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Prediction of Residual Axillary Lymph Node Metastasis Following Neoadjuvant Therapy for Breast Cancer: Habitat
Junjie Zhang1, Yi Dai2, Ruxin Xu1
1Department of Radiology, Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, China.
Rationale And Objectives:
To develop and validate a habitat radiomics model based on pretreatment breast magnetic resonance imaging (MRI) in predicting residual axillary lymph node metastasis (RALNM) after neoadjuvant therapy (NAT) in patients with clinically node-positive (cN+) breast cancer.
Materials And Methods:
This retrospective study included patients with cN+ breast cancer who underwent NAT at two centers between March 2018 and December 2022. Tumor region on pretreatment dynamic contrast-enhanced (DCE) MRI was segmented into distinct habitats using K-means clustering, and radiomics features were extracted from each habitat to build a habitat radiomics model. The conventional radiomics model was developed for comparison. Furthermore, a combined model integrating clinicopathological features with habitat radiomics signature was built. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC).
Results:
Among 686 included women (mean age ± standard deviation, 49.38 ± 10.37 years), RALNM was present in 351 patients (51.17%) after NAT. The habitat radiomics model achieved AUCs of 0.805 (95% confidence interval [CI]: 0.739-0.872) in the internal validation set and 0.802 (95% CI: 0.734-0.869) in the external test set, significantly outperforming the whole-tumor radiomics model in all cohorts (all p < 0.05). Integration of habitat radiomics with independent clinicopathological predictors yielded a combined model with further improved AUCs of 0.871 (95% CI: 0.816-0.926) and 0.857 (95% CI: 0.800-0.915) in the validation and external test sets, respectively.
Conclusion:
Habitat radiomics analysis of pretreatment breast DCE-MRI demonstrates promising performance for predicting RALNM after NAT. The combined model incorporating habitat radiomics and clinicopathological factors further improves predictive accuracy.
