Song Jiayin, Yang Lin, Feng Zhengqi, Jiang Liyu
Department of Breast Surgery, General Surgery, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China.
Department of Breast Surgery, General Surgery, Qilu Hospital of Shandong University Dezhou Hospital, Dezhou, China.
Cancer Med. 2025 Mar;14(6):e70818. doi: 10.1002/cam4.70818.
Breast cancer (BC) is the most prevalent cancer among women worldwide, with increasing incidence rates, particularly in China. Given the high costs of Oncotype DX (ODX) testing, which predicts recurrence scores (RSs) on the basis of gene expression, developing a nomogram utilizing clinicopathological variables may provide an accessible alternative for risk stratification.
We conducted a retrospective analysis of 703 estrogen receptor (ER)-positive, HER2-negative T1-3N0M0 BC patients who underwent ODX testing at Qilu Hospital. A nomogram was developed using multivariate logistic regression to predict low and high RSs in the group. Model performance was validated by receiver operating characteristic curve, calibration curve, and decision curve analysis.
Multivariate analysis revealed that older age, lower histologic grade, a higher ER expression level, a higher proportion of cells expressing progesterone receptor, and a lower proportion of cells expressing Ki-67 were significantly associated with a patient being in the low-risk subgroup. A nomogram was then developed using these variables to predict the RS, with an area under the curve (AUC) of 0.811 (95% confidence interval [CI] = 0.772-0.850) in the development group and 0.794 (95% CI = 0.737-0.851) in the validation group. Calibration and decision curve analyses further confirmed the nomogram's clinical utility. Moreover, a comparison between the TAILORx-nomogram and our nomogram was conducted, which proved that our nomogram has better predictive accuracy and reliability in Chinese BC patients.
We present the first nomogram for predicting the RS in Chinese patients with BC on the basis of clinicopathological factors. This model could aid in identifying patients who may not need ODX testing and serve as a cost-effective alternative for those unable to access ODX, thereby optimizing treatment decisions and enhancing patient management in resource-limited settings.
乳腺癌（BC）是全球女性中最常见的癌症，发病率呈上升趋势，在中国尤为明显。鉴于Oncotype DX（ODX）检测成本高昂，该检测基于基因表达预测复发评分（RS），利用临床病理变量开发列线图可为风险分层提供一种可行的替代方法。
我们对在齐鲁医院接受ODX检测的703例雌激素受体（ER）阳性、HER2阴性的T1-3N0M0期乳腺癌患者进行了回顾性分析。使用多因素逻辑回归开发列线图，以预测该组患者的低复发评分和高复发评分。通过受试者工作特征曲线、校准曲线和决策曲线分析验证模型性能。
多因素分析显示，年龄较大、组织学分级较低、ER表达水平较高、孕激素受体表达细胞比例较高以及Ki-67表达细胞比例较低与患者属于低风险亚组显著相关。然后使用这些变量开发列线图以预测复发评分，在开发组中曲线下面积（AUC）为0.811（95%置信区间[CI]=0.772-0.850），在验证组中为0.794（95%CI=0.737-0.851）。校准和决策曲线分析进一步证实了列线图的临床实用性。此外，还对TAILORx列线图和我们的列线图进行了比较，结果证明我们的列线图在中国乳腺癌患者中具有更好的预测准确性和可靠性。
我们基于临床病理因素提出了首个用于预测中国乳腺癌患者复发评分的列线图。该模型有助于识别可能不需要进行ODX检测的患者，并为无法进行ODX检测的患者提供一种经济有效的替代方法，从而在资源有限的环境中优化治疗决策并加强患者管理。
