Development of machine learning prognostic models for overall survival of prostate cancer patients with lymph node-positive

Peng, Zi-He; Tian, Juan-Hua; Chen, Bo-Hong; Zhou, Hai-Bin; Bi, Hang; He, Min-Xin; Li, Ming-Rui; Zheng, Xin-Yu; Wang, Ya-Wen; Chong, Tie; Li, Zhao-Lun

doi:10.1038/s41598-023-45804-x

Cited by 4 publications

(1 citation statement)

References 39 publications

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“…ML can facilitate various problems, from patient-level observations to employing algorithms with numerous variables, seeking combinations, and ultimately reliably predicting risks and outcomes[ 35 ]. Numerous studies have developed valuable models utilizing ML techniques[ 36 – 39 ]. However, there is a dearth of research exploring the application of ML for predicting survival outcomes in RGC patients.…”

Section: Introductionmentioning

confidence: 99%

Identification of prognostic signatures in remnant gastric cancer through an interpretable risk model based on machine learning: a multicenter cohort study

Zhan,

Chen,

Cheng

et al. 2024

BMC Cancer

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Objective The purpose of this study was to develop an individual survival prediction model based on multiple machine learning (ML) algorithms to predict survival probability for remnant gastric cancer (RGC). Methods Clinicopathologic data of 286 patients with RGC undergoing operation (radical resection and palliative resection) from a multi-institution database were enrolled and analyzed retrospectively. These individuals were split into training (80%) and test cohort (20%) by using random allocation. Nine commonly used ML methods were employed to construct survival prediction models. Algorithm performance was estimated by analyzing accuracy, precision, recall, F1-score, area under the receiver operating characteristic curve (AUC), confusion matrices, five-fold cross-validation, decision curve analysis (DCA), and calibration curve. The best model was selected through appropriate verification and validation and was suitably explained by the SHapley Additive exPlanations (SHAP) approach. Results Compared with the traditional methods, the RGC survival prediction models employing ML exhibited good performance. Except for the decision tree model, all other models performed well, with a mean ROC AUC above 0.7. The DCA findings suggest that the developed models have the potential to enhance clinical decision-making processes, thereby improving patient outcomes. The calibration curve reveals that all models except the decision tree model displayed commendable predictive performance. Through CatBoost-based modeling and SHAP analysis, the five-year survival probability is significantly influenced by several factors: the lymph node ratio (LNR), T stage, tumor size, resection margins, perineural invasion, and distant metastasis. Conclusions This study established predictive models for survival probability at five years in RGC patients based on ML algorithms which showed high accuracy and applicative value.

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Section: Introductionmentioning

confidence: 99%

Identification of prognostic signatures in remnant gastric cancer through an interpretable risk model based on machine learning: a multicenter cohort study

Zhan,

Chen,

Cheng

et al. 2024

BMC Cancer

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A deep learning framework for hepatocellular carcinoma diagnosis using MS1 data

Xu,

Zhang,

Qian

et al. 2024

Sci Rep

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Clinical proteomics analysis is of great significance for analyzing pathological mechanisms and discovering disease-related biomarkers. Using computational methods to accurately predict disease types can effectively improve patient disease diagnosis and prognosis. However, how to eliminate the errors introduced by peptide precursor identification and protein identification for pathological diagnosis remains a major unresolved issue. Here, we develop a powerful end-to-end deep learning model, termed “MS1Former”, that is able to classify hepatocellular carcinoma tumors and adjacent non-tumor (normal) tissues directly using raw MS1 spectra without peptide precursor identification. Our model provides accurate discrimination of subtle m/z differences in MS1 between tumor and adjacent non-tumor tissue, as well as more general performance predictions for data-dependent acquisition, data-independent acquisition, and full-scan data. Our model achieves the best performance on multiple external validation datasets. Additionally, we perform a detailed exploration of the model’s interpretability. Prospectively, we expect that the advanced end-to-end framework will be more applicable to the classification of other tumors.

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Prediction of recurrence-free survival and risk factors of sinonasal inverted papilloma after surgery by machine learning models

Miao,

Cheng,

et al. 2024

Eur J Med Res

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Objectives Our research aims to construct machine learning prediction models to identify patients proned to recurrence after inverted papilloma (IP) surgery and guide their follow-up treatment. Methods This study collected 210 patients underwent IP resection surgery at a university hospital from January 2010 to December 2023. Six machine learning algorithms including ExtraSurvivalTrees (EST), GradientBoostingSurvivalAnalysis (GBSA), RandomSurvivalForest (RSF), SurvivalSVM, Coxnet and Coxph, were used to construct the prediction models. Shapley Additive Explanations (SHAP) values were used to explain the importance of various features in predicting IP recurrence. Results We found that the recurrence rate of IP patients is 20.00%, with a median recurrence time of 35.5 months. Multivariate Cox regression analysis identified mild or moderate dysplasia as an independent risk factor for recurrence. The EST model performs the best in predicting postoperative recurrence of IP, with C-index of 0.968 and 0.878 in the training and testing sets. SHAP emphasizes five important predictive factors for recurrence, including bone defects, orbital involvement, smoking, no processing of tumor attachment sites and drinking. Conclusions To our knowledge, this is the first study to use multiple ML models to predict postoperative recurrence of IP. The EST model has the best predictive performance, with SHAP emphasizing several key predictive factors for IP recurrence. This study emphasizes the practicality of machine learning algorithms in predicting IP clinical outcomes, providing valuable insights into the potential for improving clinical decision-making. Supplementary Information The online version contains supplementary material available at 10.1186/s40001-024-02099-6.

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Development of machine learning prognostic models for overall survival of prostate cancer patients with lymph node-positive

Cited by 4 publications

References 39 publications

Identification of prognostic signatures in remnant gastric cancer through an interpretable risk model based on machine learning: a multicenter cohort study

Identification of prognostic signatures in remnant gastric cancer through an interpretable risk model based on machine learning: a multicenter cohort study

A deep learning framework for hepatocellular carcinoma diagnosis using MS1 data

Prediction of recurrence-free survival and risk factors of sinonasal inverted papilloma after surgery by machine learning models

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