ArticleWorld journal of urology2021
PSA-based machine learning model improves prostate cancer risk stratification in a screening population.
Article in World journal of urology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 49 citations in OpenAlex.
- Identification of Factors Affecting Prostate Cancer Using Machine Learning Methods: A Systematic Review.Asian Pacific journal of cancer prevention : APJCP · 2025Pooled it
- From prostate-specific antigen to precision: The future of prostate cancer diagnosis with artificial intelligence, biomarkers, and imaging.Current urology · 2026Review
- Artificial Intelligence Versus Conventional Methods for NCCN Risk Stratification in Localized Prostate Cancer (2020-2025): A Systematic Review.Prostate cancer · 2026Review
- Transforming Prostate Cancer Care: Innovations in Diagnosis, Treatment, and Future Directions.International journal of molecular sciences · 2025Review
- Article
- An overview of utilizing artificial intelligence in localized prostate cancer imaging.Expert review of medical devices · 2025Review
- Prediction of prostate biopsy outcomes at different cut-offs of prostate-specific antigen using machine learning: a multicenter study.Journal of the Egyptian National Cancer Institute · 2025Article
- Prediction of Prostate Cancer From Routine Laboratory Markers With Automated Machine Learning.Journal of clinical laboratory analysis · 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
- Prostate Cancer Diagnosis via Visual Representation of Tabular Data and Deep Transfer Learning.Bioengineering (Basel, Switzerland) · 2024Article
- Improvement of Cancer Prevention and Control: Reflection on the Role of Emerging Information Technologies.Journal of medical Internet research · 2024Article
- Machine learning-based prediction model and visual interpretation for prostate cancer.BMC urology · 2023Article
- Advancing prostate cancer detection: a comparative analysis of PCLDA-SVM and PCLDA-KNN classifiers for enhanced diagnostic accuracy.Scientific reports · 2023Article
- Article
- Artificial intelligence and imaging for risk prediction of pancreatic cancer: a narrative review.Chinese clinical oncology · 2022Review
- GPX2 predicts recurrence-free survival and triggers the Wnt/β-catenin/EMT pathway in prostate cancer.PeerJ · 2022Article
- Machine Learning-Based Models Enhance the Prediction of Prostate Cancer.Frontiers in oncology · 2022Article
- An introduction to machine learning for clinicians: How can machine learning augment knowledge in geriatric oncology?Journal of geriatric oncology · 2021Article
- Applied machine learning in cancer research: A systematic review for patient diagnosis, classification and prognosis.Computational and structural biotechnology journal · 2021Review
- PSA Based Biomarkers, Imagistic Techniques and Combined Tests for a Better Diagnostic of Localized Prostate Cancer.Diagnostics (Basel, Switzerland) · 2020Review
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
contextThe majority of prostate cancer diagnoses are facilitated by testing serum Prostate Specific Antigen (PSA) levels. Despite this, there are limitations to the diagnostic accuracy of PSA. Consideration of patient demographic factors and biochemical adjuncts to PSA may improve prostate cancer risk stratification. We aimed to develop a contemporary, accurate and cost-effective model based on objective measures to improve the accuracy of prostate cancer risk stratification.
methodsData were collated from a local institution and combined with patient data retrieved from the Prostate, Lung, Colorectal and Ovarian Cancer screening Trial (PLCO) database. Using a dataset of 4548 patients, a machine learning model was developed and trained using PSA, free-PSA, age and free-PSA to total PSA (FTR) ratio.
resultsThe model was trained on a dataset involving 3638 patients and was then tested on a separate set of 910 patients. The model improved prediction for prostate cancer (AUC 0.72) compared to PSA alone (AUC 0.63), age (AUC 0.52), free-PSA (AUC 0.50) and FTR alone (AUC 0.65). When an operating point is chosen such that the sensitivity of the model is 80% the specificity of the model is 45.3%. The benefit in AUC secondary to the model was related to sample size, with AUC of 0.64 observed when a subset of the cohort was assessed.
conclusionsDevelopment of a dense neural network model improved the diagnostic accuracy in screening for prostate cancer. These results demonstrate an additional utility of machine learning methods in prostate cancer risk stratification when using biochemical parameters.
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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.