Radiomics Nomogram Based on Dual‐Sequence MRI for Assessing Ki‐67 Expression in Breast Cancer

Background Radiomics has been extensively applied in predicting Ki‐67 in breast cancer (BC). However, this is often confined to the exploration of a single sequence, without considering the varying sensitivity and specificity among different sequences. Purpose To develop a nomogram based on dual‐sequence MRI derived radiomic features combined with clinical characteristics for assessing Ki‐67 expression in BC. Study Type Retrospective. Population 227 females (average age, 51 years) with 233 lesions and pathologically confirmed BC, which were divided into the training set ( n = 163) and test set ( n = 70). Field Strength/Sequence 3.0‐T, T1‐weighted dynamic contrast‐enhanced MRI (DCE‐MRI) and apparent diffusion coefficient (ADC) maps from diffusion‐weighted MRI (EPI sequence). Assessment The regions of interest were manually delineated on ADC and DCE‐MRI sequences. Three radiomics models of ADC, DCE‐MRI, and dsMRI (combined ADC and DCE‐MRI sequences) were constructed by logistic regression and the radiomics score (Radscore) of the best model was calculated. The correlation between Ki‐67 expression and clinical characteristics such as receptor status, axillary lymph node (ALN) metastasis status, ADC value, and time signal intensity curve was analyzed, and the clinical model was established. The Radscore was combined with clinical predictors to construct a nomogram. Statistical Tests The independent sample t ‐test, Mann–Whitney U test, Chi‐squared test, Interclass correlation coefficients (ICCs), single factor analysis, least absolute shrinkage and selection operator (LASSO), logistic regression, receiver operating characteristics, Delong test, Hosmer_Lemeshow test, calibration curve, decision curve. A P ‐value <0.05 was considered statistically significant. Results In the test set, the prediction efficiency of the dsMRI model (AUC = 0.862) was higher than ADC model (AUC = 0.797) and DCE‐MRI model (AUC = 0.755). With the inclusion of estrogen receptor (ER) and ALN metastasis, the nomogram displayed quality improvement (AUC = 0.876), which was superior to the clinical model (AUC = 0.787) and radiomics model. Data Conclusion The nomogram based on dsMRI radiomic features and clinical characteristics may be able to assess Ki‐67 expression in BC. Level of Evidence 3 Technical Efficacy Stage 3