文献类型：期刊文献

英文题名：Predictive value of combined DCE-MRI perfusion parameters and clinical features nomogram for microsatellite instability in colorectal cancer

作者：Peng, Leping[1];Ma, Wenting[2];Zhang, Xiuling[1];Zhang, Fan[1];Ma, Fang[1];Ai, Kai[3];Ma, Xiaomei[2];Jia, Yingmei[2];Ou-Yang, Hong[2];Pei, Shengting[2];Wang, Tao[4];Zhu, Yuanhui[2];Wang, Lili[2]

第一作者：Peng, Leping

通信作者：Zhu, YH[1];Wang, LL[1]

机构：[1]Gansu Univ Chinese Med, Lanzhou 730000, Gansu, Peoples R China;[2]Gansu Prov Hosp, Dept Radiol, Lanzhou 730000, Gansu, Peoples R China;[3]Philips Healthcare, Dept Clin & Tech Support, Xian 710065, Peoples R China;[4]Gansu Prov Hosp, Dept Colorectal Surg, Lanzhou 730000, Gansu, Peoples R China

第一机构：甘肃中医药大学

通信机构：[1]corresponding author), Gansu Prov Hosp, Dept Radiol, Lanzhou 730000, Gansu, Peoples R China.

年份：2025

卷号：16

期号：1

外文期刊名：DISCOVER ONCOLOGY

收录：;Scopus(收录号：2-s2.0-105005780136);WOS:【SCI-EXPANDED(收录号:WOS:001494402800017)】；

基金：We would like to thank Zhaokun Wei and Xinli Li for their assistance with image acquisition and technical guidance. Additionally, we appreciate the efforts of the staff from the Radiology, Pathology, and colorectal surgery departments at Gansu Provincial Hospital for their contributions to the data collection for this study.

语种：英文

外文关键词：Magnetic resonance imaging; DCE; Nomogram; Microsatellite instability; Colorectal cancer

摘要：ObjectivesTo develop a nomogram that combines dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) perfusion parameters, ADC values and clinical features to preoperatively identify microsatellite instability (MSI) in patients with colorectal cancer (CRC).MethodsThis retrospective study included 63 CRC patients who underwent preoperative DCE-MRI and had immunohistochemistry results available. Two radiologists, in a double-blind manner, placed two circular regions of interests in the area with the highest perfusion intensity on the DCE-MRI perfusion map and the corresponding area on the ADC map. Perfusion parameters and ADC values were measured, and the average values from both radiologists were used for subsequent analysis. Univariate analysis was performed to identify independent risk factors for MSI. A nomogram was then constructed by combining the most significant clinical risk factors with DCE-MRI perfusion parameters. The model's performance was evaluated using receiver operating characteristic (ROC) curves. Calibration curves, decision curve analysis (DCA), and clinical impact curves (CIC) were used to assess the nomogram's clinical utility and net benefit.ResultsThe nomogram prediction model, which combined PLT, LNM, Ktrans, Kep, iAUC, and ADC, demonstrated good predictive performance. The combined model had an AUC of 0.951 (95% CI: 0.903-0.998), an accuracy of 0.873, a sensitivity of 1.000, and a specificity of 0.818. Both the DCA and CIC demonstrated good clinical applicability and net benefit.ConclusionThe nomogram method demonstrated good potential in the preoperative individualized identification of MSI status in CRC patients. This tool can assist clinicians in adopting appropriate treatment strategies and optimizing personalized stratification for CRC patients.