ArticleOncology2025
Constructing and Validating Models for Predicting Gleason Grade Group Upgrading following Radical Prostatectomy in Localized Prostate Cancer: A Comparison between Machine Learning Algorithms and Conventional Logistic Regression.
Article in Oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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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.
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Who cites it
2 citing papers in PubMed.
- Machine learning-based prediction of post-thrombotic syndrome in unprovoked lower extremity deep vein thrombosis: a two-center study incorporating uric acid and clinical risk factors.European journal of medical research · 2026Article
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
introductionThe occurrence of Gleason grade group upgrading (GGU) significantly impacts treatment strategy developments. We aimed to develop an optimal predictive model to assess the risk of GGU in patients with localized prostate cancer (PCa), by comparing traditional logistic regression (LR) with seven machine learning algorithms.
methodsA retrospective collection of clinical data was conducted on patients who underwent radical prostatectomy at Wuhan Central Hospital (January 2017 to December 2023, n = 177) and Jiangxi Cancer Hospital (July 2019 to February 2024, n = 87). The least absolute shrinkage and selection operator regression was employed to filter the clinical characteristics of patients. Subsequently, models were conducted using multivariate LR, along with seven diverse machine learning algorithms: extreme gradient boosting, decision tree, multilayer perceptron, naive Bayes, K-nearest neighbors, random forest, and support vector machine. By employing the receiver operating characteristic curves, accuracy, brier score, recall, calibration curves, and decision curve analysis (DCA), we compared the predictive capabilities and clinical utility of eight models to identify the optimal one.
resultsIn the evaluation of eight models, the LR model demonstrated superior performance. In the modeling set, it achieved an area under curve (AUC) of 0.826 (95% CI: 0.808-0.845), accuracy of 0.765, and a brier score of 0.167. In the validation set, it kept good results with an AUC of 0.819 (95% CI: 0.758-0.880), accuracy of 0.725, and a brier score of 0.180. The calibration curves, brier score, and DCA also demonstrated the excellent calibration and net benefit of the LR model.
conclusionsAfter conducting a comprehensive multi-model comparison, we concluded that the LR model was optimal for predicting GGU, which was confirmed by external validation. Our study also revealed percent free prostate-specific antigen density as a predictive factor for GGU, offering a novel approach for managing localized PCa patients.
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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.