Background/Objectives: The EndoPredict®assay has been widely used in recent years to estimate the risk of distant recurrence and the absolute chemotherapy benefit for patients with estrogen (ER)-positive/human epidermal growth factor receptor 2 (HER2)-negative breast cancer. However, there are no well-defined criteria for selecting patients who may benefit from the test. The aim of this study was to develop a novel nomogram to estimate the probability of obtaining a high-risk EndoPredict®result in clinical practice.Methods: The study cohort comprised 348 cases of T1-3/N0-1a/M0 ER-positive/HER2-negative breast carcinoma. A multivariate analysis was conducted using a training cohort (n = 270) based on clinicopathological features that demonstrated a statistically significant correlation with the EndoPredict®result in a univariate analysis. The predictive model was subsequently represented as a nomogram to estimate the probability of obtaining a high-risk result in the EndoPredict®assay. The predictive model was then validated using a separate validation cohort (n = 78).Results: The clinicopathological features incorporated into the nomogram included tumor size, tumor grade, sentinel lymph node status, pN stage, and Ki67. The internal validation of the model yielded an area under the curve (AUC) of 0.803 (95% CI = 0.751, 0.855) in the receiver operating characteristic (ROC) curve for the training cohort, with an optimal sensitivity and specificity at a threshold of 0.536. The external validation yielded an AUC of 0.789 (95% CI = 0.689, 0.890) in its ROC curve, with optimal sensitivity and specificity achieved at a threshold of 0.393.Conclusions: This study presents, for the first time, the development of a clinically accessible nomogram designed to estimate the probability of obtaining a high-risk result in the EndoPredict®assay. The use of easily available clinicopathological features allows for the optimization of patient selection for the EndoPredict®assay, ensuring that those who would most benefit from undergoing the test are identified.
背景/目的：近年来，EndoPredict®检测已广泛应用于评估雌激素受体（ER）阳性/人表皮生长因子受体2（HER2）阴性乳腺癌患者的远处复发风险及化疗绝对获益。然而，目前尚无明确标准用于筛选可能从该检测中获益的患者。本研究旨在开发一种新型列线图，用于评估临床实践中获得高风险EndoPredict®检测结果的概率。 方法：研究队列纳入348例T1-3/N0-1a/M0期ER阳性/HER2阴性乳腺癌病例。基于单变量分析中与EndoPredict®结果具有统计学显著相关性的临床病理特征，使用训练队列（n=270）进行多变量分析。随后将预测模型构建为列线图，用于评估EndoPredict®检测中获得高风险结果的概率。最后使用独立验证队列（n=78）对该预测模型进行验证。 结果：纳入列线图的临床病理特征包括肿瘤大小、肿瘤分级、前哨淋巴结状态、pN分期及Ki67指数。模型内部验证显示，训练队列受试者工作特征（ROC）曲线下面积（AUC）为0.803（95% CI=0.751，0.855），在0.536阈值处获得最佳敏感度与特异度。外部验证显示其ROC曲线AUC为0.789（95% CI=0.689，0.890），在0.393阈值处达到最佳敏感度与特异度。 结论：本研究首次开发了具有临床适用性的列线图，用于评估EndoPredict®检测中获得高风险结果的概率。通过采用易于获取的临床病理特征，该模型可优化EndoPredict®检测的患者筛选流程，确保最可能从检测中获益的群体得到准确识别。
肿瘤(癌症)患者之家