ArticleJournal of medical imaging (Bellingham, Wash.)2018
PROSTATEx Challenges for computerized classification of prostate lesions from multiparametric magnetic resonance images.
Article in Journal of medical imaging (Bellingham, Wash.), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 74 papers.
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Who cites it
74 citing papers in PubMed.
- Deep learning for fully automatic detection, segmentation, and Gleason grade estimation of prostate cancer in multiparametric magnetic resonance images.Scientific reports · 2022Trial
- Real-world assessment of a deep learning neural network algorithm for prostate cancer detection in MRI using true de novo data.Medical physics · 2026Article
- Enhancing PET/CT Radiomics Robustness Through Graph Signal Processing.Diagnostics (Basel, Switzerland) · 2026Article
- Shifting from black box decisions to informed decision-making in using artificial intelligence to analyze prostate MRI.Abdominal radiology (New York) · 2026Review
- Few-Shot Learning for Prostate Cancer Detection on MRI: Comparative Analysis with Radiologists' Performance.Journal of imaging informatics in medicine · 2026Article
- SynSAM: a hybrid synchronous learning framework with knowledge retention for prostate zonal segmentation leveraging the segment anything model.Medical & biological engineering & computing · 2026Article
- Article
- mpMRI-Based Risk Estimation to Optimize Prostate Cancer Patient Selection for Active Surveillance.Cancers · 2026Article
- External evaluation of an open-source deep learning model for prostate cancer detection on bi-parametric MRI.European radiology · 2026Article
- An investigation of race bias in deep learning-based segmentation of prostate MRI images.Scientific reports · 2025Article
- Deep learning for multi-modal medical image segmentation: a survey and comparative study.Brain imaging and behavior · 2025Article
- Radiomics Reproducibility in Prostate Cancer Diagnosis Based on PROSTATEx.International neurourology journal · 2025Article
- Beyond Radiomics Alone: Enhancing Prostate Cancer Classification with ADC Ratio in a Multicenter Benchmarking Study.Diagnostics (Basel, Switzerland) · 2025Article
- Artificial Intelligence in Prostate MRI: Current Evidence and Clinical Translation Challenges-A Narrative Review.Journal of imaging · 2025Review
- Artificial Intelligence-Assisted Biparametric MRI for Detecting Prostate Cancer-A Comparative Multireader Multicase Accuracy Study.Journal of clinical medicine · 2025Article
- MRI super-resolution reconstruction using efficient diffusion probabilistic model with residual shifting.Physics in medicine and biology · 2025Article
- A Narrative Review of Artificial Intelligence in MRI-Guided Prostate Cancer Diagnosis: Addressing Key Challenges.Diagnostics (Basel, Switzerland) · 2025Review
- Effective reduction of unnecessary biopsies through a deep-learning-assisted aggressive prostate cancer detector.Scientific reports · 2025Article
- Assessing Cancer Presence in Prostate MRI Using Multi-Encoder Cross-Attention Networks.Journal of imaging · 2025Article
- Optimizing radiomics for prostate cancer diagnosis: feature selection strategies, machine learning classifiers, and MRI sequences.Insights into imaging · 2024Article
14 more citing papers are in PubMed but not listed here.
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Authors and funding
12 authors.
Funding
Abstract
Grand challenges stimulate advances within the medical imaging research community; within a competitive yet friendly environment, they allow for a direct comparison of algorithms through a well-defined, centralized infrastructure. The tasks of the two-part PROSTATEx Challenges (the PROSTATEx Challenge and the PROSTATEx-2 Challenge) are (1) the computerized classification of clinically significant prostate lesions and (2) the computerized determination of Gleason Grade Group in prostate cancer, both based on multiparametric magnetic resonance images. The challenges incorporate well-vetted cases for training and testing, a centralized performance assessment process to evaluate results, and an established infrastructure for case dissemination, communication, and result submission. In the PROSTATEx Challenge, 32 groups apply their computerized methods (71 methods total) to 208 prostate lesions in the test set. The area under the receiver operating characteristic curve for these methods in the task of differentiating between lesions that are and are not clinically significant ranged from 0.45 to 0.87; statistically significant differences in performance among the top-performing methods, however, are not observed. In the PROSTATEx-2 Challenge, 21 groups apply their computerized methods (43 methods total) to 70 prostate lesions in the test set. When compared with the reference standard, the quadratic-weighted kappa values for these methods in the task of assigning a five-point Gleason Grade Group to each lesion range from
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