Table_1_Construction and Validation of a Serum Albumin-to-Alkaline Phosphatase Ratio-Based Nomogram for Predicting Pathological Complete Response in Breast Cancer.docx

BackgroundBreast cancer patients who achieve pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) have favorable outcomes. Reliable predictors for pCR help to identify patients who will benefit most from NAC. The pretreatment serum albumin-to-alkaline phosphatase ratio (AAPR) has been shown to be a prognostic predictor in several malignancies, but its predictive value for pCR in breast cancer is still unknown. This study aims to investigate the predictive role of AAPR in breast cancer patients and develop an AAPR-based nomogram for pCR rate prediction.
MethodsA total of 780 patients who received anthracycline and taxane-based NAC from January 2012 to March 2018 were retrospectively analyzed. Univariate and multivariate analyses were performed to assess the predictive value of AAPR and other clinicopathological factors. A nomogram was developed and calibrated based on multivariate logistic regression. A validation cohort of 234 patients was utilized to further validate the predictive performance of the model. 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 lower AAPR (<0.583) had a significantly reduced pCR rate (OR 2.228, 95% CI 1.246-3.986, p=0.007). Tumor size, clinical nodal status, histological grade, PR, Ki67 and AAPR were identified as independent predictors and included in the final model. The nomogram was used as a graphical representation of the model. The nomogram had satisfactory calibration and discrimination in both the training cohort and validation cohort (the C-index was 0.792 in the training cohort and 0.790 in the validation cohort). Furthermore, DCA indicated a clinical net benefit from the nomogram.
ConclusionsPretreatment serum AAPR is a potentially valuable predictor for pCR in breast cancer patients who receive NAC. The AAPR-based nomogram is a noninvasive tool with favorable predictive accuracy for pCR, which helps to make individualized treatment strategy decisions.

研究背景
接受新辅助化疗(neoadjuvant chemotherapy, NAC)后达到病理完全缓解(pathological complete response, pCR)的乳腺癌患者，预后良好。可靠的pCR预测指标有助于甄别出最能从NAC中获益的患者。预处理血清白蛋白与碱性磷酸酶比值(albumin-to-alkaline phosphatase ratio, AAPR)已被证实可作为多种恶性肿瘤的预后预测指标，但其在乳腺癌患者中对pCR的预测价值尚不明确。本研究旨在探讨AAPR在乳腺癌患者中的预测作用，并构建基于AAPR的列线图(nomogram)以预测pCR率。

研究方法
本研究回顾性分析了2012年1月至2018年3月期间，780例接受以蒽环类和紫杉类为基础的NAC的乳腺癌患者。采用单因素及多因素分析评估AAPR及其他临床病理因素的预测价值。基于多因素logistic回归构建并校准列线图。纳入234例患者作为验证队列，进一步验证该模型的预测性能。采用一致性指数(concordance index, C-index)、校准曲线及决策曲线分析(decision curve analysis, DCA)评估模型的区分度、校准度及临床价值。

研究结果
AAPR较低（<0.583）的患者pCR率显著降低（优势比(odds ratio, OR)=2.228，95%置信区间(confidence interval, CI)：1.246~3.986，P=0.007）。肿瘤大小、临床淋巴结状态、组织学分级、孕激素受体(progesterone receptor, PR)、Ki67及AAPR被确定为独立预测因素，并纳入最终模型。列线图可作为该模型的可视化呈现形式。该列线图在训练队列及验证队列中均表现出良好的校准度与区分度，训练队列C-index为0.792，验证队列C-index为0.790。此外，DCA显示该列线图可带来临床净获益。

研究结论
预处理血清AAPR可作为接受NAC的乳腺癌患者pCR的潜在有效预测指标。基于AAPR构建的列线图是一种无创的pCR预测工具，具备良好的预测准确性，有助于制定个体化治疗策略。