ArticleScientific reports2019
Objective risk stratification of prostate cancer using machine learning and radiomics applied to multiparametric magnetic resonance images.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05911412 (Development of the Neuroimaging Biomarker-based Prediction Model of Anxiety-related Disorders and Novel Treatment Strategies), which is not on this map. Cited by 56 papers, 1 of them a synthesis that pooled it.
What it found
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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.
Development of the Neuroimaging Biomarker-based Prediction Model of Anxiety-related Disorders and Novel Treatment Strategies: Effect of Mindfulness-based Cognitive Therapy Using Neuroscience on the Brain
Who cites it
56 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning-based MRI imaging for prostate cancer diagnosis: systematic review and meta-analysis.Prostate cancer and prostatic diseases · 2026Pooled it
- Risk stratification for 1- to 2-cm gastric gastrointestinal stromal tumors: visual assessment of CT and EUS high-risk features versus CT radiomics analysis.European radiology · 2023Trial
- Biomarkers for Precision Prognosis in Prostate Cancer: Imaging, Molecular, and Integrated Approaches.Cancers · 2026Review
- Radiomics and Back Pain.Global spine journal · 2026Review
- Artificial Intelligence Across the Prostate Cancer Pathway: Screening, Imaging, Pathology, and Biomarkers.Cureus · 2025Review
- Advancements in artificial intelligence for prostate cancer: Optimizing diagnosis, treatment, and prognostic assessment.Asian journal of urology · 2025Review
- Review
- The Role of Radiomic Analysis and Different Machine Learning Models in Prostate Cancer Diagnosis.Journal of imaging · 2025Article
- Article
- Externally validated and clinically useful machine learning algorithms to support patient-related decision-making in oncology: a scoping review.BMC medical research methodology · 2025Article
- Role of AI in empowering and redefining the oncology care landscape: perspective from a developing nation.Frontiers in digital health · 2025Review
- Multimodal MRI radiomics based on habitat subregions of the tumor microenvironment for predicting risk stratification in glioblastoma.PloS one · 2025Article
- Epigenetic profiling of prostate cancer reveals potential prognostic signatures.Journal of cancer research and clinical oncology · 2024Article
- Radiomic Pipelines for Prostate Cancer in External Beam Radiation Therapy: A Review of Methods and Future Directions.Journal of clinical medicine · 2024Review
- Article
- A primer on the use of machine learning to distil knowledge from data in biological psychiatry.Molecular psychiatry · 2024Review
- Risk score model to automatically detect prostate cancer patients by integrating diagnostic parameters.Frontiers in oncology · 2024Article
- Computed Tomography-Based Radiomics for Long-Term Prognostication of High-Risk Localized Prostate Cancer Patients Received Whole Pelvic Radiotherapy.Journal of personalized medicine · 2023Article
- T2-Weighted MRI Radiomic Features Predict Prostate Cancer Presence and Eventual Biochemical Recurrence.Cancers · 2023Article
- MRI-Based Surrogate Imaging Markers of Aggressiveness in Prostate Cancer: Development of a Machine Learning Model Based on Radiomic Features.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Multiparametric magnetic resonance imaging (mpMRI) has become increasingly important for the clinical assessment of prostate cancer (PCa), but its interpretation is generally variable due to its relatively subjective nature. Radiomics and classification methods have shown potential for improving the accuracy and objectivity of mpMRI-based PCa assessment. However, these studies are limited to a small number of classification methods, evaluation using the AUC score only, and a non-rigorous assessment of all possible combinations of radiomics and classification methods. This paper presents a systematic and rigorous framework comprised of classification, cross-validation and statistical analyses that was developed to identify the best performing classifier for PCa risk stratification based on mpMRI-derived radiomic features derived from a sizeable cohort. This classifier performed well in an independent validation set, including performing better than PI-RADS v2 in some aspects, indicating the value of objectively interpreting mpMRI images using radiomics and classification methods for PCa risk assessment.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.