Chen Siming, Xiong Kangping, Shi Jiageng, Yao Shijie, Wang Gang, Qian Kaiyu, Wang Xinghuan
Department of Urology, Zhongnan Hospital of Wuhan University, Wuhan, China.
Department of Gynecological Oncology, Zhongnan Hospital of Wuhan University, Wuhan, China.
Front Surg. 2023 Mar 10;10:1110040. doi: 10.3389/fsurg.2023.1110040. eCollection 2023.
The tumor biology of neuroendocrine prostate cancer (NEPC) is different from that of ordinary prostate cancer, herefore, existing clinical prognosis models for prostate cancer patients are unsuitable for NEPC. The specialized individual situation assessment and clinical decision-making tools for NEPC patients are urgently needed. This study aimed to develop a valid NEPC prognostic nomogram and risk stratification model to predict risk associated with patient outcomes.
We collected 340 de-novo NEPC patients from the SEER database, and randomly selected 240 of them as the training set and the remaining 100 as the validation set. Cox regression model was used to screen for risk factors affecting overall survival (OS) and cancer-specific survival (CSS) and construct a corresponding nomogram. The receiver operating characteristic (ROC) curves, calibration curves, C-indexes, and decision curve analysis (DCA) curves are used to verify and calibrate nomograms.
NEPC prognosis nomograms were constructed by integrating independent risk factors. The C-indexes, ROC curves, calibration curves, and DCA curves revealed excellent prediction accuracy of the prognostic nomogram. Furthermore, we demonstrated that NEPC patients in the high-risk group had significantly lower OS and CSS than those in the low-risk group with risk scores calculated from nomograms.
The nomogram established in this research has the potential to be applied to the clinic to evaluate the prognosis of NEPC patients and support corresponding clinical decision-making.
神经内分泌前列腺癌（NEPC）的肿瘤生物学与普通前列腺癌不同，因此，现有的前列腺癌患者临床预后模型不适用于NEPC。迫切需要针对NEPC患者的专门个体情况评估和临床决策工具。本研究旨在开发一种有效的NEPC预后列线图和风险分层模型，以预测与患者预后相关的风险。
我们从SEER数据库中收集了340例初发NEPC患者，随机选择其中240例作为训练集，其余100例作为验证集。采用Cox回归模型筛选影响总生存（OS）和癌症特异性生存（CSS）的危险因素，并构建相应的列线图。采用受试者操作特征（ROC）曲线、校准曲线、C指数和决策曲线分析（DCA）曲线对列线图进行验证和校准。
通过整合独立危险因素构建了NEPC预后列线图。C指数、ROC曲线、校准曲线和DCA曲线显示预后列线图具有出色的预测准确性。此外，我们证明，根据列线图计算风险评分，高危组NEPC患者的OS和CSS显著低于低危组。
本研究建立的列线图有潜力应用于临床，以评估NEPC患者的预后并支持相应的临床决策。
