ArticleScientific reports2023
Development of machine learning prognostic models for overall survival of prostate cancer patients with lymph node-positive.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 25 citations in OpenAlex.
- Prediction models in prostate cancer: a systematic review and meta-analysis.Frontiers in oncology · 2026Pooled it
- Prognostic prediction of hepatoblastoma in children: development and validation of machine learning models-an SEER-based study.Updates in surgery · 2026Article
- Explainable Machine Learning-Based Overall Survival Classification in Prostate Adenocarcinoma Using Integrated Clinical and Transcriptomic Features.Diagnostics (Basel, Switzerland) · 2026Article
- Lymph Node Invasion Prediction in Prostate Cancer: A Comparative Machine-Learning Study.Annals of surgical oncology · 2026Article
- Artificial Intelligence Versus Conventional Methods for NCCN Risk Stratification in Localized Prostate Cancer (2020-2025): A Systematic Review.Prostate cancer · 2026Review
- Machine learning and cancer survival: analysis of the Registro Hospitalar de Câncer do Estado de São Paulo.Revista de saude publica · 2026Article
- Comparison of machine learning and Cox regression models for prognostic analysis in hepatocellular carcinoma patients with distant metastasis.Surgery open science · 2025Article
- Preoperative arterial and venous CT radiomics for survival prediction after pylorus preserving pancreatoduodenectomy in pancreatic head cancer.Scientific reports · 2025Article
- Developing practical machine learning survival models to identify high-risk patients for in-hospital mortality following traumatic brain injury.Scientific reports · 2025Article
- A prognostic model for highly aggressive prostate cancer using interpretable machine learning techniques.Frontiers in medicine · 2025Article
- Ensemble machine learning models for predicting bone metastasis in bladder cancer.Frontiers in oncology · 2025Article
- A deep learning framework for hepatocellular carcinoma diagnosis using MS1 data.Scientific reports · 2024Article
- Prediction of recurrence-free survival and risk factors of sinonasal inverted papilloma after surgery by machine learning models.European journal of medical research · 2024Article
- Article
- Predicting Early Treatment Effectiveness in Bell's Palsy Using Machine Learning: A Focus on Corticosteroids and Antivirals.International journal of general medicine · 2024Article
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Authors and funding
11 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prostate cancer (PCa) patients with lymph node involvement (LNI) constitute a single-risk group with varied prognoses. Existing studies on this group have focused solely on those who underwent prostatectomy (RP), using statistical models to predict prognosis. This study aimed to develop an easily accessible individual survival prediction tool based on multiple machine learning (ML) algorithms to predict survival probability for PCa patients with LNI. A total of 3280 PCa patients with LNI were identified from the Surveillance, Epidemiology, and End Results (SEER) database, covering the years 2000-2019. The primary endpoint was overall survival (OS). Gradient Boosting Survival Analysis (GBSA), Random Survival Forest (RSF), and Extra Survival Trees (EST) were used to develop prognosis models, which were compared to Cox regression. Discrimination was evaluated using the time-dependent areas under the receiver operating characteristic curve (time-dependent AUC) and the concordance index (c-index). Calibration was assessed using the time-dependent Brier score (time-dependent BS) and the integrated Brier score (IBS). Moreover, the beeswarm summary plot in SHAP (SHapley Additive exPlanations) was used to display the contribution of variables to the results. The 3280 patients were randomly split into a training cohort (n = 2624) and a validation cohort (n = 656). Nine variables including age at diagnosis, race, marital status, clinical T stage, prostate-specific antigen (PSA) level at diagnosis, Gleason Score (GS), number of positive lymph nodes, radical prostatectomy (RP), and radiotherapy (RT) were used to develop models. The mean time-dependent AUC for GBSA, RSF, and EST was 0.782 (95% confidence interval [CI] 0.779-0.783), 0.779 (95% CI 0.776-0.780), and 0.781 (95% CI 0.778-0.782), respectively, which were higher than the Cox regression model of 0.770 (95% CI 0.769-0.773). Additionally, all models demonstrated almost similar calibration, with low IBS. A web-based prediction tool was developed using the best-performing GBSA, which is accessible at https://pengzihexjtu-pca-n1.streamlit.app/ . ML algorithms showed better performance compared with Cox regression and we developed a web-based tool, which may help to guide patient treatment and follow-up.
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