Department of Radiation Oncology, Sun Yat-Sen University Cancer Center, Guangzhou, Guangdong, China.
State Key Laboratory of Oncology in South China, Sun Yat-Sen University, Guangzhou, Guangdong, China.
BMJ Open. 2023 Feb 21;13(2):e065312. doi: 10.1136/bmjopen-2022-065312.
The present study aimed to develop and validate nomograms to predict the survival of patients with breast invasive micropapillary carcinoma (IMPC) to aid objective decision-making.
Prognostic factors were identified using Cox proportional hazards regression analyses and used to construct nomograms to predict overall survival (OS) and breast cancer-specific survival (BCSS) at 3 and 5 years. Kaplan-Meier analysis, calibration curves, the area under the curve (AUC) and the concordance index (C-index) evaluated the nomograms' performance. Decision curve analysis (DCA), integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were used to compare the nomograms with the American Joint Committee on Cancer (AJCC) staging system.
Patient data were collected from the Surveillance, Epidemiology, and End Results (SEER) database. This database holds data related to the incidence of cancer acquired from 18 population-based cancer registries in the US.
We ruled out 1893 patients and allowed the incorporation of 1340 patients into the present study.
The C-index of the AJCC8 stage was lower than that of the OS nomogram (0.670 vs 0.766) and the OS nomograms had higher AUCs than the AJCC8 stage (3 years: 0.839 vs 0.735, 5 years: 0.787 vs 0.658). On calibration plots, the predicted and actual outcomes agreed well, and DCA revealed that the nomograms had better clinical utility compared with the conventional prognosis tool. In the training cohort, the NRI for OS was 0.227, and for BCSS was 0.182, while the IDI for OS was 0.070, and for BCSS was 0.078 (both p<0.001), confirming its accuracy. The Kaplan-Meier curves for nomogram-based risk stratification showed significant differences (p<0.001).
The nomograms showed excellent discrimination and clinical utility to predict OS and BCSS at 3 and 5 years, and could identify high-risk patients, thus providing IMPC patients with personalised treatment strategies.
本研究旨在开发和验证列线图，以预测乳腺浸润性微乳头状癌（IMPC）患者的生存情况，为客观决策提供帮助。
使用 Cox 比例风险回归分析确定预后因素，并用于构建预测总生存（OS）和乳腺癌特异性生存（BCSS）的 3 年和 5 年列线图。Kaplan-Meier 分析、校准曲线、曲线下面积（AUC）和一致性指数（C-index）评估列线图的性能。决策曲线分析（DCA）、综合判别改善（IDI）和净重新分类改善（NRI）用于比较列线图与美国癌症联合委员会（AJCC）分期系统。
患者数据来自监测、流行病学和最终结果（SEER）数据库。该数据库包含了美国 18 个基于人群的癌症登记处获得的癌症发病率相关数据。
我们排除了 1893 例患者，允许将 1340 例患者纳入本研究。
AJCC8 分期的 C-index 低于 OS 列线图（0.670 比 0.766），OS 列线图的 AUC 高于 AJCC8 分期（3 年：0.839 比 0.735，5 年：0.787 比 0.658）。在校准图上，预测结果与实际结果吻合较好，DCA 显示列线图具有比传统预后工具更好的临床应用价值。在训练队列中，OS 的 NRI 为 0.227，BCSS 的 NRI 为 0.182，OS 的 IDI 为 0.070，BCSS 的 IDI 为 0.078（均 P<0.001），证实了其准确性。基于列线图的风险分层的 Kaplan-Meier 曲线显示出显著差异（P<0.001）。
该列线图在预测 3 年和 5 年 OS 和 BCSS 方面具有良好的区分度和临床实用性，可以识别高危患者，从而为 IMPC 患者提供个体化治疗策略。
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