Habitat-Based Radiomics for Revealing Tumor Heterogeneity and Predicting Residual Cancer Burden Classification in Breast Cancer.
Authors
Affiliations (4)
Affiliations (4)
- Department of Ultrasound, Fujian Medical University Union Hospital, Fuzhou, China.
- Department of Ultrasound, the First Affiliated Hospital, Fujian Medical University, Fuzhou, China; Department of Ultrasound, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
- Department of Pathology, Fujian Medical University Union Hospital, Fuzhou, China.
- Department of Ultrasound, Fujian Medical University Union Hospital, Fuzhou, China. Electronic address: [email protected].
Abstract
To investigate the feasibility of characterizing tumor heterogeneity in breast cancer ultrasound images using habitat analysis technology and establish a radiomics machine learning model for predicting response to neoadjuvant chemotherapy (NAC). Ultrasound images from patients with pathologically confirmed breast cancer who underwent neoadjuvant therapy at our institution between July 2021 and December 2023 were retrospectively reviewed. Initially, the region of interest was delineated and segmented into multiple habitat areas using local feature delineation and cluster analysis techniques. Subsequently, radiomics features were extracted from each habitat area to construct 3 machine learning models. Finally, the model's efficacy was assessed through operating characteristic (ROC) curve analysis, decision curve analysis (DCA), and calibration curve evaluation. A total of 945 patients were enrolled, with 333 demonstrating a favorable response to NAC and 612 exhibiting an unfavorable response to NAC. Through the application of habitat analysis techniques, 3 distinct habitat regions within the tumor were identified. Subsequently, a predictive model was developed by incorporating 19 radiomics features, and all 3 machine learning models demonstrated excellent performance in predicting treatment outcomes. Notably, extreme gradient boosting (XGBoost) exhibited superior performance with an area under the curve (AUC) of 0.872 in the training cohort and 0.740 in the testing cohort. Additionally, DCA and calibration curves were employed for further evaluation. The habitat analysis technique effectively distinguishes distinct biological subregions of breast cancer, while the established radiomics machine learning model predicts NAC response by forecasting residual cancer burden (RCB) classification.