Artificial intelligence system for predicting hand-foot skin reaction induced by vascular endothelial growth factor receptor inhibitors
Abstract Hand-foot skin reaction (HFSR) is a common adverse effect of vascular endothelial growth factor receptor (VEGFR) inhibitors that significantly impacts patients’ quality of life. Prevention and management of HFSR require individualized approaches, but risk factors remain unclear. This study...
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Nature Portfolio
2025-03-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-93471-x |
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| author | Taro Yamanaka Jumpei Ukita Dongyi Xue Chihiro Kondoh Seiwa Honda Maiko Noguchi Yoshiko Yonejima Kiyomi Nonogaki Kohji Takemura Rika Kizawa Takeshi Yamaguchi Yuko Tanabe Koichi Suyama Keisuke Ogaki Yuji Miura |
| author_facet | Taro Yamanaka Jumpei Ukita Dongyi Xue Chihiro Kondoh Seiwa Honda Maiko Noguchi Yoshiko Yonejima Kiyomi Nonogaki Kohji Takemura Rika Kizawa Takeshi Yamaguchi Yuko Tanabe Koichi Suyama Keisuke Ogaki Yuji Miura |
| author_sort | Taro Yamanaka |
| collection | DOAJ |
| description | Abstract Hand-foot skin reaction (HFSR) is a common adverse effect of vascular endothelial growth factor receptor (VEGFR) inhibitors that significantly impacts patients’ quality of life. Prevention and management of HFSR require individualized approaches, but risk factors remain unclear. This study aimed to develop artificial intelligence (AI) models to predict grade ≥ 2 HFSR using clinical data and foot sole images from 93 instances of VEGFR inhibitor administration in 76 patients. Image-based, clinical information-based, and ensemble AI models achieved areas under the curve of 0.550, 0.693, and 0.699, respectively. At a high-specificity cutoff, the ensemble AI had a positive predictive value of 0.824, suggesting potential clinical utility for identifying high-risk patients. Feature importance analysis revealed heavier weight, good performance status, lack of prior VEGFR inhibitor exposure, and baseline skin toxicity as risk factors. These findings represent the first AI-based HFSR prediction models and provide insights for preventive interventions, but further accuracy improvements are needed. |
| format | Article |
| id | doaj-art-8a2e727896c247bcb4af4a24dbcf6fd5 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-8a2e727896c247bcb4af4a24dbcf6fd52025-08-20T02:41:31ZengNature PortfolioScientific Reports2045-23222025-03-011511710.1038/s41598-025-93471-xArtificial intelligence system for predicting hand-foot skin reaction induced by vascular endothelial growth factor receptor inhibitorsTaro Yamanaka0Jumpei Ukita1Dongyi Xue2Chihiro Kondoh3Seiwa Honda4Maiko Noguchi5Yoshiko Yonejima6Kiyomi Nonogaki7Kohji Takemura8Rika Kizawa9Takeshi Yamaguchi10Yuko Tanabe11Koichi Suyama12Keisuke Ogaki13Yuji Miura14Department of Medical Oncology, Toranomon HospitalM3 Inc.M3 Inc.Department of Medical Oncology, National Cancer Center Hospital EastM3 Inc.Mebix Inc.Department of Medical Oncology, Toranomon HospitalDepartment of Medical Oncology, Toranomon HospitalDepartment of Medical Oncology, Toranomon HospitalDepartment of Medical Oncology, Toranomon HospitalDepartment of Medical Oncology, Toranomon HospitalDepartment of Medical Oncology, Toranomon HospitalDepartment of Medical Oncology, Toranomon HospitalM3 Inc.Department of Medical Oncology, Toranomon HospitalAbstract Hand-foot skin reaction (HFSR) is a common adverse effect of vascular endothelial growth factor receptor (VEGFR) inhibitors that significantly impacts patients’ quality of life. Prevention and management of HFSR require individualized approaches, but risk factors remain unclear. This study aimed to develop artificial intelligence (AI) models to predict grade ≥ 2 HFSR using clinical data and foot sole images from 93 instances of VEGFR inhibitor administration in 76 patients. Image-based, clinical information-based, and ensemble AI models achieved areas under the curve of 0.550, 0.693, and 0.699, respectively. At a high-specificity cutoff, the ensemble AI had a positive predictive value of 0.824, suggesting potential clinical utility for identifying high-risk patients. Feature importance analysis revealed heavier weight, good performance status, lack of prior VEGFR inhibitor exposure, and baseline skin toxicity as risk factors. These findings represent the first AI-based HFSR prediction models and provide insights for preventive interventions, but further accuracy improvements are needed.https://doi.org/10.1038/s41598-025-93471-xHFSRVEGFR inhibitorDeep learningAI |
| spellingShingle | Taro Yamanaka Jumpei Ukita Dongyi Xue Chihiro Kondoh Seiwa Honda Maiko Noguchi Yoshiko Yonejima Kiyomi Nonogaki Kohji Takemura Rika Kizawa Takeshi Yamaguchi Yuko Tanabe Koichi Suyama Keisuke Ogaki Yuji Miura Artificial intelligence system for predicting hand-foot skin reaction induced by vascular endothelial growth factor receptor inhibitors Scientific Reports HFSR VEGFR inhibitor Deep learning AI |
| title | Artificial intelligence system for predicting hand-foot skin reaction induced by vascular endothelial growth factor receptor inhibitors |
| title_full | Artificial intelligence system for predicting hand-foot skin reaction induced by vascular endothelial growth factor receptor inhibitors |
| title_fullStr | Artificial intelligence system for predicting hand-foot skin reaction induced by vascular endothelial growth factor receptor inhibitors |
| title_full_unstemmed | Artificial intelligence system for predicting hand-foot skin reaction induced by vascular endothelial growth factor receptor inhibitors |
| title_short | Artificial intelligence system for predicting hand-foot skin reaction induced by vascular endothelial growth factor receptor inhibitors |
| title_sort | artificial intelligence system for predicting hand foot skin reaction induced by vascular endothelial growth factor receptor inhibitors |
| topic | HFSR VEGFR inhibitor Deep learning AI |
| url | https://doi.org/10.1038/s41598-025-93471-x |
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