SynthesisJournal of medical Internet research2021
Radiomic and Genomic Machine Learning Method Performance for Prostate Cancer Diagnosis: Systematic Literature Review.
Synthesis in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 2 of them syntheses that pooled it.
What it found
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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
30 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial Intelligence Performance in Image-Based Cancer Identification: Umbrella Review of Systematic Reviews.Journal of medical Internet research · 2025Pooled it
- Circulating miRNA profiles in COVID-19 patients and meta-analysis: implications for disease progression and prognosis.Scientific reports · 2023Pooled it
- Predicting the severity of COVID-19 using machine learning methods.BMC medical informatics and decision making · 2026Article
- The gastrin-releasing peptide receptor in oncology: a comprehensive review of recent advances in targeted radionuclide theranostics.Frontiers in oncology · 2026Review
- Biomarkers in Localized Prostate Cancer: From Diagnosis to Treatment.International journal of molecular sciences · 2025Review
- Framework for Race-Specific Prostate Cancer Detection Using Machine Learning Through Gene Expression Data: Feature Selection Optimization Approach.JMIR bioinformatics and biotechnology · 2025Article
- Evaluating prostate cancer diagnostic methods: The role and relevance of digital rectal examination in modern era.Investigative and clinical urology · 2025Review
- Personalized optimization of systematic prostate biopsy core number based on mpMRI radiomics features: a large-sample retrospective analysis.BMC cancer · 2025Article
- Evaluating the feasibility of AI-predicted bpMRI image features for predicting prostate cancer aggressiveness: a multi-center study.Insights into imaging · 2025Article
- Machine learning models for predicting prostate cancer recurrence and identifying potential molecular biomarkers.Frontiers in oncology · 2025Article
- Gastrin-releasing peptide receptor (GRPR) as a novel biomarker and therapeutic target in prostate cancer.Annals of medicine · 2024Review
- A pulmonary hypertension targeted algorithm to improve referral to right heart catheterization: A machine learning approach.Computational and structural biotechnology journal · 2024Article
- Article
- Article
- The Diagnostic Accuracy of Transcranial Color-Coded Doppler Ultrasound Technique in Stratifying Intracranial Cerebral Artery Stenoses in Cerebrovascular Disease Patients: A Systematic Review and Meta-Analysis.Journal of clinical medicine · 2024Review
- Phenotype clustering of hospitalized high-risk patients with COVID-19 - a machine learning approach within the multicentre, multinational PCHF-COVICAV registry.Cardiology journal · 2024Article
- A transfer learning nomogram for predicting prostate cancer and benign conditions on MRI.BMC medical imaging · 2023Article
- Multi-Institutional Development and Validation of a Radiomic Model to Predict Prostate Cancer Recurrence Following Radical Prostatectomy.Journal of clinical medicine · 2023Article
- An overview of meta-analyses on radiomics: more evidence is needed to support clinical translation.Insights into imaging · 2023Article
- Emerging Hallmarks of Metabolic Reprogramming in Prostate Cancer.International journal of molecular sciences · 2023Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMachine learning algorithms have been drawing attention at the joining of pathology and radiology in prostate cancer research. However, due to their algorithmic learning complexity and the variability of their architecture, there is an ongoing need to analyze their performance.
objectiveThis study assesses the source of heterogeneity and the performance of machine learning applied to radiomic, genomic, and clinical biomarkers for the diagnosis of prostate cancer. One research focus of this study was on clearly identifying problems and issues related to the implementation of machine learning in clinical studies.
methodsFollowing the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol, 816 titles were identified from the PubMed, Scopus, and OvidSP databases. Studies that used machine learning to detect prostate cancer and provided performance measures were included in our analysis. The quality of the eligible studies was assessed using the QUADAS-2 (quality assessment of diagnostic accuracy studies-version 2) tool. The hierarchical multivariate model was applied to the pooled data in a meta-analysis. To investigate the heterogeneity among studies, I
resultsIn the final analysis, 37 studies were included, of which 29 entered the meta-analysis pooling. The analysis of machine learning methods to detect prostate cancer reveals the limited usage of the methods and the lack of standards that hinder the implementation of machine learning in clinical applications.
conclusionsThe performance of machine learning for diagnosis of prostate cancer was considered satisfactory for several studies investigating the multiparametric magnetic resonance imaging and urine biomarkers; however, given the limitations indicated in our study, further studies are warranted to extend the potential use of machine learning to clinical settings. Recommendations on the use of machine learning techniques were also provided to help researchers to design robust studies to facilitate evidence generation from the use of radiomic and genomic biomarkers.
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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.