Development and External Validation of Machine Learning-Based Models for Predicting Lung Metastasis in Kidney Cancer: A Large Population-Based Study
The accuracy of indices widely used to evaluate lung metastasis (LM) in patients with kidney cancer (KC) is insufficient. Therefore, we aimed at developing a model to estimate the risk of developing LM in KC based on a large population size and machine learning algorithms. Demographic and clinicopat...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | International Journal of Clinical Practice |
| Online Access: | http://dx.doi.org/10.1155/2023/8001899 |
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| author | Xinglin Yi Yuhan Zhang Juan Cai Yu Hu Kai Wen Pan Xie Na Yin Xiangdong Zhou Hu Luo |
| author_facet | Xinglin Yi Yuhan Zhang Juan Cai Yu Hu Kai Wen Pan Xie Na Yin Xiangdong Zhou Hu Luo |
| author_sort | Xinglin Yi |
| collection | DOAJ |
| description | The accuracy of indices widely used to evaluate lung metastasis (LM) in patients with kidney cancer (KC) is insufficient. Therefore, we aimed at developing a model to estimate the risk of developing LM in KC based on a large population size and machine learning algorithms. Demographic and clinicopathologic variables of patients with KC diagnosed between 2004 and 2017 were retrospectively analyzed. We performed a univariate logistic regression analysis to identify risk factors for LM in patients with KC. Six machine learning (ML) classifiers were established and tuned using the ten-fold cross-validation method. External validation was performed using clinicopathologic information from 492 patients from the Southwest Hospital, Chongqing, China. Algorithm performance was estimated by analyzing the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, precision, recall, F1 score, clinical decision analysis (DCA), and clinical utility curve (CUC). A total of 52,714 eligible patients diagnosed with KC were enrolled, of whom 2,618 developed LM. Variables of age, sex, race, T stage, N stage, tumor size, histology, and grade were identified as important for the prediction of LM. The extreme gradient boosting (XGB) algorithm performed better than other models in both the internal validation (AUC: 0.913, sensitivity: 0.873, specificity: 0.809, and F1 score: 0.325) and the external validation (AUC: 0.904, sensitivity: 0.750, specificity: 0.878, and F1 score: 0.364). This study established a predictive model for LM in KC patients based on ML algorithms which showed high accuracy and applicative value. A web-based predictor was built using the XGB model to help clinicians make more rational and personalized decisions. |
| format | Article |
| id | doaj-art-ab633ecbf989468e960f7e1ba0ca72b3 |
| institution | Kabale University |
| issn | 1742-1241 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Clinical Practice |
| spelling | doaj-art-ab633ecbf989468e960f7e1ba0ca72b32025-08-20T03:39:40ZengWileyInternational Journal of Clinical Practice1742-12412023-01-01202310.1155/2023/8001899Development and External Validation of Machine Learning-Based Models for Predicting Lung Metastasis in Kidney Cancer: A Large Population-Based StudyXinglin Yi0Yuhan Zhang1Juan Cai2Yu Hu3Kai Wen4Pan Xie5Na Yin6Xiangdong Zhou7Hu Luo8Department of Respiratory and Critical Care MedicineDepartment of Renal Dialysis CenterDepartment of Renal Dialysis CenterDepartment of Renal Dialysis CenterDepartment of Renal Dialysis CenterDepartment of Renal Dialysis CenterDepartment of Renal Dialysis CenterDepartment of Respiratory and Critical Care MedicineDepartment of Respiratory and Critical Care MedicineThe accuracy of indices widely used to evaluate lung metastasis (LM) in patients with kidney cancer (KC) is insufficient. Therefore, we aimed at developing a model to estimate the risk of developing LM in KC based on a large population size and machine learning algorithms. Demographic and clinicopathologic variables of patients with KC diagnosed between 2004 and 2017 were retrospectively analyzed. We performed a univariate logistic regression analysis to identify risk factors for LM in patients with KC. Six machine learning (ML) classifiers were established and tuned using the ten-fold cross-validation method. External validation was performed using clinicopathologic information from 492 patients from the Southwest Hospital, Chongqing, China. Algorithm performance was estimated by analyzing the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, precision, recall, F1 score, clinical decision analysis (DCA), and clinical utility curve (CUC). A total of 52,714 eligible patients diagnosed with KC were enrolled, of whom 2,618 developed LM. Variables of age, sex, race, T stage, N stage, tumor size, histology, and grade were identified as important for the prediction of LM. The extreme gradient boosting (XGB) algorithm performed better than other models in both the internal validation (AUC: 0.913, sensitivity: 0.873, specificity: 0.809, and F1 score: 0.325) and the external validation (AUC: 0.904, sensitivity: 0.750, specificity: 0.878, and F1 score: 0.364). This study established a predictive model for LM in KC patients based on ML algorithms which showed high accuracy and applicative value. A web-based predictor was built using the XGB model to help clinicians make more rational and personalized decisions.http://dx.doi.org/10.1155/2023/8001899 |
| spellingShingle | Xinglin Yi Yuhan Zhang Juan Cai Yu Hu Kai Wen Pan Xie Na Yin Xiangdong Zhou Hu Luo Development and External Validation of Machine Learning-Based Models for Predicting Lung Metastasis in Kidney Cancer: A Large Population-Based Study International Journal of Clinical Practice |
| title | Development and External Validation of Machine Learning-Based Models for Predicting Lung Metastasis in Kidney Cancer: A Large Population-Based Study |
| title_full | Development and External Validation of Machine Learning-Based Models for Predicting Lung Metastasis in Kidney Cancer: A Large Population-Based Study |
| title_fullStr | Development and External Validation of Machine Learning-Based Models for Predicting Lung Metastasis in Kidney Cancer: A Large Population-Based Study |
| title_full_unstemmed | Development and External Validation of Machine Learning-Based Models for Predicting Lung Metastasis in Kidney Cancer: A Large Population-Based Study |
| title_short | Development and External Validation of Machine Learning-Based Models for Predicting Lung Metastasis in Kidney Cancer: A Large Population-Based Study |
| title_sort | development and external validation of machine learning based models for predicting lung metastasis in kidney cancer a large population based study |
| url | http://dx.doi.org/10.1155/2023/8001899 |
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