Artificial intelligence for predicting five-year survival in stage IV metastatic breast cancer patients: A focus on sarcopenia and other host factors
We developed an artificial intelligence (AI) model that can predict five-year survival in patients with stage IV metastatic breast cancer, mainly based on host factors and sarcopenia. From a prospectively built breast cancer registry, a total of 210 metastatic breast cancer patients were selected in...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-09-01
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| Series: | Frontiers in Physiology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphys.2022.977189/full |
| _version_ | 1818033146986233856 |
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| author | Woocheol Jang Woocheol Jang Changwon Jeong KyungA Kwon KyungA Kwon Tae In Yoon Onvox Yi Kyung Won Kim Seoung-Oh Yang Jinseok Lee |
| author_facet | Woocheol Jang Woocheol Jang Changwon Jeong KyungA Kwon KyungA Kwon Tae In Yoon Onvox Yi Kyung Won Kim Seoung-Oh Yang Jinseok Lee |
| author_sort | Woocheol Jang |
| collection | DOAJ |
| description | We developed an artificial intelligence (AI) model that can predict five-year survival in patients with stage IV metastatic breast cancer, mainly based on host factors and sarcopenia. From a prospectively built breast cancer registry, a total of 210 metastatic breast cancer patients were selected in a consecutive manner using inclusion/exclusion criteria. The patients’ data were divided into two categories: a group that survived for more than 5 years and a group that did not survive for 5 years. For the AI model input, 11 features were considered, including age, body mass index, skeletal muscle area (SMA), height-relative SMA (H-SMI), height square-relative SMA (H2-SMA), weight-relative SMA (W-SMA), muscle mass, anticancer chemotherapy, radiation therapy, and comorbid diseases such as hypertension and mellitus. For the feature importance analysis, we compared classifiers using six different machine learning algorithms and found that extreme gradient boosting (XGBoost) provided the best accuracy. Subsequently, we performed the feature importance analysis based on XGBoost and proposed a 4-layer deep neural network, which considered the top 10 ranked features. Our proposed 4-layer deep neural network provided high sensitivity (75.00%), specificity (78.94%), accuracy (78.57%), balanced accuracy (76.97%), and an area under receiver operating characteristics of 0.90. We generated a web application for anyone to easily access and use this AI model to predict five-year survival. We expect this web application to be helpful for patients to understand the importance of host factors and sarcopenia and achieve survival gain. |
| first_indexed | 2024-12-10T06:18:38Z |
| format | Article |
| id | doaj.art-20ef84cb949c407ead631c0f27900913 |
| institution | Directory Open Access Journal |
| issn | 1664-042X |
| language | English |
| last_indexed | 2024-12-10T06:18:38Z |
| publishDate | 2022-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physiology |
| spelling | doaj.art-20ef84cb949c407ead631c0f279009132022-12-22T01:59:24ZengFrontiers Media S.A.Frontiers in Physiology1664-042X2022-09-011310.3389/fphys.2022.977189977189Artificial intelligence for predicting five-year survival in stage IV metastatic breast cancer patients: A focus on sarcopenia and other host factorsWoocheol Jang0Woocheol Jang1Changwon Jeong2KyungA Kwon3KyungA Kwon4Tae In Yoon5Onvox Yi6Kyung Won Kim7Seoung-Oh Yang8Jinseok Lee9Department of Biomedical Engineering, Kyung Hee University, Yongin, South KoreaDepartment of Electronics and Information Convergence Engineering, Kyung Hee University, Yongin, South KoreaMedical Convergence Research Center, Smart Business Team in Information Management Office, Wonkwang University Hospital, Wonkwang University, Iksan, South KoreaDepartment of Nuclear Medicine, Dongnam Institute of Radiological and Medical Sciences, Busan, South KoreaDepartment of Hemato-Oncology, Dongnam Institute of Radiological and Medical Sciences, Busan, South KoreaDepartment of Surgery, Dongnam Institute of Radiological and Medical Sciences, Busan, South KoreaDepartment of Surgery, Dongnam Institute of Radiological and Medical Sciences, Busan, South KoreaThe Department of Radiology and Research Institute of Radiology, Asan Image Metrics, Clinical Trial Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul, KoreaDepartment of Nuclear Medicine, Dongnam Institute of Radiological and Medical Sciences, Busan, South KoreaDepartment of Biomedical Engineering, Kyung Hee University, Yongin, South KoreaWe developed an artificial intelligence (AI) model that can predict five-year survival in patients with stage IV metastatic breast cancer, mainly based on host factors and sarcopenia. From a prospectively built breast cancer registry, a total of 210 metastatic breast cancer patients were selected in a consecutive manner using inclusion/exclusion criteria. The patients’ data were divided into two categories: a group that survived for more than 5 years and a group that did not survive for 5 years. For the AI model input, 11 features were considered, including age, body mass index, skeletal muscle area (SMA), height-relative SMA (H-SMI), height square-relative SMA (H2-SMA), weight-relative SMA (W-SMA), muscle mass, anticancer chemotherapy, radiation therapy, and comorbid diseases such as hypertension and mellitus. For the feature importance analysis, we compared classifiers using six different machine learning algorithms and found that extreme gradient boosting (XGBoost) provided the best accuracy. Subsequently, we performed the feature importance analysis based on XGBoost and proposed a 4-layer deep neural network, which considered the top 10 ranked features. Our proposed 4-layer deep neural network provided high sensitivity (75.00%), specificity (78.94%), accuracy (78.57%), balanced accuracy (76.97%), and an area under receiver operating characteristics of 0.90. We generated a web application for anyone to easily access and use this AI model to predict five-year survival. We expect this web application to be helpful for patients to understand the importance of host factors and sarcopenia and achieve survival gain.https://www.frontiersin.org/articles/10.3389/fphys.2022.977189/fullbreast cancerartificial intelligencefeature importancesarcopeniafive-year survival |
| spellingShingle | Woocheol Jang Woocheol Jang Changwon Jeong KyungA Kwon KyungA Kwon Tae In Yoon Onvox Yi Kyung Won Kim Seoung-Oh Yang Jinseok Lee Artificial intelligence for predicting five-year survival in stage IV metastatic breast cancer patients: A focus on sarcopenia and other host factors Frontiers in Physiology breast cancer artificial intelligence feature importance sarcopenia five-year survival |
| title | Artificial intelligence for predicting five-year survival in stage IV metastatic breast cancer patients: A focus on sarcopenia and other host factors |
| title_full | Artificial intelligence for predicting five-year survival in stage IV metastatic breast cancer patients: A focus on sarcopenia and other host factors |
| title_fullStr | Artificial intelligence for predicting five-year survival in stage IV metastatic breast cancer patients: A focus on sarcopenia and other host factors |
| title_full_unstemmed | Artificial intelligence for predicting five-year survival in stage IV metastatic breast cancer patients: A focus on sarcopenia and other host factors |
| title_short | Artificial intelligence for predicting five-year survival in stage IV metastatic breast cancer patients: A focus on sarcopenia and other host factors |
| title_sort | artificial intelligence for predicting five year survival in stage iv metastatic breast cancer patients a focus on sarcopenia and other host factors |
| topic | breast cancer artificial intelligence feature importance sarcopenia five-year survival |
| url | https://www.frontiersin.org/articles/10.3389/fphys.2022.977189/full |
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