"Construction and validation of a prognostic nutritional index-based nomogram for predicting pathological complete response in breast cancer: a two-center study of 1,170 patients"

作者全名："Qu, Fanli; Luo, Yaxi; Peng, Yang; Yu, Haochen; Sun, Lu; Liu, Shengchun; Zeng, Xiaohua"

作者地址："[Qu, Fanli; Zeng, Xiaohua] Chongqing Univ, Canc Hosp, Dept Breast Canc Ctr, Chongqing, Peoples R China; [Qu, Fanli; Peng, Yang; Yu, Haochen; Liu, Shengchun] Chongqing Med Univ, Affiliated Hosp 1, Dept Breast & Thyroid Surg, Chongqing, Peoples R China; [Luo, Yaxi] Chongqing Med Univ, Affiliated Hosp 2, Dept Rehabil, Chongqing, Peoples R China; [Sun, Lu] Sun Yat sen Univ, Affiliated Hosp 8, Dept Thyroid & Breast Surg, Shenzhen, Guangdong, Peoples R China"

通信作者："Zeng, XH (通讯作者)，Chongqing Univ, Canc Hosp, Dept Breast Canc Ctr, Chongqing, Peoples R China.; Liu, SC (通讯作者)，Chongqing Med Univ, Affiliated Hosp 1, Dept Breast & Thyroid Surg, Chongqing, Peoples R China."

来源：FRONTIERS IN IMMUNOLOGY

ESI学科分类：IMMUNOLOGY

WOS号：WOS:001148275600001

JCR分区：Q1

影响因子：7.3

年份：2024

卷号：14

期号：　

开始页：　

结束页：　

文献类型：Article

关键词：breast cancer; prognostic nutritional index; neoadjuvant chemotherapy; nomogram; pathological complete response

摘要："BackgroundPathological complete response (pCR) after neoadjuvant chemotherapy (NAC) is associated with favorable outcomes in breast cancer patients. Identifying reliable predictors for pCR can assist in selecting patients who will derive the most benefit from NAC. The prognostic nutritional index (PNI) serves as an indicator of nutritional status and systemic immune competence. It has emerged as a prognostic biomarker in several malignancies; however, its predictive value for pCR in breast cancer remains uncertain. The objective of this study is to assess the predictive value of pretreatment PNI for pCR in breast cancer patients.MethodsA total of 1170 patients who received NAC in two centers were retrospectively analyzed. The patients were divided into three cohorts: a training cohort (n=545), an internal validation cohort (n=233), and an external validation cohort (n=392). Univariate and multivariate analyses were performed to assess the predictive value of PNI and other clinicopathological factors. A stepwise logistic regression model for pCR based on the smallest Akaike information criterion was utilized to develop a nomogram. The C-index, calibration plots and decision curve analysis (DCA) were used to evaluate the discrimination, calibration and clinical value of the model.ResultsPatients with a high PNI (>= 53) had a significantly increased pCR rate (OR 2.217, 95% CI 1.215-4.043, p=0.009). Tumor size, clinical nodal status, histological grade, ER, Ki67 and PNI were identified as independent predictors and included in the final model. A nomogram was developed as a graphical representation of the model, which incorporated the PNI and five other factors (AIC=356.13). The nomogram demonstrated satisfactory calibration and discrimination in the training cohort (C-index: 0.816, 95% CI 0.765-0.866), the internal validation cohort (C-index: 0.780, 95% CI 0.697-0.864) and external validation cohort (C-index: 0.714, 95% CI 0.660-0.769). Furthermore, DCA indicated a clinical net benefit from the nomogram.ConclusionThe pretreatment PNI is a reliable predictor for pCR in breast cancer patients. The PNI-based nomogram is a low-cost, noninvasive tool with favorable predictive accuracy for pCR, which can assist in determining individualized treatment strategies for breast cancer patients."

基金机构：Talent Program of Chongqing [CQYC20200303137]; Chongqing Municipal Health and Health Commission [2019NLTS005]; Chongqing Research Institute Performance Incentive Guide Special Project; Beijing Science and Technology Innovation Medical Development Foundation [KC2021-JF-0167-05]

基金资助正文："The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the Talent Program of Chongqing (Grant No. CQYC20200303137), Chongqing Municipal Health and Health Commission (Grant No.2019NLTS005), Chongqing Research Institute Performance Incentive Guide Special Project and Beijing Science and Technology Innovation Medical Development Foundation (Grant No. KC2021-JF-0167-05)."