ArticleTomography (Ann Arbor, Mich.)2019
Gleason Probability Maps: A Radiomics Tool for Mapping Prostate Cancer Likelihood in MRI Space.
Article in Tomography (Ann Arbor, Mich.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
34 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Radiomics in prostate cancer imaging for a personalized treatment approach - current aspects of methodology and a systematic review on validated studies.Theranostics · 2021Pooled it
- Radio-pathomic maps of histo-morphometric features trained with whole mount prostate histology distinguish prostate cancer on MP-MRI.medRxiv : the preprint server for health sciences · 2026Article
- Machine learning-based construction and validation of an radiomics model for predicting ISUP grading in prostate cancer: a multicenter radiomics study based on [68Ga]Ga-PSMA PET/CT.European journal of nuclear medicine and molecular imaging · 2025Article
- The Role of Radiomic Analysis and Different Machine Learning Models in Prostate Cancer Diagnosis.Journal of imaging · 2025Article
- MRI-based virtual pathology of the prostate.Magma (New York, N.Y.) · 2024Review
- Artificial Intelligence Improves the Ability of Physicians to Identify Prostate Cancer Extent.The Journal of urology · 2024Article
- RAPHIA: A deep learning pipeline for the registration of MRI and whole-mount histopathology images of the prostate.Computers in biology and medicine · 2024Article
- Detecting MRI-Invisible Prostate Cancers Using a Weakly Supervised Deep Learning Model.International journal of biomedical imaging · 2024Article
- Quantitative Histomorphometric Features of Prostate Cancer Predict Patients Who Biochemically Recur Following Prostatectomy.Laboratory investigation; a journal of technical methods and pathology · 2023Article
- T2-Weighted MRI Radiomic Features Predict Prostate Cancer Presence and Eventual Biochemical Recurrence.Cancers · 2023Article
- Prediction and Mapping of Intraprostatic Tumor Extent with Artificial Intelligence.European urology open science · 2023Article
- The Use of MRI-Derived Radiomic Models in Prostate Cancer Risk Stratification: A Critical Review of Contemporary Literature.Diagnostics (Basel, Switzerland) · 2023Review
- Comparison of a machine and deep learning model for automated tumor annotation on digitized whole slide prostate cancer histology.PloS one · 2023Article
- Multi-view radiomics and deep learning modeling for prostate cancer detection based on multi-parametric MRI.Frontiers in oncology · 2023Article
- Bridging the gap between prostate radiology and pathology through machine learning.Medical physics · 2022Article
- Multi-Site Concordance of Diffusion-Weighted Imaging Quantification for Assessing Prostate Cancer Aggressiveness.Journal of magnetic resonance imaging : JMRI · 2022Article
- Radio-Pathomic Maps of Cell Density Identify Brain Tumor Invasion beyond Traditional MRI-Defined Margins.AJNR. American journal of neuroradiology · 2022Article
- Tumor Connectomics: Mapping the Intra-Tumoral Complex Interaction Network Using Machine Learning.Cancers · 2022Article
- Diffusion Restriction Comparison between Gleason 4 Fused Glands and Cribriform Glands within Patient Using Whole-Mount Prostate Pathology as Ground Truth.Tomography (Ann Arbor, Mich.) · 2022Article
- Homologous point transformer for multi-modality prostate image registration.PeerJ. Computer science · 2022Article
Corrections and comments
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Authors and funding
17 authors.
Funding
Abstract
Prostate cancer is the most common noncutaneous cancer in men in the United States. The current paradigm for screening and diagnosis is imperfect, with relatively low specificity, high cost, and high morbidity. This study aims to generate new image contrasts by learning a distribution of unique image signatures associated with prostate cancer. In total, 48 patients were prospectively recruited for this institutional review board-approved study. Patients underwent multiparametric magnetic resonance imaging 2 weeks before surgery. Postsurgical tissues were annotated by a pathologist and aligned to the in vivo imaging. Radiomic profiles were generated by linearly combining 4 image contrasts (T2, apparent diffusion coefficient [ADC] 0-1000, ADC 50-2000, and dynamic contrast-enhanced) segmented using global thresholds. The distribution of radiomic profiles in high-grade cancer, low-grade cancer, and normal tissues was recorded, and the generated probability values were applied to a naive test set. The resulting Gleason probability maps were stable regardless of training cohort, functioned independent of prostate zone, and outperformed conventional clinical imaging (area under the curve [AUC] = 0.79). Extensive overlap was seen in the most common image signatures associated with high- and low-grade cancer, indicating that low- and high-grade tumors present similarly on conventional imaging.
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Registered trials
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