针对乳腺癌患者新辅助化疗病理完全缓解的个体化预测模型：一项多中心研究。

Individualized model for predicting pathological complete response to neoadjuvant chemotherapy in patients with breast cancer: A multicenter study.

机构信息

Department of Thyroid and Breast Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

Department of Breast Surgery, Department of General Surgery, First Affiliated Hospital of Anhui Medical University, Hefei, China.

出版信息

Front Endocrinol (Lausanne). 2022 Aug 17;13:955250. doi: 10.3389/fendo.2022.955250. eCollection 2022.

BACKGROUND

Pathological complete response (pCR) is considered a surrogate for favorable survival in breast cancer (BC) patients treated with neoadjuvant chemotherapy (NACT), which is the goal of NACT. This study aimed to develop and validate a nomogram for predicting the pCR probability of BC patients after NACT based on the clinicopathological features.

METHODS

A retrospective analysis of 527 BC patients treated with NACT between January 2018 and December 2021 from two institutions was conducted. Univariate and multivariate logistic regression analyses were performed to select the most useful predictors from the training cohort (n = 225), and then a nomogram model was developed. The performance of the nomogram was evaluated with respect to its discrimination, calibration, and clinical usefulness. Internal validation and external validation were performed in an independent validation cohort of 96 and 205 consecutive BC patients, respectively.

RESULTS

Among the 18 clinicopathological features, five variables were selected to develop the prediction model, including age, American Joint Committee on Cancer (AJCC) T stage, Ki67 index before NACT, human epidermal growth factor receptor 2 (HER2), and hormone receptor (HR) status. The model showed good discrimination with an area under the receiver operating characteristic curve (AUC) of 0.825 (95% CI, 0.772 to 0.878) in the training cohort, and 0.755 (95% CI, 0.658 to 0.851) and 0.79 (95% CI, 0.724 to 0.856) in the internal and external validation cohorts, respectively. The calibration curve presented good agreement between prediction by nomogram and actual observation, and decision curve analysis (DCA) indicated that the nomogram had good net benefits in clinical scenarios.

CONCLUSION

This study constructed a validated nomogram based on age, AJCC T stage, Ki67 index before NACT, HER2, and HR status, which could be non-invasively applied to personalize the prediction of pCR in BC patients treated with NACT.

背景

病理完全缓解（pCR）被认为是接受新辅助化疗（NACT）的乳腺癌（BC）患者生存良好的替代指标，这也是 NACT 的目标。本研究旨在基于临床病理特征，为接受 NACT 的 BC 患者开发和验证一种预测 pCR 概率的列线图。

方法

对 2018 年 1 月至 2021 年 12 月在两个机构接受 NACT 的 527 例 BC 患者进行回顾性分析。在训练队列（n = 225）中进行单变量和多变量逻辑回归分析，以选择最有用的预测指标，然后构建列线图模型。使用鉴别度、校准度和临床实用性来评估列线图的性能。内部验证和外部验证分别在 96 例和 205 例连续 BC 患者的独立验证队列中进行。

结果

在 18 个临床病理特征中，有 5 个变量被选入预测模型，包括年龄、美国癌症联合委员会（AJCC）T 分期、NACT 前 Ki67 指数、人表皮生长因子受体 2（HER2）和激素受体（HR）状态。该模型在训练队列中的受试者工作特征曲线（ROC）下面积（AUC）为 0.825（95%CI，0.772 至 0.878），内部验证队列为 0.755（95%CI，0.658 至 0.851），外部验证队列为 0.79（95%CI，0.724 至 0.856），具有良好的鉴别度。校准曲线显示，列线图预测与实际观察结果之间有较好的一致性，决策曲线分析（DCA）表明，该列线图在临床情况下具有较好的净收益。