ArticleDiagnostics (Basel, Switzerland)2023
MRI-Based Surrogate Imaging Markers of Aggressiveness in Prostate Cancer: Development of a Machine Learning Model Based on Radiomic Features.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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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.
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Who cites it
11 citing papers in PubMed, 10 citations in OpenAlex.
- Article
- PI-RADS v2.1 for prostate MRI: current applications, ongoing debates, and synergies with advanced technologies-a narrative review.Translational andrology and urology · 2026Review
- Dissecting self-supervised learning strategies for transfer learning in MRI prostate cancer diagnosis.Scientific reports · 2026Article
- The Role of Radiomic Analysis and Different Machine Learning Models in Prostate Cancer Diagnosis.Journal of imaging · 2025Article
- Combining multi-parametric MRI radiomics features with tumor abnormal protein to construct a machine learning-based predictive model for prostate cancer.Scientific reports · 2025Article
- Integrating radiological and clinical data for clinically significant prostate cancer detection with machine learning techniques.Scientific reports · 2025Article
- Editorial for Special Topics: Imaging-Based Diagnosis for Prostate Cancer-State of the Art.Diagnostics (Basel, Switzerland) · 2024Article
- Review
- Radiomic Pipelines for Prostate Cancer in External Beam Radiation Therapy: A Review of Methods and Future Directions.Journal of clinical medicine · 2024Review
- Relationship between Eccentricity and Volume Determined by Spectral Algorithms Applied to Spatially Registered Bi-Parametric MRI and Prostate Tumor Aggressiveness: A Pilot Study.Diagnostics (Basel, Switzerland) · 2023Article
- Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 2 countries.
Funding
Abstract
This study aimed to develop a noninvasive Machine Learning (ML) model to identify clinically significant prostate cancer (csPCa) according to Gleason Score (GS) based on biparametric MRI (bpMRI) radiomic features and clinical information.
methodsThis retrospective study included 86 adult Hispanic men (60 ± 8.2 years, median prostate-specific antigen density (PSA-D) 0.15 ng/mL
resultsRadiomic features derived from T2WI/ADC are associated with GS in patients with PCa. The best model found was multivariate, including image (T2WI/ADC) and clinical (PV and PSA-D) information. The validation area under the curve (AUC) was 0.80 for differentiating csPCa from non-csPCa, exhibiting better performance than PI-RADS (AUC: 0.71) and PSA-D (AUC: 0.78).
conclusionOur multivariate ML model outperforms PI-RADS v2.1 and established clinical indicators like PSA-D in classifying csPCa accurately. This underscores MRI-derived radiomics' (T2WI/ADC) potential as a robust biomarker for assessing PCa aggressiveness in Hispanic patients.
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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.