AUTHOR=Xu Jing , Qi Pan , Ou Xiaoyan , Zhong Qiaoxin , Chen Zhen-Wen , Wang Yin , Yi Aijiao , Wang Bin 

TITLE=Bi-modal ultrasound radiomics and habitat analysis enhanced the pre-operative prediction of axillary lymph node burden in patients with early-stage breast cancer

JOURNAL=Frontiers in Oncology

VOLUME=Volume 15 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1607442

DOI=10.3389/fonc.2025.1607442

ISSN=2234-943X

ABSTRACT=ObjectiveThis study aimed to evaluate the value of habitat analysis based bi-modal ultrasound radiomics in predicting axillary lymph node (ALN) status in patients with early-stage breast cancer, and find a non-invasive and accurate method to predict ALN status.Materials and methodsA total of 206 patients with 206 breast lesions were enrolled in this study from July 2019 to December 2023. All patients were randomly divided into training cohort (165 patients) and test cohort (41 patients). The feature extraction was manually delineated with ITK-SNAP software, while a K-means clustering algorithm was employed for the segmentation of sub-regions, with the number of clusters ranging from 2 to 10. Radiomic features were extracted separately from the subregions of B-mode ultrasound (BMUS) and shear wave elastography (SWE) images after habitat generation. These modality-specific features were then combined. Eleven machine learning models were used to build models, including support vector machines (SVM), k-nearest neighbor (KNN), RandomForest (RF), ExtraTrees, XGBoost, light gradient boosting machine (LGB), NaiveBayes, AdaBoost, GradientBoosting, LR and MLP. Prediction performance was compared among clinicopathological model, omics models and habitat models.ResultsAccording to the habitat analysis results of K clustering for BMUS and SWE, the omics features of 4 subregions for BMUS images and the 5 subregions for SWE images were extracted respectively. Compared the prediction performance of the clinicopathologic (C) risk factors model, habitat and omics models in the test cohort, NaiveBayes model based on SWE habitat achieved the highest prediction performance with AUC of 0.953 (95% CI: 0.893, 1.000)ConclusionHabitat analysis based on ultrasound might be a potential method to visualize the intratumoral heterogeneity of breast lesions. The machine learning models based on SWE radiomics with habitat analysis could enhance the ability of prediction lymph node burden in patients with early-stage breast cancer, which could be a promising approach to make clinical decisions.