Purpose: To assess risk-group migration and subsequent management change following biparametric MRI using a risk-stratified approach in a group of Caribbean men with prostate adenocarcinoma being offered external beam radiation therapy (EBRT).
Materials And Methods: This retrospective study assessed the biparametric MRI findings in men who opted for EBRT from January 2018 to June 2019 ( = 79; mean age, 67.9 years ± 6.2 [standard deviation]). Serum prostate-specific antigen level, digital rectal examination findings, histologic grade group (GG) from transrectal US-guided biopsy, prior androgen deprivation therapy (ADT), and any prior CT results were used to stratify patients into low-, intermediate-, and high-risk groups, according to the National Comprehensive Cancer Network risk categories. Risk-group stratification prior to MRI separated patients into low- (seven of 79 [8.9%]), intermediate- (36 of 79 [45.6%]) and high-risk (36 of 79 [45.6%]) groups. Following MRI, any risk group (low, intermediate, high, nodal involvement, and metastatic disease) or oncologic management changes were recorded. Multivariable binary logistic regression analyses were used to assess predictor of upgrade status, with adjustment for demographic covariates jointly.
Results: Following MRI, 30 of 79 (38.0%) patients had risk-group upshifts compared with their original assessment. Patients were recategorized into low risk (one of 79, 1.3%), intermediate risk (19 of 79, 24.1%), high risk (51 of 79, 64.6%), nodal involvement (one of 79, 1.3%), and metastatic disease (seven of 79, 8.9%). From the original groupings, there were six of seven (85.7%) from the low group, 18 of 36 (50.0%) from the intermediate group, and six of 36 (16.7%) from the high group that had risk group upward shifts. There was no association with GG: GG2 versus GG1, = .53; GG3 versus GG1, = .98; or prior ADT ( = .37) and the adjusted odds of risk-group upshifts. MRI findings resulted in treatment plan modification for 39 of 79 (49.4%) men overall.
Conclusion: Prostate MRI should be considered for patients in high-risk populations prior to EBRT because upstaging from MR image assessment may have implications for modification of treatment. MR-Imaging, Prostate, Radiation Therapy© RSNA, 2020See the commentary by Davenport and Shankar in this issue.
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http://dx.doi.org/10.1148/rycan.2020200007 | DOI Listing |
Insights Imaging
January 2025
Department of Radiology, the Second Affiliated Hospital of Dalian Medical University, Dalian, 116023, China.
Objective: To evaluate the feasibility of utilizing artificial intelligence (AI)-predicted biparametric MRI (bpMRI) image features for predicting the aggressiveness of prostate cancer (PCa).
Materials And Methods: A total of 878 PCa patients from 4 hospitals were retrospectively collected, all of whom had pathological results after radical prostatectomy (RP). A pre-trained AI algorithm was used to select suspected PCa lesions and extract lesion features for model development.
Eur Urol Open Sci
January 2025
Department of Radiation Medicine and Applied Sciences, University of California San Diego School of Medicine, La Jolla, CA, USA.
Multiparametric magnetic resonance imaging (mpMRI) is strongly recommended by current clinical guidelines for improved detection of clinically significant prostate cancer (csPCa). However, the major limitations are the need for intravenous (IV) contrast and dependence on reader expertise. Efforts to address these issues include use of biparametric magnetic resonance imaging (bpMRI) and advanced, quantitative magnetic resonance imaging (MRI) techniques.
View Article and Find Full Text PDFRadiology
January 2025
From the Department of Radiology, Shenzhen Nanshan People's Hospital, Shenzhen University, Taoyuan Rd No. 89, Nanshan District, Shenzhen 518000, Guangdong, China (H.H., Z.D., Y.Q.); Medical AI Laboratory and Guangdong Key Laboratory of Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China (J.M., R.L., B.H.); Department of Medical Imaging, People's Hospital of Longhua, Shenzhen, Guangdong, China (X.P., Y.Z.); and Department of Radiology, Shenzhen People's Hospital, Shenzhen, Guangdong, China (D.Z., G.H.).
Background Multiparametric MRI, including contrast-enhanced sequences, is recommended for evaluating suspected prostate cancer, but concerns have been raised regarding potential contrast agent accumulation and toxicity. Purpose To evaluate the feasibility of generating simulated contrast-enhanced MRI from noncontrast MRI sequences using deep learning and to explore their potential value for assessing clinically significant prostate cancer using Prostate Imaging Reporting and Data System (PI-RADS) version 2.1.
View Article and Find Full Text PDFEur Radiol
January 2025
Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
Background: Chronic liver disease (CLD) is a substantial cause of morbidity and mortality worldwide. Liver stiffness, as measured by MR elastography (MRE), is well-accepted as a surrogate marker of liver fibrosis.
Purpose: To develop and validate deep learning (DL) models for predicting MRE-derived liver stiffness using routine clinical non-contrast abdominal T1-weighted (T1w) and T2-weighted (T2w) data from multiple institutions/system manufacturers in pediatric and adult patients.
Sci Rep
January 2025
Department of MRI, Zhongshan City People's Hospital, No. 2, Sunwen East Road, Shiqi District, Zhongshan, 528403, Guangdong, China.
To investigate the potential of an MRI-based radiomic model in distinguishing malignant prostate cancer (PCa) nodules from benign prostatic hyperplasia (BPH)-, as well as determining the incremental value of radiomic features to clinical variables, such as prostate-specific antigen (PSA) level and Prostate Imaging Reporting and Data System (PI-RADS) score. A restrospective analysis was performed on a total of 251 patients (training cohort, n = 119; internal validation cohort, n = 52; and external validation cohort, n = 80) with prostatic nodules who underwent biparametric MRI at two hospitals between January 2018 and December 2020. A total of 1130 radiomic features were extracted from each MRI sequence, including shape-based features, gray-level histogram-based features, texture features, and wavelet features.
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