ArticleJournal of magnetic resonance imaging : JMRI2018
Radiomic features on MRI enable risk categorization of prostate cancer patients on active surveillance: 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 62 papers, 3 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
62 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Machine learning-based MRI imaging for prostate cancer diagnosis: systematic review and meta-analysis.Prostate cancer and prostatic diseases · 2026Pooled it
- Algorithms on the rise: a machine learning-driven survey of prostate cancer literature.Frontiers in oncology · 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
- MRI-based radiomics signature for localized prostate cancer: a new clinical tool for cancer aggressiveness prediction? Sub-study of prospective phase II trial on ultra-hypofractionated radiotherapy (AIRC IG-13218).European radiology · 2021Trial
- Diagnostic performance of magnetic resonance imaging-based radiomics for detecting prostate cancer: A systematic review and meta-analysis.World journal of radiology · 2026Article
- Next-Generation Advances in Prostate Cancer Imaging and Artificial Intelligence Applications.Journal of imaging · 2025Review
- Risk of reclassification and novel tools in active surveillance.Asian journal of urology · 2025Review
- Population-Specific Radiomics From Biparametric Magnetic Resonance Imaging Improves Prostate Cancer Risk Stratification in African American Men.JU open plus · 2025Article
- Predictive impact of T2-MRI radiomics model on initial diagnosis of bone metastasis in prostate cancer patients.BMC medical imaging · 2025Article
- Extracellular Microvesicle MicroRNAs and Imaging Metrics Improve the Detection of Aggressive Prostate Cancer: A Pilot Study.Cancers · 2025Article
- Predicting early recurrence of colorectal cancer liver metastases: an integrative approach using radiomics and machine learning.Frontiers in oncology · 2025Article
- Can we predict pathology without surgery? Weighing the added value of multiparametric MRI and whole prostate radiomics in integrative machine learning models.European radiology · 2024Article
- Extracellular microvesicle microRNAs, along with imaging metrics, improve detection of aggressive prostate cancer.medRxiv : the preprint server for health sciences · 2024Article
- Non-invasively identifying candidates of active surveillance for prostate cancer using magnetic resonance imaging radiomics.Visual computing for industry, biomedicine, and art · 2024Article
- Multi-scale statistical deformation based co-registration of prostate MRI and post-surgical whole mount histopathology.Medical physics · 2024Article
- Comparison of diagnostic accuracy of radiomics parameter maps and standard reconstruction for the detection of liver lesions in computed tomography.Frontiers in oncology · 2024Article
- State of the art: radiomics and radiomics-related artificial intelligence on the road to clinical translation.BJR open · 2024Review
- Machine learning based on radiomics features combing B-mode transrectal ultrasound and contrast-enhanced ultrasound to improve peripheral zone prostate cancer detection.Abdominal radiology (New York) · 2024Article
- T2-Weighted MRI Radiomic Features Predict Prostate Cancer Presence and Eventual Biochemical Recurrence.Cancers · 2023Article
- Stability of Multi-Parametric Prostate MRI Radiomic Features to Variations in Segmentation.Journal of personalized medicine · 2023Article
2 more citing papers are in PubMed but not listed here.
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Authors and funding
19 authors.
Funding
Abstract
backgroundRadiomic analysis is defined as computationally extracting features from radiographic images for quantitatively characterizing disease patterns. There has been recent interest in examining the use of MRI for identifying prostate cancer (PCa) aggressiveness in patients on active surveillance (AS). PURPOSE: To evaluate the performance of MRI-based radiomic features in identifying the presence or absence of clinically significant PCa in AS patients. STUDY TYPE: Retrospective. SUBJECTS MODEL: MRI/TRUS (transperineal grid ultrasound) fusion-guided biopsy was performed for 56 PCa patients on AS who had undergone prebiopsy. FIELD STRENGTH/SEQUENCE: 3T, T ASSESSMENT: A pathologist histopathologically defined the presence of clinically significant disease. A radiologist manually delineated lesions on T STATISTICAL TESTS: Wilcoxon rank-sum tests with P < 0.05 considered statistically significant.
resultsSeven T DATA
conclusionRadiomic features could help in identifying the presence and absence of clinically significant disease in AS patients when PIRADS v2.0 assessment on MRI contradicted pathology findings of MRI-TRUS prostate biopsies. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.
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