{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T17:42:40Z","timestamp":1793295760278,"version":"4.5.0"},"reference-count":27,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T00:00:00Z","timestamp":1781654400000},"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>Introduction<\/jats:title>\n                    <jats:p>Breast cancer diagnosis relies on imaging, yet conventional Doppler ultrasound possesses limitations in visualizing tumor microvasculature. This study aimed to compare Microvascular Flow imaging (MV-Flow) with Color Doppler Flow Imaging (CDFI) and evaluate a machine learning (ML) framework integrating MV-Flow parameters for differentiating breast lesions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this prospective study, 101 breast lesions from 79 patients underwent grayscale ultrasound, CDFI, and MV-Flow. Adler grading and the quantitative Vascular Index (VI) were obtained. Based on six features (age, size, CDFI\/MV-Flow Adler grades, VI, BI-RADS), eight ML models were trained (80% data) and tested (20% data). Model performance was evaluated, and SHAP analysis identified key predictors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>MV-Flow demonstrated superior sensitivity, detecting blood flow in 16 lesions missed by CDFI, and showed significantly higher inter-observer agreement (weighted Kappa=0.68 vs. 0.51 for CDFI). The median Vascular Index (VI) was markedly higher in malignant lesions (20.25) compared to benign ones (3.10, P&amp;lt;0.001). The diagnostic AUC for MV-Flow Adler grade, VI alone, and their combination were 0.874, 0.823, and 0.888, respectively. Among eight machine learning models trained on six clinical and sonographic features, the K-Nearest Neighbors model achieved the best performance on the independent test set with an accuracy of 0.927 and an F1-score of 0.947. SHAP analysis identified BI-RADS category and patient age as the most important predictive features in the model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>MV-Flow outperforms CDFI in depicting breast tumor microvasculature. ML models integrating MV-Flow parameters can optimize diagnostic accuracy, offering an objective tool for clinical decision support.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fonc.2026.1827050","type":"journal-article","created":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T04:48:46Z","timestamp":1781671726000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Enhanced differentiation of breast lesions through integration of microvascular flow imaging and machine learning algorithms"],"prefix":"10.3389","volume":"16","author":[{"given":"Fangfang","family":"Zhou","sequence":"first","affiliation":[{"name":"Department of Ultrasound, Hangzhou Women\u2019s Hospital (Hangzhou Maternal and Child Health Care Hospital)","place":["Hangzhou, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wanling","family":"Lin","sequence":"additional","affiliation":[{"name":"Department 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