Machine learning-based prediction of acute kidney injury after nephrectomy in patients with renal cell carcinoma
Abstract The precise prediction of acute kidney injury (AKI) after nephrectomy for renal cell carcinoma (RCC) is an important issue because of its relationship with subsequent kidney dysfunction and high mortality. Herein we addressed whether machine learning (ML) algorithms could predict postoperat...
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Nature Portfolio
2021
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oai:doaj.org-article:453c98a5ce9c472bbe11c7f343605b8a2021-12-02T18:49:32ZMachine learning-based prediction of acute kidney injury after nephrectomy in patients with renal cell carcinoma10.1038/s41598-021-95019-12045-2322https://doaj.org/article/453c98a5ce9c472bbe11c7f343605b8a2021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-95019-1https://doaj.org/toc/2045-2322Abstract The precise prediction of acute kidney injury (AKI) after nephrectomy for renal cell carcinoma (RCC) is an important issue because of its relationship with subsequent kidney dysfunction and high mortality. Herein we addressed whether machine learning (ML) algorithms could predict postoperative AKI risk better than conventional logistic regression (LR) models. A total of 4104 RCC patients who had undergone unilateral nephrectomy from January 2003 to December 2017 were reviewed. ML models such as support vector machine, random forest, extreme gradient boosting, and light gradient boosting machine (LightGBM) were developed, and their performance based on the area under the receiver operating characteristic curve, accuracy, and F1 score was compared with that of the LR-based scoring model. Postoperative AKI developed in 1167 patients (28.4%). All the ML models had higher performance index values than the LR-based scoring model. Among them, the LightGBM model had the highest value of 0.810 (0.783–0.837). The decision curve analysis demonstrated a greater net benefit of the ML models than the LR-based scoring model over all the ranges of threshold probabilities. The application of ML algorithms improves the predictability of AKI after nephrectomy for RCC, and these models perform better than conventional LR-based models.Yeonhee LeeJiwon RyuMin Woo KangKyung Ha SeoJayoun KimJungyo SuhYong Chul KimDong Ki KimKook-Hwan OhKwon Wook JooYon Su KimChang Wook JeongSang Chul LeeCheol KwakSejoong KimSeung Seok HanNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021) |
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DOAJ |
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Medicine R Science Q |
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Medicine R Science Q Yeonhee Lee Jiwon Ryu Min Woo Kang Kyung Ha Seo Jayoun Kim Jungyo Suh Yong Chul Kim Dong Ki Kim Kook-Hwan Oh Kwon Wook Joo Yon Su Kim Chang Wook Jeong Sang Chul Lee Cheol Kwak Sejoong Kim Seung Seok Han Machine learning-based prediction of acute kidney injury after nephrectomy in patients with renal cell carcinoma |
| description |
Abstract The precise prediction of acute kidney injury (AKI) after nephrectomy for renal cell carcinoma (RCC) is an important issue because of its relationship with subsequent kidney dysfunction and high mortality. Herein we addressed whether machine learning (ML) algorithms could predict postoperative AKI risk better than conventional logistic regression (LR) models. A total of 4104 RCC patients who had undergone unilateral nephrectomy from January 2003 to December 2017 were reviewed. ML models such as support vector machine, random forest, extreme gradient boosting, and light gradient boosting machine (LightGBM) were developed, and their performance based on the area under the receiver operating characteristic curve, accuracy, and F1 score was compared with that of the LR-based scoring model. Postoperative AKI developed in 1167 patients (28.4%). All the ML models had higher performance index values than the LR-based scoring model. Among them, the LightGBM model had the highest value of 0.810 (0.783–0.837). The decision curve analysis demonstrated a greater net benefit of the ML models than the LR-based scoring model over all the ranges of threshold probabilities. The application of ML algorithms improves the predictability of AKI after nephrectomy for RCC, and these models perform better than conventional LR-based models. |
| format |
article |
| author |
Yeonhee Lee Jiwon Ryu Min Woo Kang Kyung Ha Seo Jayoun Kim Jungyo Suh Yong Chul Kim Dong Ki Kim Kook-Hwan Oh Kwon Wook Joo Yon Su Kim Chang Wook Jeong Sang Chul Lee Cheol Kwak Sejoong Kim Seung Seok Han |
| author_facet |
Yeonhee Lee Jiwon Ryu Min Woo Kang Kyung Ha Seo Jayoun Kim Jungyo Suh Yong Chul Kim Dong Ki Kim Kook-Hwan Oh Kwon Wook Joo Yon Su Kim Chang Wook Jeong Sang Chul Lee Cheol Kwak Sejoong Kim Seung Seok Han |
| author_sort |
Yeonhee Lee |
| title |
Machine learning-based prediction of acute kidney injury after nephrectomy in patients with renal cell carcinoma |
| title_short |
Machine learning-based prediction of acute kidney injury after nephrectomy in patients with renal cell carcinoma |
| title_full |
Machine learning-based prediction of acute kidney injury after nephrectomy in patients with renal cell carcinoma |
| title_fullStr |
Machine learning-based prediction of acute kidney injury after nephrectomy in patients with renal cell carcinoma |
| title_full_unstemmed |
Machine learning-based prediction of acute kidney injury after nephrectomy in patients with renal cell carcinoma |
| title_sort |
machine learning-based prediction of acute kidney injury after nephrectomy in patients with renal cell carcinoma |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/453c98a5ce9c472bbe11c7f343605b8a |
| work_keys_str_mv |
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