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

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). This study involved a retrospective analysis of 12...

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Bibliographic Details
Published in:BMC cancer 2024-08, Vol.24 (1), p.965-17, Article 965
Main Authors: Lee, Tsair-Fwu, Chang, Chu-Ho, Chi, Chih-Hsuan, Liu, Yen-Hsien, Shao, Jen-Chung, Hsieh, Yang-Wei, Yang, Pei-Ying, Tseng, Chin-Dar, Chiu, Chien-Liang, Hu, Yu-Chang, Lin, Yu-Wei, Chao, Pei-Ju, Lee, Shen-Hao, Yeh, Shyh-An
Format: Article
Language:English
Subjects:
Accuracy
Adult
Aged
Artificial Intelligence
Breast cancer
Breast Neoplasms - diagnostic imaging
Breast Neoplasms - radiotherapy
Cancer
Cancer patients
Cancer therapies
Care and treatment
Clinical outcomes
Complications and side effects
Computed tomography
CT imaging
Data collection
Dermatitis
Diagnostic imaging
Dosiomics
Erythema
Feature selection
Female
Health aspects
Hospitals
Humans
Inflammation
Information systems
Lymphatic system
Medical imaging
Medical imaging equipment
Middle Aged
Oncology
Oncology, Experimental
Patients
Predictions
Radiation
Radiation dermatitis
Radiation therapy
Radiodermatitis - diagnostic imaging
Radiodermatitis - etiology
Radiomics
Radiotherapy
Radiotherapy Dosage
Radiotherapy Planning, Computer-Assisted - methods
Radiotherapy, Intensity-Modulated - adverse effects
Radiotherapy, Intensity-Modulated - methods
Regression analysis
Retrospective Studies
Risk assessment
Skin
Tomography
Tomography, X-Ray Computed - methods
Wavelet transforms
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Description
Summary: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). 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 
ISSN:1471-2407
1471-2407
DOI:10.1186/s12885-024-12753-1