ArticleFrontiers in oncology2022
Machine Learning-Based Models Enhance the Prediction of Prostate Cancer.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prediction models in prostate cancer: a systematic review and meta-analysis.Frontiers in oncology · 2026Pooled it
- Application of machine learning model in predicting the likelihood of blood transfusion after hip fracture surgery.Aging clinical and experimental research · 2023Trial
- Present status of prostate cancer diagnosis, limitations, challenges, and future endeavors.Annals of medicine · 2026Review
- Prioritising Data Quality Governance for AI in Prostate Cancer: A Methodological Proof-of-Concept Study Using Neural Networks for Risk Stratification.Diagnostics (Basel, Switzerland) · 2026Article
- Predicting Prostate Cancer Risk and Its Associated Factors Using Machine Learning Techniques: A Retrospective Study.Health science reports · 2026Article
- Integrating multimodal clinical data with a large model for prostate cancer diagnosis.NPJ digital medicine · 2026Article
- Review
- Artificial Intelligence Versus Conventional Methods for NCCN Risk Stratification in Localized Prostate Cancer (2020-2025): A Systematic Review.Prostate cancer · 2026Review
- Prediction of bone or lung metastasis in prostate cancer using machine learning: a SEER-based study with external validation.Discover oncology · 2025Article
- Utilizing Predictive Analytics to Understand Neurogenic Bladder Symptom Score (NBSS) Variations in Adults With Acquired Spinal Cord Injury.Neurourology and urodynamics · 2025Article
- Predicting surgical intervention in paediatric cervical abscesses using machine learning: a comparative analysis.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2025Article
- Transforming Prostate Cancer Care: Innovations in Diagnosis, Treatment, and Future Directions.International journal of molecular sciences · 2025Review
- Multi-center study: ultrasound-based deep learning features for predicting Ki-67 expression in breast cancer.Scientific reports · 2025Article
- 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
- An online clustering algorithm predicting model for prostate cancer based on PHI-related variables and PI-RADS in different PSA populations.Cancer cell international · 2025Article
- Machine Learning-Based Prediction of Prostate Biopsy Necessity Using PSA, MRI, and Hematologic Parameters.Journal of clinical medicine · 2024Article
- Machine learning algorithms that predict the risk of prostate cancer based on metabolic syndrome and sociodemographic characteristics: a prospective cohort study.BMC public health · 2024Article
- Article
- Unleashing the potential: Artificial intelligence in urology for enhanced diagnosis, treatment, and personalized care.Investigative and clinical urology · 2023Article
- Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: PSA is currently the most commonly used screening indicator for prostate cancer. However, it has limited specificity for the diagnosis of prostate cancer. We aim to construct machine learning-based models and enhance the prediction of prostate cancer. Methods: The data of 551 patients who underwent prostate biopsy were retrospectively retrieved and divided into training and test datasets in a 3:1 ratio. We constructed five PCa prediction models with four supervised machine learning algorithms, including tPSA univariate logistic regression (LR), multivariate LR, decision tree (DT), random forest (RF), and support vector machine (SVM). The five prediction models were compared based on model performance metrics, such as the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, calibration curve, and clinical decision curve analysis (DCA). Results: All five models had good calibration in the training dataset. In the training dataset, the RF, DT, and multivariate LR models showed better discrimination, with AUCs of 1.0, 0.922 and 0.91, respectively, than the tPSA univariate LR and SVM models. In the test dataset, the multivariate LR model exhibited the best discrimination (AUC=0.918). The multivariate LR model and SVM model had better extrapolation and generalizability, with little change in performance between the training and test datasets. Compared with the DCA curves of the tPSA LR model, the other four models exhibited better net clinical benefits. Conclusion: The results of the current retrospective study suggest that machine learning techniques can predict prostate cancer with significantly better AUC, accuracy, and net clinical benefits.
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