{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T04:46:05Z","timestamp":1782794765759,"version":"3.54.5"},"reference-count":18,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T00:00:00Z","timestamp":1782777600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>To develop and validate a multimodal habitat radiomics model integrating automated breast volume scanning (ABVS) and conventional two-dimensional ultrasound (2D-US) for risk stratification of biopsy-selected BI-RADS 4A breast lesions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This retrospective single-center study included 160 consecutive patients with BI-RADS 4A breast lesions confirmed by histopathology between January 2024 and May 2025. Tumoral and peritumoral regions were manually segmented on ABVS and 2D-US images. Habitat subregions were generated using a local spatial autocorrelation-based heterogeneity analysis. Radiomic features were extracted using PyRadiomics. Feature selection was performed using t-test filtering, Pearson correlation analysis, and least absolute shrinkage and selection operator (LASSO) regression within the training folds. Multiple machine learning classifiers were constructed using three-fold cross-validation. Model performance was evaluated using area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, F1 score, calibration curves, and decision curve analysis (DCA).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Of the 160 lesions, 51 (31.9%) were malignant and 109 (68.1%) were benign. The habitat radiomics model outperformed the clinical-ultrasound model. The optimal multilayer perceptron classifier achieved an AUC of 0.910 and an accuracy of 0.869 in the validation cohort. Decision curve analysis demonstrated higher net benefit across a range of threshold probabilities, and calibration curves indicated good agreement between predicted and observed outcomes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Multimodal habitat radiomics integrating ABVS and conventional ultrasound demonstrated promising performance for risk stratification of biopsy-selected BI-RADS 4A lesions and may provide supportive information for individualized clinical decision-making. Further prospective multicenter validation is warranted before clinical application.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fonc.2026.1828818","type":"journal-article","created":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T04:24:26Z","timestamp":1782793466000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Multimodal habitat radiomics based on automated breast volume scanning and conventional ultrasound for risk stratification of biopsy-confirmed BI-RADS 4A breast lesions"],"prefix":"10.3389","volume":"16","author":[{"given":"Fei","family":"Xia","sequence":"first","affiliation":[{"name":"Department of Ultrasound, WuHu Hospital, East China Normal University (The Second People\u2019s Hospital, WuHu)","place":["Wuhu, Anhui, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Ultrasound, 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