ArticleJournal of magnetic resonance imaging : JMRI2018
Radiomic features from pretreatment biparametric MRI predict prostate cancer biochemical recurrence: Preliminary findings.
Article in Journal of magnetic resonance imaging : JMRI, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 91 papers, 5 of them syntheses that pooled it.
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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.
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Who cites it
91 citing papers in PubMed, 5 syntheses or guidelines pooled it, 139 citations in OpenAlex.
- Magnetic Resonance Imaging-Based Artificial Intelligence in Predicting Prostate Cancer Biochemical Recurrence: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- MRI-based radiomics for prediction of biochemical recurrence in prostate cancer: a systematic review and meta-analysis.Abdominal radiology (New York) · 2025Pooled 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
- Reproducibility analysis of multi-institutional paired expert annotations and radiomic features of the Ivy Glioblastoma Atlas Project (Ivy GAP) dataset.Medical physics · 2020Pooled it
- Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics.European journal of nuclear medicine and molecular imaging · 2019Pooled it
- Biomarkers for Precision Prognosis in Prostate Cancer: Imaging, Molecular, and Integrated Approaches.Cancers · 2026Review
- Diagnostic performance of magnetic resonance imaging-based radiomics for detecting prostate cancer: A systematic review and meta-analysis.World journal of radiology · 2026Article
- Development and Validation of a Multimodal AI-Based Model for Predicting Post-Prostatectomy Treatment Outcomes from Baseline Biparametric Prostate MRI.medRxiv : the preprint server for health sciences · 2026Article
- Multicenter Study Suggests Unsupervised Learning Derived From MRI Identifies Prognostic Subgroups in Prostate Cancer Patients After Prostatectomy.Radiology research and practice · 2026Article
- Integrating biparametric MRI radiomics with clinical variables improves pre-treatment prediction of prostate cancer recurrence.Frontiers in oncology · 2026Article
- Computed Tomography-Based Radiomics Prediction of Biochemical Failure and Distant Metastasis in Patients With High- and Very High-Risk Localized Prostate Cancer.Advances in radiation oncology · 2025Article
- Article
- AI in Prostate MRI: A Task-Based Review.Journal of the Korean Society of Radiology · 2025Review
- Precision medicine in prostate cancer: individualized treatment through radiomics, genomics, and biomarkers.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Review
- Multimodal radiomics based on fluorine-18 prostate-specific membrane antigen positron emission tomography and multiparametric magnetic resonance imaging in predicting persistent prostate-specific antigen after radical prostatectomy.Quantitative imaging in medicine and surgery · 2025Article
- Haralick Texture Analysis for Differentiating Suspicious Prostate Lesions from Normal Tissue in Low-Field MRI.Bioengineering (Basel, Switzerland) · 2025Article
- DWI-based Biologically Interpretable Radiomic Nomogram for Predicting 1-year Biochemical Recurrence after Radical Prostatectomy: A Deep Learning, Multicenter Study.Current medical imaging · 2025Article
- Magnetic Resonance Imaging Techniques for Post-Treatment Evaluation After External Beam Radiation Therapy of Prostate Cancer: Narrative Review.Clinics and practice · 2024Review
- Developmental trends and knowledge frameworks in the application of radiomics in prostate cancer: a bibliometric analysis from 2000 to 2024.Discover oncology · 2024Article
- Article
31 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
Abstract
backgroundRadiomics or computer-extracted texture features derived from MRI have been shown to help quantitatively characterize prostate cancer (PCa). Radiomics have not been explored depth in the context of predicting biochemical recurrence (BCR) of PCa. PURPOSE: To identify a set of radiomic features derived from pretreatment biparametric MRI (bpMRI) that may be predictive of PCa BCR. STUDY TYPE: Retrospective. SUBJECTS: In all, 120 PCa patients from two institutions, I SEQUENCE: 3T, T ASSESSMENT: PCa regions of interest (ROIs) on T STATISTICAL TESTS: Wilcoxon rank-sum tests with P < 0.05 were considered statistically significant. Differences in BCR-free survival at 3 years using p
resultsDistribution statistics of co-occurrence of local anisotropic gradient orientation (CoLlAGe) and Haralick features from T DATA
conclusionRadiomic features from pretreatment bpMRI can be predictive of PCa BCR after therapy and may help identify men who would benefit from adjuvant therapy. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2018;48:1626-1636.
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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.