How platinum-induced nephrotoxicity occurs? Machine learning prediction in non-small cell lung cancer patients

Shih Hui Huang, Chao Yu Chu, Yu Chia Hsu, San Yuan Wang, Li Na Kuo, Kuan Jen Bai, Ming Chih Yu, Jer Hwa Chang, Eugene H. Liu, Hsiang Yin Chen

研究成果: 雜誌貢獻文章同行評審

1 引文 斯高帕斯(Scopus)

摘要

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.
原文英語
文章編號106839
期刊Computer Methods and Programs in Biomedicine
221
DOIs
出版狀態已發佈 - 6月 2022

ASJC Scopus subject areas

  • 軟體
  • 健康資訊學
  • 電腦科學應用

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