Utilizing radiomics and dosiomics with AI for precision prediction of radiation dermatitis in breast cancer patients.

Autor: Lee TF; Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, Jiangong RdSanmin Dist., No.415, Kaohsiung, 80778, Taiwan, ROC.; Graduate Institute of Clinical Medicine, Kaohsiung Medical University, Kaohsiung, 807, Taiwan, ROC.; Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung, 80708, Taiwan, ROC., Chang CH; Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, Jiangong RdSanmin Dist., No.415, Kaohsiung, 80778, Taiwan, ROC., Chi CH; Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, Jiangong RdSanmin Dist., No.415, Kaohsiung, 80778, Taiwan, ROC., Liu YH; Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, Jiangong RdSanmin Dist., No.415, Kaohsiung, 80778, Taiwan, ROC., Shao JC; Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, Jiangong RdSanmin Dist., No.415, Kaohsiung, 80778, Taiwan, ROC., Hsieh YW; Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, Jiangong RdSanmin Dist., No.415, Kaohsiung, 80778, Taiwan, ROC., Yang PY; Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, Jiangong RdSanmin Dist., No.415, Kaohsiung, 80778, Taiwan, ROC., Tseng CD; Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, Jiangong RdSanmin Dist., No.415, Kaohsiung, 80778, Taiwan, ROC., Chiu CL; Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, Jiangong RdSanmin Dist., No.415, Kaohsiung, 80778, Taiwan, ROC., Hu YC; Department of Radiation Oncology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan, ROC., Lin YW; Department of Radiation Oncology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan, ROC., Chao PJ; Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, Jiangong RdSanmin Dist., No.415, Kaohsiung, 80778, Taiwan, ROC. pjchao99@gmail.com., Lee SH; Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, Jiangong RdSanmin Dist., No.415, Kaohsiung, 80778, Taiwan, ROC. tflee@nkust.edu.tw.; Department of Radiation Oncology, Linkou Chang Gung Memorial Hospitaland, Chang Gung University College of Medicine, Linkou, Taiwan, ROC. tflee@nkust.edu.tw., Yeh SA; Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Science and Technology, Jiangong RdSanmin Dist., No.415, Kaohsiung, 80778, Taiwan, ROC. sayeh@outlook.com.; Department of Medical Imaging and Radiological Sciences, I-Shou University, Kaohsiung, 82445, Taiwan, ROC. sayeh@outlook.com.; Department of Radiation Oncology, E-DA Hospital, Kaohsiung, 82445, Taiwan, ROC. sayeh@outlook.com.
Jazyk: angličtina
Zdroj: BMC cancer [BMC Cancer] 2024 Aug 06; Vol. 24 (1), pp. 965. Date of Electronic Publication: 2024 Aug 06.
DOI: 10.1186/s12885-024-12753-1
Abstrakt: Purpose: This study explores integrating clinical features with radiomic and dosiomic characteristics into AI models to enhance the prediction accuracy of radiation dermatitis (RD) in breast cancer patients undergoing volumetric modulated arc therapy (VMAT).
Materials and Methods: This study involved a retrospective analysis of 120 breast cancer patients treated with VMAT at Kaohsiung Veterans General Hospital from 2018 to 2023. Patient data included CT images, radiation doses, Dose-Volume Histogram (DVH) data, and clinical information. Using a Treatment Planning System (TPS), we segmented CT images into Regions of Interest (ROIs) to extract radiomic and dosiomic features, focusing on intensity, shape, texture, and dose distribution characteristics. Features significantly associated with the development of RD were identified using ANOVA and LASSO regression (p-value < 0.05). These features were then employed to train and evaluate Logistic Regression (LR) and Random Forest (RF) models, using tenfold cross-validation to ensure robust assessment of model efficacy.
Results: In this study, 102 out of 120 VMAT-treated breast cancer patients were included in the detailed analysis. Thirty-two percent of these patients developed Grade 2 + RD. Age and BMI were identified as significant clinical predictors. Through feature selection, we narrowed down the vast pool of radiomic and dosiomic data to 689 features, distributed across 10 feature subsets for model construction. In the LR model, the J subset, comprising DVH, Radiomics, and Dosiomics features, demonstrated the highest predictive performance with an AUC of 0.82. The RF model showed that subset I, which includes clinical, radiomic, and dosiomic features, achieved the best predictive accuracy with an AUC of 0.83. These results emphasize that integrating radiomic and dosiomic features significantly enhances the prediction of Grade 2 + RD.
Conclusion: Integrating clinical, radiomic, and dosiomic characteristics into AI models significantly improves the prediction of Grade 2 + RD risk in breast cancer patients post-VMAT. The RF model analysis demonstrates that a comprehensive feature set maximizes predictive efficacy, marking a promising step towards utilizing AI in radiation therapy risk assessment and enhancing patient care outcomes.
(© 2024. The Author(s).)
Databáze: MEDLINE
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