基于核心针吸活检和手术标本预测接受新辅助化疗后无病理完全缓解的女性乳腺癌患者无病生存期的列线图。
Nomograms for Predicting Disease-Free Survival Based on Core Needle Biopsy and Surgical Specimens in Female Breast Cancer Patients with Non-Pathological Complete Response to Neoadjuvant Chemotherapy.
作者信息
Lan Ailin, Li Han, Chen Junru, Shen Meiying, Jin Yudi, Dai Yuran, Jiang Linshan, Dai Xin, Peng Yang, Liu Shengchun
机构信息
Department of Breast and Thyroid Surgery, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing 400016, China.
Department of Cardiothoracic Surgery, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing 400016, China.
出版信息
J Pers Med. 2023 Jan 29;13(2):249. doi: 10.3390/jpm13020249.
PURPOSE
While a pathologic complete response (pCR) is regarded as a surrogate endpoint for pos-itive outcomes in breast cancer (BC) patients receiving neoadjuvant chemotherapy (NAC), fore-casting the prognosis of non-pCR patients is still an open issue. This study aimed to create and evaluate nomogram models for estimating the likelihood of disease-free survival (DFS) for non-pCR patients.
METHODS
A retrospective analysis of 607 non-pCR BC patients was conducted (2012-2018). After converting continuous variables to categorical variables, variables entering the model were progressively identified by univariate and multivariate Cox regression analyses, and then pre-NAC and post-NAC nomogram models were developed. Regarding their discrimination, ac-curacy, and clinical value, the performance of the models was evaluated by internal and external validation. Two risk assessments were performed for each patient based on two models; patients were separated into different risk groups based on the calculated cut-off values for each model, including low-risk (assessed by the pre-NAC model) to low-risk (assessed by the post-NAC model), high-risk to low-risk, low-risk to high-risk, and high-risk to high-risk groups. The DFS of different groups was assessed using the Kaplan-Meier method.
RESULTS
Both pre-NAC and post-NAC nomogram models were built with clinical nodal (cN) status and estrogen receptor (ER), Ki67, and p53 status (all < 0.05), showing good discrimination and calibration in both internal and external validation. We also assessed the performance of the two models in four subtypes, with the tri-ple-negative subtype showing the best prediction. Patients in the high-risk to high-risk subgroup have significantly poorer survival rates ( < 0.0001).
CONCLUSION
Two robust and effective nomo-grams were developed to personalize the prediction of DFS in non-pCR BC patients treated with NAC.
目的
虽然病理完全缓解（pCR）被视为接受新辅助化疗（NAC）的乳腺癌（BC）患者获得阳性结局的替代终点，但预测非pCR患者的预后仍然是一个未解决的问题。本研究旨在创建并评估列线图模型，以估计非pCR患者无病生存（DFS）的可能性。
方法
对607例非pCR的BC患者进行回顾性分析（2012 - 2018年）。将连续变量转换为分类变量后，通过单因素和多因素Cox回归分析逐步确定进入模型的变量，然后构建NAC前和NAC后的列线图模型。基于模型的区分度、准确性和临床价值，通过内部和外部验证对模型性能进行评估。基于两个模型对每位患者进行两次风险评估；根据每个模型计算的截断值将患者分为不同的风险组，包括低风险（由NAC前模型评估）到低风险（由NAC后模型评估）、高风险到低风险、低风险到高风险以及高风险到高风险组。使用Kaplan-Meier方法评估不同组的DFS。
结果
NAC前和NAC后的列线图模型均基于临床淋巴结（cN）状态、雌激素受体（ER）、Ki-６７和p53状态构建（均P<0.05），在内部和外部验证中均显示出良好的区分度和校准度。我们还评估了两个模型在四种亚型中的性能，三阴性亚型显示出最佳预测效果。高风险到高风险亚组的患者生存率显著较差（P<0.0001）。
结论
开发了两个稳健且有效的列线图，以实现对接受NAC治疗的非pCR BC患者DFS预测的个性化。
Zotero 插件