionuclides to target the metabolic and cellular activities during tumor growth. Results from studies using these various imaging techniques are discussed and compared.
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Lopes Dias, João, João Magalhães Pina, Nuno Vasco Costa, Sandra Carmo, Cecília Leal, Tiago Bilhim, Rui Mateus Marques, and Luís Campos Pinheiro. "The utility of apparent diffusion coefficient values in the risk stratification of prostate cancer using a 1.5T magnetic resonance imaging without endorectal coil." Acta Urológica Portuguesa 33, no. 3 (April 10, 2017): 81–86. http://dx.doi.org/10.24915/aup.33.3.33.
Abstract:
PurposeTo evaluate the relationship between mean apparent diffusion coefficient (ADC) and post-surgical Gleason scores. To determine the diagnostic accuracy of multiparametric magnetic resonance imaging (mp-MRI) on a 1.5T magnet in distinguishing low, intermediate and high-grade prostate tumors.Material and methodsThis is a retrospective institutional-review-board-approved, single-center study including 30 patients (median age, 60 years) who underwent mp-MRI before prostatectomy for prostate cancer. Using histological reports for guidance, the tumors were localized in ADC maps, and mean ADCs were measured and examined for correlation with Gleason scores. 2 patients had 2 measurable foci, so a total of 32 tumors were studied. The diagnostic accuracy of the mean ADC was assessed by using the area under the receiver operating characteristic curve (ROC).ResultsIn the differentiation of tumors with a Gleason score of 6 from those with a Gleason score of at least 7, mean ADC yielded an AUC of 0.76 (95% confidence interval: 0.59, 0.93). In the differentiation of tumors with Gleason scores of 6 or 7 from those with a Gleason score of at least 8, mean ADC yielded an AUC of 0.94 (95% confidence interval: 0.86, 1.00).ConclusionMean ADC values may allow a correct assessment of the patient risk using a 1.5T magnet without ERC.
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Damascelli, Anna, Francesca Gallivanone, Giulia Cristel, Claudia Cava, Matteo Interlenghi, Antonio Esposito, Giorgio Brembilla, et al. "Advanced Imaging Analysis in Prostate MRI: Building a Radiomic Signature to Predict Tumor Aggressiveness." Diagnostics 11, no. 4 (March 26, 2021): 594. http://dx.doi.org/10.3390/diagnostics11040594.
Abstract:
Radiomics allows the extraction quantitative features from imaging, as imaging biomarkers of disease. The objective of this exploratory study is to implement a reproducible radiomic-pipeline for the extraction of a magnetic resonance imaging (MRI) signature for prostate cancer (PCa) aggressiveness. One hundred and two consecutive patients performing preoperative prostate multiparametric magnetic resonance imaging (mpMRI) and radical prostatectomy were enrolled. Multiparametric images, including T2-weighted (T2w), diffusion-weighted and dynamic contrast-enhanced images, were acquired at 1.5 T. Ninety-three imaging features (Ifs) were extracted from segmentation of index lesion. Ifs were ranked based on a stability rank and redundant Ifs were excluded. Using unsupervised hierarchical clustering, patients were grouped on the basis of similar radiomic patterns, whose association with Gleason Grade Group (GGG), extracapsular extension (ECE), and nodal involvement (pN) was tested. Signatures composed by IFs from T2w-images and Apparent Diffusion Coefficient (ADC) maps were tested for the prediction of GGG, ECE, and pN. T2w radiomic pattern was associated with pN, ECE, and GGG (p = 0.027, 0.05, 0.03) and ADC radiomic pattern was associated with GGG (p = 0.004). The best performance was reached by the signature combing IFs from multiparametric images (0.88, 0.89, and 0.84 accuracy for GGG, pN, and ECE). A reliable multiparametric MRI radiomic signature was extracted, potentially able to predict PCa aggressiveness, to be further validated on an independent sample.