Development and Validation of a New Immune-Inflammatory-Nutritional Score to Predict Pathological Complete Response...

To construct a novel immune-inflammatory-nutritional (IIN) score based on peripheral blood biomarkers related to inflammation, immunity, and nutrition, and to predict the efficacy of neoadjuvant chemotherapy (NAC) in patients with triple-negative breast cancer (TNBC).

We retrospectively selected 431 patients with TNBC from Xijing Hospital, and then randomly divided the patients into a training set and an internal validation set in a ratio of 7:3. An external validation set was included with 154 patients selected from West China Hospital of Sichuan University. In the training set, patients were divided into the pathological complete response (pCR) group and the non-pathological complete response group. Univariate logistic regression analysis and LASSO regression analysis were used to select biomarkers that affect the efficacy of NAC in TNBC patients and to construct the IIN score. A nomogram model was constructed based on the IIN score and clinical pathological characteristics to predict whether TNBC patients could achieve pCR after NAC before treatment. The predictive performance and clinical application value of the nomogram model were assessed using the receiver operating characteristic (ROC) curve, calibration curve, decision curve analysis, and confusion matrix.

Through LASSO regression analysis, 6 biomarkers were ultimately identified to construct the scoring system. A nomogram model was constructed based on the IIN score and clinical pathological characteristics, and the ROC curve showed the areas under the curve to be 0.827, 0.786, and 0.754 in the training set, internal validation set, and external validation set, respectively. Calibration curves, decision curves, and confusion matrices all demonstrated that the nomogram model exhibited robust predictive performance and holds certain clinical application value.

The nomogram model based on the IIN score offers high predictive performance and can accurately predict the efficacy of NAC in TNBC patients before treatment, highlighting its clinical application potential.