How platinum-induced nephrotoxicity occurs? Machine learning prediction in non-small cell lung cancer patients. (June 2022)

• Record Type: Journal Article
• Authors: Huang, Shih-Hui; Chu, Chao-Yu; Hsu, Yu-Chia; Wang, San-Yuan; Kuo, Li-Na; Bai, Kuan-Jen; Yu, Ming-Chih; Chang, Jer-Hwa; Liu, Eugene H.; Chen, Hsiang-Yin
• Is Part Of: Computer methods and programs in biomedicine. Volume 221(2022)
• Publication Date: 2022-06
• Subjects: Platinum -- Nephrotoxicity -- Artificial neural network -- Random forest -- Support vector machine -- Machine learning; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.106839
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Machine learning models can help the healthcare professionals predict platinum-induced nephrotoxicity in non-small cell lung cancer patients. The model could help clinicians identify the risk of nephrotoxicity for a patient newly prescribed with platinum chemotherapy, or administered for continuous cycles of the therapy. Dose-related features, especially accumulative platinum dose, play an important role in platinum-induced nephrotoxicity prediction. Abstract: Background and objective: Platinum-induced nephrotoxicity is a severe and unexpected adverse drug reaction that could lead to treatment failure in non-small cell lung cancer patients. Better prediction and management of this nephrotoxicity can increase patient survival. Our study aimed to build up and compare the best machine learning models with clinical and genomic features to predict platinum-induced nephrotoxicity in non-small cell lung cancer patients. Methods: Clinical and genomic data of patients undergoing platinum chemotherapy at Wan Fang Hospital were collected after they were recruited. Twelve models were established by artificial neural network, logistic regression, random forest, and support vector machine with integrated, clinical, and genomic modes. Grid search and genetic algorithm were applied to construct the fine-tuned model with the best combination of predictive hyperparameters and features. Accuracy, precision, recall, F1 score, and area under the receiver operating characteristic curve were calculated to compare the performance of the 12 models. Results: In total, 118 patients were recruited for this study, among which 28 (23.73%) were experiencing nephrotoxicity. Machine learning models with clinical and genomic features achieved better prediction performances than clinical or genomic features alone. Artificial neural network with clinical and genomic features demonstrated the best predictive outcomes among all 12 models. The average accuracy, precision, recall, F1 score and area under the receiver operating characteristic curve of the artificial neural network with integrated mode were 0.923, 0.950, 0.713, 0.808 and 0.900, respectively. Conclusions: Machine learning models with clinical and genomic features can be a preliminary tool for oncologists to predict platinum-induced nephrotoxicity and provide preventive strategies in advance.