Frontiers in oncology · 2026
Enhanced differentiation of breast lesions through integration of microvascular flow imaging and machine learning algorithms.
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Zhou Fangfang, Lin Wanling, Yao Jiqin, Lu Xiaoxi, Yu Lifang
Аннотация
INTRODUCTION: 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.
METHODS: 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.
RESULTS: 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<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.
CONCLUSION: 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.
METHODS: 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.
RESULTS: 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<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.
CONCLUSION: 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.