Development of an Intra- and Peritumoral Radiomics Nomogram Using Digital Breast Tomosynthesis for Preoperative Assessment of Ki-67 Expression in Invasive Breast Cancer

Abstract

Rationale and Objectives

This study aimed to develop a radiomics nomogram model using preoperative digital breast tomosynthesis (DBT) images to predict Ki-67 expression in patients with invasive breast cancer (IBC).

Materials and Methods

This retrospective study involved a cohort of 289 patients with IBC, who were randomly divided into a training dataset (N= 202) and a validation dataset (N= 87). Ki-67 expression was categorized into low and high groups using a 14% threshold. Radiomics features from both the intra- and peritumoral regions of DBT images were used to develop the radiomics model, referred to as Radscore. Clinical and nomogram models were constructed using multivariate logistic regression. The performance of the established models was evaluated using receiver operating characteristic (ROC) curve analysis, calibration curve analysis, decision curve analysis (DCA), net reclassification improvement (NRI), and integrated discrimination improvement (IDI).

Results

The clinical model was constructed using tumor size and DBT-reported lymph node metastasis (DBT_reported_LNM). By integrating Radscore_Combine—which incorporates both intra- and peritumoral radiomics features—along with tumor size and DBT_reported_LNM into the nomogram, the model achieved the highest area under the curve (AUC) values of 0.819 and 0.755 in the training and validation datasets, respectively. The notable improvement shown by the NRI and IDI suggests that Radscore_Combine could serve as a valuable biomarker for predicting Ki-67 expression effectively.

Conclusion

The nomogram offers a non-invasive method to predict Ki-67 expression in IBC patients, which could aid in creating personalized treatment plans.