Radiomics-based Machine Learning Approach to Predict Chemotherapy Responses in Colorectal Liver Metastases
Objectives: This study explored the clinical utility of CT radiomics-driven machine learning as a predictive marker for chemotherapy response in colorectal liver metastasis (CRLM) patients. Methods: We included 150 CRLM patients who underwent first-line doublet chemotherapy, dividing them into a tra...
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| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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The Japan Society of Coloproctology
2025-01-01
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| Series: | Journal of the Anus, Rectum and Colon |
| Subjects: | |
| Online Access: | https://www.jstage.jst.go.jp/article/jarc/9/1/9_2024-077/_pdf/-char/en |
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| _version_ | 1832584951815471104 |
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| author | Yuji Miyamoto Takeshi Nakaura Mayuko Ohuchi Katsuhiro Ogawa Rikako Kato Yuto Maeda Kojiro Eto Masaaki Iwatsuki Yoshifumi Baba Toshinori Hirai Hideo Baba |
| author_facet | Yuji Miyamoto Takeshi Nakaura Mayuko Ohuchi Katsuhiro Ogawa Rikako Kato Yuto Maeda Kojiro Eto Masaaki Iwatsuki Yoshifumi Baba Toshinori Hirai Hideo Baba |
| author_sort | Yuji Miyamoto |
| collection | DOAJ |
| description | Objectives: This study explored the clinical utility of CT radiomics-driven machine learning as a predictive marker for chemotherapy response in colorectal liver metastasis (CRLM) patients.
Methods: We included 150 CRLM patients who underwent first-line doublet chemotherapy, dividing them into a training cohort (n=112) and a test cohort (n=38). We manually delineated three-dimensional tumor volumes, selecting the largest liver metastasis for measurement, using pretreatment portal-phase CT images and extracted 107 radiomics features. Treatment response was classified as responder (complete or partial response) or non-responder (stable or progressive disease), based on the best overall response according to RECIST criteria, version 1.1. Employing Random Forest and Boruta algorithms, we identified significant features for responder-non-responder differentiation. Radiomics signatures were developed and validated in the training cohort using five-fold cross-validation, and performance was assessed using the area under the curve (AUC).
Results: Among the patients, 91 (61%) were responders and 59 (39%) were non-responders. Variable selection with Boruta revealed three key parameters (“DependenceVariance,” “ClusterShade,” and “RunVariance”). In the training cohort, individual CT texture parameter AUCs ranged from 0.4 to 0.65, while the machine learning analysis incorporating all valid parameters exhibited a significantly higher AUC of 0.94 (p<0.01). The validation cohort also demonstrated strong predictive accuracy, with an AUC of 0.87 for treatment response.
Conclusions: This study highlights the potential of CT radiomics-driven machine learning in predicting chemotherapy responses among CRLM patients. |
| format | Article |
| id | doaj-art-b34fd309357243e18be36ba351f9b15e |
| institution | Kabale University |
| issn | 2432-3853 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | The Japan Society of Coloproctology |
| record_format | Article |
| series | Journal of the Anus, Rectum and Colon |
| spelling | doaj-art-b34fd309357243e18be36ba351f9b15e2025-01-27T10:02:40ZengThe Japan Society of ColoproctologyJournal of the Anus, Rectum and Colon2432-38532025-01-019111712610.23922/jarc.2024-0772024-077Radiomics-based Machine Learning Approach to Predict Chemotherapy Responses in Colorectal Liver MetastasesYuji Miyamoto0Takeshi Nakaura1Mayuko Ohuchi2Katsuhiro Ogawa3Rikako Kato4Yuto Maeda5Kojiro Eto6Masaaki Iwatsuki7Yoshifumi Baba8Toshinori Hirai9Hideo Baba10Department of Gastroenterological Surgery, Graduate School of Medical Sciences, Kumamoto UniversityDepartment of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto UniversityDepartment of Gastroenterological Surgery, Graduate School of Medical Sciences, Kumamoto UniversityDepartment of Gastroenterological Surgery, Graduate School of Medical Sciences, Kumamoto UniversityDepartment of Gastroenterological Surgery, Graduate School of Medical Sciences, Kumamoto UniversityDepartment of Gastroenterological Surgery, Graduate School of Medical Sciences, Kumamoto UniversityDepartment of Gastroenterological Surgery, Graduate School of Medical Sciences, Kumamoto UniversityDivision of Translational Research and Advanced Treatment Against Gastrointestinal Cancer, Kumamoto UniversityDepartment of Next-Generation Surgical Therapy Development, Kumamoto UniversityDepartment of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto UniversityDepartment of Gastroenterological Surgery, Graduate School of Medical Sciences, Kumamoto UniversityObjectives: This study explored the clinical utility of CT radiomics-driven machine learning as a predictive marker for chemotherapy response in colorectal liver metastasis (CRLM) patients. Methods: We included 150 CRLM patients who underwent first-line doublet chemotherapy, dividing them into a training cohort (n=112) and a test cohort (n=38). We manually delineated three-dimensional tumor volumes, selecting the largest liver metastasis for measurement, using pretreatment portal-phase CT images and extracted 107 radiomics features. Treatment response was classified as responder (complete or partial response) or non-responder (stable or progressive disease), based on the best overall response according to RECIST criteria, version 1.1. Employing Random Forest and Boruta algorithms, we identified significant features for responder-non-responder differentiation. Radiomics signatures were developed and validated in the training cohort using five-fold cross-validation, and performance was assessed using the area under the curve (AUC). Results: Among the patients, 91 (61%) were responders and 59 (39%) were non-responders. Variable selection with Boruta revealed three key parameters (“DependenceVariance,” “ClusterShade,” and “RunVariance”). In the training cohort, individual CT texture parameter AUCs ranged from 0.4 to 0.65, while the machine learning analysis incorporating all valid parameters exhibited a significantly higher AUC of 0.94 (p<0.01). The validation cohort also demonstrated strong predictive accuracy, with an AUC of 0.87 for treatment response. Conclusions: This study highlights the potential of CT radiomics-driven machine learning in predicting chemotherapy responses among CRLM patients.https://www.jstage.jst.go.jp/article/jarc/9/1/9_2024-077/_pdf/-char/enmachine learningct texture analysiscolorectal cancerliver metastaseschemotherapy |
| spellingShingle | Yuji Miyamoto Takeshi Nakaura Mayuko Ohuchi Katsuhiro Ogawa Rikako Kato Yuto Maeda Kojiro Eto Masaaki Iwatsuki Yoshifumi Baba Toshinori Hirai Hideo Baba Radiomics-based Machine Learning Approach to Predict Chemotherapy Responses in Colorectal Liver Metastases Journal of the Anus, Rectum and Colon machine learning ct texture analysis colorectal cancer liver metastases chemotherapy |
| title | Radiomics-based Machine Learning Approach to Predict Chemotherapy Responses in Colorectal Liver Metastases |
| title_full | Radiomics-based Machine Learning Approach to Predict Chemotherapy Responses in Colorectal Liver Metastases |
| title_fullStr | Radiomics-based Machine Learning Approach to Predict Chemotherapy Responses in Colorectal Liver Metastases |
| title_full_unstemmed | Radiomics-based Machine Learning Approach to Predict Chemotherapy Responses in Colorectal Liver Metastases |
| title_short | Radiomics-based Machine Learning Approach to Predict Chemotherapy Responses in Colorectal Liver Metastases |
| title_sort | radiomics based machine learning approach to predict chemotherapy responses in colorectal liver metastases |
| topic | machine learning ct texture analysis colorectal cancer liver metastases chemotherapy |
| url | https://www.jstage.jst.go.jp/article/jarc/9/1/9_2024-077/_pdf/-char/en |
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