A nomogram to predict the pathological complete response in patients with breast cancer based on the TILs-US score.

Abstract

The tumor-infiltrating lymphocytes-ultrasonography score is a calculation system for predicting lymphocyte-predominant breast cancers in surgical specimens. A nomogram based on the tumor-infiltrating lymphocytes-ultrasonography score was developed to predict the pathological complete response in breast cancer treated with neoadjuvant chemotherapy. A retrospective evaluation was conducted on 118 patients with breast cancer treated with neoadjuvant chemotherapy at Hiroshima University Hospital. Tumor-infiltrating lymphocytes-ultrasonography scores ≥4 were classified as high. A nomogram was developed using a stepwise logistic regression model for pathological complete response (ypT0 ypN0), based on the smallest Akaike information criterion. The predictive ability and clinical usefulness of the nomogram were also evaluated. Among 118 patients, 34 (28.8%) achieved a pathological complete response, and 52 (44.1%) exhibited high tumor-infiltrating lymphocytes-ultrasonography. In multivariate logistic regression analysis, high tumor-infiltrating lymphocytes-ultrasonography (odds ratio, 6.01; P<0.001), clinical complete response (odds ratio, 4.83; P=0.004) and hormone receptor (odds ratio, 3.48; P=0.038) were independent predictors of pathological complete response. A nomogram based on tumor-infiltrating lymphocytes-ultrasonography score, clinical complete response, hormone receptor and clinical N status was developed. The nomogram showed an area under the curve of 0.831 and a bias-corrected area under the curve of 0.809. The calibration plot showed a good fit between the expected and actual pathological complete response values. Decision curve analysis also showed the clinical utility of the nomogram for predicting pathological complete responses. A nomogram based on the tumor-infiltrating lymphocytes-ultrasonography score exhibited a favorable predictive ability for pathological complete response in patients with breast cancer, which can be useful in predicting the residual disease status after neoadjuvant chemotherapy.