Explainable machine learning for predicting lung metastasis of colorectal cancer
Abstract Patients with lung metastasis of colorectal cancer typically have a poor prognosis. Therefore, establishing an effective screening and diagnosis model is paramount. Our study seeks to construct and verify a predictive model utilizing machine learning (ML) that can evaluate the risk of lung...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-04-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-98188-5 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849699445708423168 |
|---|---|
| author | Zhentian Guo Zongming Zhang Limin Liu Yue Zhao Zhuo Liu Chong Zhang Hui Qi Jinqiu Feng Peijie Yao |
| author_facet | Zhentian Guo Zongming Zhang Limin Liu Yue Zhao Zhuo Liu Chong Zhang Hui Qi Jinqiu Feng Peijie Yao |
| author_sort | Zhentian Guo |
| collection | DOAJ |
| description | Abstract Patients with lung metastasis of colorectal cancer typically have a poor prognosis. Therefore, establishing an effective screening and diagnosis model is paramount. Our study seeks to construct and verify a predictive model utilizing machine learning (ML) that can evaluate the risk of lung metastasis with newly diagnosed colorectal cancer (CRC) using Shapley Additive exPlanations (SHAP). Using the Surveillance, Epidemiology, and End Results database, 39,674 were extracted for model development, all of whom had been pathologically diagnosed with CRC. The data spans from 2010 to 2015. Our study has constructed seven ML algorithms based on the data mentioned above, including Random Forest (RF), Decision Tree, Support Vector Machine, Naive Bayes, K-Nearest Neighbor, eXtreme Gradient Boosting, and Gradient Boosting Machine. We selected the best algorithm and visualized it using SHAP. We conducted a validation of the model utilizing data from a Chinese hospital to assess its practicality. Based on this, we have constructed an open web calculator. 39,674 patient data were included in our study, among whom 1369 (3.5%) presented with distant lung metastasis. The Random Forest (RF) algorithm demonstrated the highest predictive capability within the internal test set (AUC of 0.980, AUPR of 0.941). Furthermore, the random forest algorithm also exhibited excellent performance in external validation sets. Meanwhile, we have also established a web calculator ( http://121.43.117.60:8003/ ). The RF algorithm has demonstrated excellent predictive performance. It can assist clinicians in devising more personalized treatment plans. |
| format | Article |
| id | doaj-art-a4c82a33f8864be0a2e558d70ba835fa |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-a4c82a33f8864be0a2e558d70ba835fa2025-08-20T03:18:34ZengNature PortfolioScientific Reports2045-23222025-04-0115111210.1038/s41598-025-98188-5Explainable machine learning for predicting lung metastasis of colorectal cancerZhentian Guo0Zongming Zhang1Limin Liu2Yue Zhao3Zhuo Liu4Chong Zhang5Hui Qi6Jinqiu Feng7Peijie Yao8Department of General Surgery, Beijing Electric Power Hospital, State Grid Corporation of China, Capital Medical UniversityDepartment of General Surgery, Beijing Electric Power Hospital, State Grid Corporation of China, Capital Medical UniversityDepartment of General Surgery, Beijing Electric Power Hospital, State Grid Corporation of China, Capital Medical UniversityDepartment of General Surgery, Beijing Electric Power Hospital, State Grid Corporation of China, Capital Medical UniversityDepartment of General Surgery, Beijing Electric Power Hospital, State Grid Corporation of China, Capital Medical UniversityDepartment of General Surgery, Beijing Electric Power Hospital, State Grid Corporation of China, Capital Medical UniversityDepartment of General Surgery, Beijing Electric Power Hospital, State Grid Corporation of China, Capital Medical UniversityChina Clinical Medical Research Center for Hepatobiliary Diseases in General Surgery, China General Technology GroupChina Clinical Medical Research Center for Hepatobiliary Diseases in General Surgery, China General Technology GroupAbstract Patients with lung metastasis of colorectal cancer typically have a poor prognosis. Therefore, establishing an effective screening and diagnosis model is paramount. Our study seeks to construct and verify a predictive model utilizing machine learning (ML) that can evaluate the risk of lung metastasis with newly diagnosed colorectal cancer (CRC) using Shapley Additive exPlanations (SHAP). Using the Surveillance, Epidemiology, and End Results database, 39,674 were extracted for model development, all of whom had been pathologically diagnosed with CRC. The data spans from 2010 to 2015. Our study has constructed seven ML algorithms based on the data mentioned above, including Random Forest (RF), Decision Tree, Support Vector Machine, Naive Bayes, K-Nearest Neighbor, eXtreme Gradient Boosting, and Gradient Boosting Machine. We selected the best algorithm and visualized it using SHAP. We conducted a validation of the model utilizing data from a Chinese hospital to assess its practicality. Based on this, we have constructed an open web calculator. 39,674 patient data were included in our study, among whom 1369 (3.5%) presented with distant lung metastasis. The Random Forest (RF) algorithm demonstrated the highest predictive capability within the internal test set (AUC of 0.980, AUPR of 0.941). Furthermore, the random forest algorithm also exhibited excellent performance in external validation sets. Meanwhile, we have also established a web calculator ( http://121.43.117.60:8003/ ). The RF algorithm has demonstrated excellent predictive performance. It can assist clinicians in devising more personalized treatment plans.https://doi.org/10.1038/s41598-025-98188-5SEERColorectal cancerRandom forestLung metastasisSHapley additive exPlanations |
| spellingShingle | Zhentian Guo Zongming Zhang Limin Liu Yue Zhao Zhuo Liu Chong Zhang Hui Qi Jinqiu Feng Peijie Yao Explainable machine learning for predicting lung metastasis of colorectal cancer Scientific Reports SEER Colorectal cancer Random forest Lung metastasis SHapley additive exPlanations |
| title | Explainable machine learning for predicting lung metastasis of colorectal cancer |
| title_full | Explainable machine learning for predicting lung metastasis of colorectal cancer |
| title_fullStr | Explainable machine learning for predicting lung metastasis of colorectal cancer |
| title_full_unstemmed | Explainable machine learning for predicting lung metastasis of colorectal cancer |
| title_short | Explainable machine learning for predicting lung metastasis of colorectal cancer |
| title_sort | explainable machine learning for predicting lung metastasis of colorectal cancer |
| topic | SEER Colorectal cancer Random forest Lung metastasis SHapley additive exPlanations |
| url | https://doi.org/10.1038/s41598-025-98188-5 |
| work_keys_str_mv | AT zhentianguo explainablemachinelearningforpredictinglungmetastasisofcolorectalcancer AT zongmingzhang explainablemachinelearningforpredictinglungmetastasisofcolorectalcancer AT liminliu explainablemachinelearningforpredictinglungmetastasisofcolorectalcancer AT yuezhao explainablemachinelearningforpredictinglungmetastasisofcolorectalcancer AT zhuoliu explainablemachinelearningforpredictinglungmetastasisofcolorectalcancer AT chongzhang explainablemachinelearningforpredictinglungmetastasisofcolorectalcancer AT huiqi explainablemachinelearningforpredictinglungmetastasisofcolorectalcancer AT jinqiufeng explainablemachinelearningforpredictinglungmetastasisofcolorectalcancer AT peijieyao explainablemachinelearningforpredictinglungmetastasisofcolorectalcancer |