Dataset Information

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

ABSTRACT:

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.

SUBMITTER: Lee TF

PROVIDER: S-EPMC11304569 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature

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Publications

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

Lee Tsair-Fwu TF Chang Chu-Ho CH Chi Chih-Hsuan CH Liu Yen-Hsien YH Shao Jen-Chung JC Hsieh Yang-Wei YW Yang Pei-Ying PY Tseng Chin-Dar CD Chiu Chien-Liang CL Hu Yu-Chang YC Lin Yu-Wei YW Chao Pei-Ju PJ Lee Shen-Hao SH Yeh Shyh-An SA

BMC cancer 20240806 1

<h4>Purpose</h4>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).<h4>Materials and methods</h4>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 ...[more]

PMID: 39107701

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Dataset Information

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

Purpose

Materials and methods

Results

Conclusion

Publications

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

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