ReviewFrontiers in oncology2024
Harnessing machine learning to predict prostate cancer survival: a review.
Review in Frontiers in oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Article
- SARIFA and Lipid Metabolic Reprogramming in Prostate Cancer: Fundamental Mechanisms, Tumor Microenvironment, and Novel Biomarker Prospects.Life (Basel, Switzerland) · 2026Review
- Multi-Model Machine Learning for Survival Predictions for Castration-Resistant Prostate Cancer.Cancers · 2026Article
- Machine learning-based prognostic model integrating preoperative HALP score and lactate dehydrogenase for predicting postoperative recurrence of prostate cancer.World journal of surgical oncology · 2026Article
- Explainable Machine Learning-Based Overall Survival Classification in Prostate Adenocarcinoma Using Integrated Clinical and Transcriptomic Features.Diagnostics (Basel, Switzerland) · 2026Article
- Early treatment outcome prediction in metastatic castration-resistant prostate cancer utilizing 3-month tumor growth rate (medRxiv : the preprint server for health sciences · 2026Article
- Interpretable machine-learning risk stratification at the time of diagnosis for 3-year mortality inFrontiers in oncology · 2026Article
- RETRACTED: Machine-Learning-Based Survival Prediction in Castration-Resistant Prostate Cancer: A Multi-Model Analysis Using a Comprehensive Clinical Dataset.Journal of personalized medicine · 2025Article
- Optimizing clinical risk stratification of localized prostate cancer.Current opinion in urology · 2025Review
- Machine learning-based comparison of transperineal vs. transrectal biopsy for prostate cancer diagnosis: evaluating procedural effectiveness.The Canadian journal of urology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The prediction of survival outcomes is a key factor in making decisions for prostate cancer (PCa) treatment. Advances in computer-based technologies have increased the role of machine learning (ML) methods in predicting cancer prognosis. Due to the various effective treatments available for each non-linear landscape of PCa, the integration of ML can help offer tailored treatment strategies and precision medicine approaches, thus improving survival in patients with PCa. There has been an upsurge of studies utilizing ML to predict the survival of these patients using complex datasets, including patient and tumor features, radiographic data, and population-based databases. This review aims to explore the evolving role of ML in predicting survival outcomes associated with PCa. Specifically, we will focus on the applications of ML in forecasting biochemical recurrence-free, progression to castration-resistance-free, metastasis-free, and overall survivals. Additionally, we will suggest areas in need of further research in the future to enhance the utility of ML for a more clinically-utilizable PCa prognosis prediction and treatment optimization.
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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.