Department of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China.
Department of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China.
Breast. 2022 Jun;63:61-70. doi: 10.1016/j.breast.2022.03.007. Epub 2022 Mar 16.
Breast cancer is the most common malignancy in women. Clinical models such as Oncotype DX recurrence score (RS) and Clinical Treatment Score post-5 years (CTS5) model for survival prediction are crucial for clinical practice. However, it remains unclear whether CTS5 or RS would be a more powerful clinical model for recurrence risk evaluation. Therefore, we conducted the present study to compare the performance of CTS5 risk model and RS on different recurrence evaluation. And we further integrated the two models into a novel nomogram to improve the power for prognosis prediction.
Female patients with invasive hormone receptor positive breast cancer in the Surveillance, Epidemiology, and End Results Program (SEER) database with RS data available were included. The clinicopathological data were directly extracted from SEER database. Participants were divided into three subsets according to recurrence timing (<36 months, between 36 and 60 months, and >60 months) for model evaluation. Survival receiver operating characteristic curve and C-index were calculated to evaluate discrimination. Calibration curve were used to visual inspection for calibration. Model comparison was assessed by net reclassification index (NRI) method. Nomogram prognostic model was developed with the combination of CTS5 score and RS and also included other critical clinicopathological parameters.
In total, 64044 breast cancer patients were enrolled for analysis. The number of patients with survival <36 months (early recurrence subset), 36-60 months (intermediate recurrence subset) and >60 months (late recurrence subset) were 64044, 36878 and 15926, respectively. For model discrimination, CTS5 model was superior to RS for overall survival (OS) prediction (likelihood ratio test P < 0 0.001). RS model showed better performance for breast cancer specific survival (BCSS) in late recurrence subsets and worse performance in early and intermediate recurrence subsets than CTS5 (likelihood ratio test P < 0 0.001). For calibration, CTS5 model was superior to RS model for OS, which overestimated the recurrence risk in low-risk subgroup. Both models overestimated the risk for BCSS. In either early/intermediate/late recurrence patient subsets, there was no significant difference in NRI between two models in terms of both BCSS and OS, indicating the two models had comparable prognostic value. The nomogram which combined these two models largely improved the discrimination and calibration power (C-index 0.70-0.72).
Our study proved the CTS5 risk model had comparable prognostic value as RS in HR + breast cancer patients. And the novel nomogram model had better discrimination and calibration than both CTS5 and RS, and future large-scale clinical trials are warranted for further validation.
乳腺癌是女性最常见的恶性肿瘤。临床模型，如 Oncotype DX 复发评分（RS）和临床治疗评分后 5 年（CTS5）模型，对于临床实践至关重要。然而，尚不清楚 CTS5 或 RS 哪个模型更适合用于评估复发风险。因此，我们进行了本研究，旨在比较 CTS5 风险模型和 RS 在不同的复发评估中的表现。我们进一步将这两种模型整合到一个新的列线图中，以提高预后预测的能力。
本研究纳入了美国监测、流行病学和最终结果（SEER）数据库中具有 RS 数据的激素受体阳性浸润性乳腺癌女性患者。临床病理数据直接从 SEER 数据库中提取。根据复发时间（<36 个月、36-60 个月和>60 个月）将参与者分为三个亚组，以评估模型的性能。使用生存接收者操作特征曲线和 C 指数评估区分度。校准曲线用于直观检查校准情况。通过净重新分类指数（NRI）方法评估模型比较。列线图预后模型是通过 CTS5 评分和 RS 的结合建立的，同时还包括其他关键临床病理参数。
共纳入 64044 例乳腺癌患者进行分析。生存时间<36 个月（早期复发亚组）、36-60 个月（中期复发亚组）和>60 个月（晚期复发亚组）的患者分别为 64044、36878 和 15926 例。对于模型区分度，CTS5 模型在总生存期（OS）预测方面优于 RS（似然比检验 P<0.001）。RS 模型在晚期复发亚组中对乳腺癌特异性生存（BCSS）的预测表现更好，而在早期和中期复发亚组中表现不如 CTS5（似然比检验 P<0.001）。在校准方面，CTS5 模型在 OS 方面优于 RS 模型，后者低估了低风险亚组的复发风险。两种模型均高估了 BCSS 的风险。在早期/中期/晚期复发患者亚组中，两种模型在 BCSS 和 OS 方面的 NRI 均无显著差异，表明两种模型具有相当的预后价值。联合这两种模型的列线图大大提高了区分度和校准能力（C 指数 0.70-0.72）。
本研究证明，在 HR+乳腺癌患者中，CTS5 风险模型与 RS 具有相当的预后价值。新型列线图模型在区分度和校准方面优于 CTS5 和 RS，需要进一步进行大规模的临床试验进行验证。
