An Ultrasound-Based Radiomics Nomogram for Differentiation of Triple-Negative Breast Cancer From Fibroadenoma

Abstract Background: To develop a radiomics nomogram that incorporates the radiomics features and ultrasound (US) conventional features and clinical findings to differentiate triple-negative breast cancer (TNBC) from fibroadenoma.Methods: A total of 182 pathology-proven fibroadenomas and 178 pathology-proven TNBCs which underwent preoperative US examination were involved and randomly divided into training (n = 253) and validation cohorts (n = 107). The regions of interest (ROIs) of all lesions were delineated based on preoperative US examination subsequently radiomics features extracted. The least absolute shrinkage and selection operator model and the maximum relevance minimum redundancy algorithm were established for the selection of tumor status-related features and construction of radiomics signature (Rad-score). Then, multivariate logistic regression analyses were utilized to develop a radiomics model by incorporating the radiomics signature and clinical findings. Finally, the usefulness of the combined nomogram was assessed by the receiver operator characteristic curve (ROC), calibration curve, and decision curve analysis (DCA).Results: The radiomics signature, composing of twelve selected features, achieved good diagnostic performance. The nomogram incorporated with radiomics signature and clinical data showed favorable diagnostic efficacy in the training cohort (AUC 0.986, 95% CI, 0.975-0.997) and validation cohort (AUC 0.977, 95% CI, 0.953-1.000). The radiomics nomogram outperformed the Rad-score and clinical models alone (p < 0.05). The calibration curve and decision curve analysis demonstrated the better clinical utility of the combined radiomics nomogram.Conclusions: The radiomics signature is a potential predictive indicator for differentiating TNBC and fibroadenoma. The radiomics nomogram associated with Rad-score, US conventional features, and clinical data outperformed the Rad-score and clinical models alone.Advances in knowledge: Recent advances in radiomics-based US identified increasingly gaining ground in oncology to improve diagnosis, assessment of therapeutic response, and disease prediction. We revealed that the Rad-score is an independent predictive indicator for differentiating TNBC and fibroadenoma. The radiomics nomogram associated with Rad-score, US conventional features, and clinical data outperformed the Rad-score and clinical models alone.