ArticleBMC cancer2024
Time-dependent personalized prognostic analysis by machine learning in biochemical recurrence after radical prostatectomy: a retrospective cohort study.
Article in BMC cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 2 of them syntheses that pooled it.
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
7 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Dynamic Prediction of Clinical Endpoints in Survival Analysis With Machine and Deep Learning: A Systematic Review and Regulatory Perspective.CPT: pharmacometrics & systems pharmacology · 2026Pooled it
- Artificial Intelligence (AI)-based tools in the diagnosis and management of prostate cancer: a systematic review and meta-analysis.Prostate cancer and prostatic diseases · 2026Pooled it
- Evaluating Risk Factors Associating With Late Recurrence After Low-Dose-Rate Brachytherapy for Prostate Cancer Using Machine Learning.The Prostate · 2026Article
- A Metabolic-Related Gene Signature for Predicting Biochemical Recurrence After Radical Prostatectomy: An Integrative Analysis and Targeted Therapeutic Validation.International journal of molecular sciences · 2026Article
- Machine learning-based prognostic signature integrating mitochondrial function and programmed cell death patterns in pancreatic adenocarcinoma.Scientific reports · 2026Article
- Integrating machine learning models with multi-omics analysis to decipher the prognostic significance of mitotic catastrophe heterogeneity in bladder cancer.Biology direct · 2025Article
- Artificial intelligence in prostate cancer: navigating the new frontier of precision uro-oncology.American journal of clinical and experimental urology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
backgroundFor biochemical recurrence following radical prostatectomy for prostate cancer, treatments such as radiation therapy and androgen deprivation therapy are administered. To diagnose postoperative recurrence as early as possible and to intervene with treatment at the appropriate time, it is essential to accurately predict recurrence after radical prostatectomy. However, postoperative recurrence involves numerous patient-related factors, making its prediction challenging. The purpose of this study is to accurately predict the timing of biochemical recurrence after radical prostatectomy and to analyze the risk factors for follow-up of high-risk patients and early detection of recurrence.
methodsWe utilized the machine learning survival analysis model called the Random Survival Forest utilizing the 58 clinical factors from 548 patients who underwent radical prostatectomy at Chiba University Hospital. To visualize prognostic factors and assess accuracy of the time course probability, we employed SurvSHAP(t) and time-dependent Area Under Cureve(AUC).
resultsThe time-dependent AUC of RSF was 0.785, which outperformed the Cox proportional hazards model (0.704), the Cancer of the Prostate Risk Assessment (CAPRA) score (0.710), and the D'Amico score (0.658). The key prognostic factors for early recurrence were Gleason score(GS), Seminal vesicle invasion(SV), and PSA. The contribution of PSA to recurrence decreases after the first year, while SV and GS increase over time.
conclusionOur prognostic model analyzed the time-dependent relationship between the timing of recurrence and prognostic factors. Our study achieved personalized prognosis analysis and its rationale after radical prostatectomy by employing machine learning prognostic model. This prognostic model contributes to the early detection of recurrence by enabling clinicians to conduct appropriate follow-ups for high-risk patients.
Indexed as
Identifiers
What OpenQuestion holds
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.