| Title: |
Value of intra- and peritumoral ultrasound radiomics for predicting axillary lymph node burden in breast cancer |
| Authors: |
Mo-Han Hao; Fan Zhang; Cong Zhang; Naijing Shi; Weina Mu |
| Source: |
Frontiers in Oncology, Vol 15 (2026) |
| Publisher Information: |
Frontiers Media S.A., 2026. |
| Publication Year: |
2026 |
| Collection: |
LCC:Neoplasms. Tumors. Oncology. Including cancer and carcinogens |
| Subject Terms: |
breast cancer; axillary lymph node burden; radiomics; ultrasound; peritumoral region; Neoplasms. Tumors. Oncology. Including cancer and carcinogens; RC254-282 |
| Description: |
ObjectiveTo evaluate ultrasound-based radiomic features, derived from both intratumoral and peritumoral regions, for noninvasive preoperative prediction of axillary lymph node(ALN) burden in breast cancer.MethodsThis retrospective study analyzed data from 300 pathologically confirmed breast cancer patients undergoing preoperative ultrasound. The cohort was randomly divided into a training set (n = 210) and a testing set (n = 90) at a 7∶3 ratio. Primary tumor regions of interest (ROIs) were manually delineated on preoperative ultrasound images using ITK-SNAP. Peritumoral ROIs were generated by radially expanding the intratumoral ROI by 2mm, 3mm, and 4mm. A comprehensive set of radiomic features was extracted from each ROI, with feature selection via LASSO based methods. Six machine-learning classifiers were trained on intratumoral features to identify the optimal algorithm. Using this algorithm, we built: (1) A radiomics model based solely on intratumoral or peritumoral features. (2) Combined models incorporating intratumoral and peritumoral features at each expansion margin (2mm, 3mm, and 4mm). The best-performing radiomics model was then integrated with significant clinical and conventional imaging variables to construct a composite nomogram. Model discrimination was evaluated by area under the receiver operating characteristic curve (AUC), calibration was assessed via calibration curves, and clinical utility was appraised using decision curve analysis (DCA). Model interpretability was facilitated through Shapley additive explanation (SHAP) values and visualized in a nomogram.ResultsA Random Forest classifier applied to combined intratumoral and 3mm peritumoral features yielded the highest AUCs (training set: 0.825; testing set: 0.746). Multivariable logistic regression identified lesion location and ultrasonographic axillary lymph node status as independent clinical predictors (p |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2234-943X |
| Relation: |
https://www.frontiersin.org/articles/10.3389/fonc.2025.1674922/full; https://doaj.org/toc/2234-943X |
| DOI: |
10.3389/fonc.2025.1674922 |
| Access URL: |
https://doaj.org/article/14add0ec79fb417d87d2b2575ce0d42b |
| Accession Number: |
edsdoj.14add0ec79fb417d87d2b2575ce0d42b |
| Database: |
Directory of Open Access Journals |