Ultrasound- based radiomics nomogram for differentiation of triple- negative breast cancer from fibroadenoma

Objective: This study aimed to develop a radiomics nomogram that incorporates radiomics, conventional ultrasound (US) and clinical features in order to differen-tiate triple-negative breast cancer (TNBC) from fibroad-enoma.Methods: A total of 182 pathology-proven fibroade-nomas and 178 pathology-proven TNBCs, which under-went preoperative US examination, were involved and randomly divided into training (n = 253) and validation cohorts (n = 107). The radiomics features were extracted from the regions of interest of all lesions, which were delineated on the basis of preoperative US examination. The least absolute shrinkage and selection operator model and the maximum relevance minimum redun-dancy algorithm were established for the selection of tumor status-related features and construction of radi-omics 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 using the receiver oper-ator characteristic curve, calibration curve, and decision curve analysis (DCA).Results: The radiomics signature, composed of 12 selected features, achieved good diagnostic perfor-mance. 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 (p < 0.05). The cali-bration curve and DCA demonstrated the good clinical utility of the combined radiomics nomogram.Conclusion: The radiomics signature is a poten-tial 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. Advances in knowledge: Recent advances in radiomics- based US are increasingly showing poten-tial for improved diagnosis, assessment of thera-peutic response and disease prediction in oncology. Rad- score is an independent predictive indicator for differentiating TNBC and fibroadenoma. The radiomics nomogram associated with Rad-score, US conven-tional features, and clinical data outperformed the Rad- score and clinical models.