ArticleDiagnostics (Basel, Switzerland)2022
Machine Learning and Clinical-Radiological Characteristics for the Classification of Prostate Cancer in PI-RADS 3 Lesions.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 28 citations in OpenAlex.
- Deep learning based on multiparametric MRI for differentiating benign from malignant PI-RADS 3 prostate lesions: a dual-center study.Scientific reports · 2026Article
- Possible Role of Diffusion-Weighted Imaging in Prediction of Prostate Cancer Grade Group Upgrading: Insights from Biopsy to Radical Prostatectomy.Medicina (Kaunas, Lithuania) · 2026Article
- Using Artificial Intelligence as a Risk Prediction Model in Patients with Equivocal Multiparametric Prostate MRI Findings.Cancers · 2025Review
- Clinical Predictors and Risk Factors of Gleason Score Upgrade: A Retrospective Cohort Analysis.Diagnostics (Basel, Switzerland) · 2025Article
- Impact of Nerve-Sparing Techniques on Prostate-Specific Antigen Persistence Following Robot-Assisted Radical Prostatectomy: A Multivariable Analysis of Clinical and Pathological Predictors.Diagnostics (Basel, Switzerland) · 2025Article
- Management Strategy for Prostate Imaging Reporting and Data System Category 3 Lesions.Current urology reports · 2023Review
- Emerging Role of Nuclear Medicine in Prostate Cancer: Current State and Future Perspectives.Cancers · 2023Review
- Reliability and Efficiency of the CAPRI-3 Metastatic Prostate Cancer Registry Driven by Artificial Intelligence.Cancers · 2023Article
- Incidence and Predicting Factors of Histopathological Features at Robot-Assisted Radical Prostatectomy in the mpMRI Era: Results of a Single Tertiary Referral Center.Medicina (Kaunas, Lithuania) · 2023Article
- Diagnostic Performance Evaluation of Multiparametric Magnetic Resonance Imaging in the Detection of Prostate Cancer with Supervised Machine Learning Methods.Diagnostics (Basel, Switzerland) · 2023Review
- Underlying Features of Prostate Cancer-Statistics, Risk Factors, and Emerging Methods for Its Diagnosis.Current oncology (Toronto, Ont.) · 2023Review
- Biomarkers of Aggressive Prostate Cancer at Diagnosis.International journal of molecular sciences · 2023Review
- Recognition of Postoperative Cystography Features by Artificial Intelligence to Predict Recovery from Postprostatectomy Urinary Incontinence: A Rapid and Easy Way to Predict Functional Outcome.Journal of personalized medicine · 2023Article
- Comparative Outcomes of Robotic Radical Prostatectomy in Patients with Locally Advanced Prostate Cancer.Medicina (Kaunas, Lithuania) · 2022Article
- Review
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Authors and funding
17 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Prostate Imaging Reporting and Data System (PI-RADS) classification is based on a scale of values from 1 to 5. The value is assigned according to the probability that a finding is a malignant tumor (prostate carcinoma) and is calculated by evaluating the signal behavior in morphological, diffusion, and post-contrastographic sequences. A PI-RADS score of 3 is recognized as the equivocal likelihood of clinically significant prostate cancer, making its diagnosis very challenging. While PI-RADS values of 4 and 5 make biopsy necessary, it is very hard to establish whether to perform a biopsy or not in patients with a PI-RADS score 3. In recent years, machine learning algorithms have been proposed for a wide range of applications in medical fields, thanks to their ability to extract hidden information and to learn from a set of data without previous specific programming. In this paper, we evaluate machine learning approaches in detecting prostate cancer in patients with PI-RADS score 3 lesions via considering clinical-radiological characteristics. A total of 109 patients were included in this study. We collected data on body mass index (BMI), location of suspicious PI-RADS 3 lesions, serum prostate-specific antigen (PSA) level, prostate volume, PSA density, and histopathology results. The implemented classifiers exploit a patient's clinical and radiological information to generate a probability of malignancy that could help the physicians in diagnostic decisions, including the need for a biopsy.
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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.