基于影像和肿瘤生物标志物的多变量模型用于早期预测三阴性乳腺癌新辅助全身治疗的病理完全缓解

The response of triple-negative breast cancer (TNBC) to neoadjuvant therapy (NAT) varies widely. This study aimed to determine the performance of clinicopathologic biomarkers and volumetric changes on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in predicting pathologic complete response (pCR) to NAT in patients with TNBC.

This study included 264 patients with stage I to III TNBC enrolled in a prospective clinical trial. These patients underwent DCE-MRI at baseline and after two and/or after four cycles of dose-dense anthracycline and cyclophosphamide. Tumor volume (TV) was calculated by measuring three tumor dimensions at each time point. Clinicopathologic markers were analyzed. Treatment response at surgery (pCR v non-pCR) was documented. The patients were randomly assigned to discovery and validation cohorts. Multiple logistic regression and receiver operating characteristic analysis were used to assess associations and build predictive models.

Of the 264 patients, 124 (47%) achieved a pCR. The optimal thresholds for TV reduction (TVR) on DCE-MRI were ≥60% after two cycles and ≥90% after four cycles. TVR, Ki-67, and stromal tumor-infiltrating lymphocytes (sTILs) were independently associated with pCR on univariable analysis. A combined model including TVR ≥60% after two cycles, sTILs, and Ki-67 predicted pCR with an AUC of 0.84 (90% CI. 0.76 to 0.92) in the discovery cohort and 0.80 (95% CI, 0.71 to 0.88) in the validation cohort. A combined model including TVR ≥90% after four cycles, sTILs, and Ki-67 predicted pCR with an AUC of 0.79 (90% CI, 0.71 to 0.86) in the discovery cohort and 0.80 (95% CI, 0.72 to 0.88) in the validation cohort.

A model incorporating imaging and clinicopathologic variables showed good performance in predicting pCR.