文献类型：期刊文献

英文题名：T2WI and ADC radiomics combined with a nomogram based on clinicopathologic features to quantitatively predict microsatellite instability in colorectal cancer

作者：Peng, Leping[1];Zhang, Xiuling[1];Zhu, Yuanhui[2];Shi, Liuyan[2];Ai, Kai[3];Huang, Gang[2];Ma, Wenting[2];Wei, Zhaokun[2];Wang, Ling[2];Ma, Yaqiong[2];Wang, Lili[4]

第一作者：Peng, Leping

通信作者：Wang, LL[1]

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

年份：2025

卷号：32

期号：3

起止页码：1431

外文期刊名：ACADEMIC RADIOLOGY

收录：;Scopus(收录号：2-s2.0-85207725596);WOS:【SCI-EXPANDED(收录号:WOS:001435837600001)】；

基金：This work was supported by the Gansu Provincial Youth Science and Technology Fund Project (No.20JR5RA143) and the Gansu Provincial Hospital Research Fund Project (No.23GSSYF-4 and No.23GSSYA-2) . At the same time, we express our gratitude to Diliang He and Jianxin Zhao from Gansu University of Chinese Medicine for their valuable assistance in data analysis. Additionally, we appreciate the efforts of the staff from the Department of Radiology, the Department of Pathology, and Department of Colorectal Surgery at Gansu Provincial Hospital in collecting the in-formation used in this study.

语种：英文

外文关键词：Colorectal neoplasia; Microsatellite instability; MRI; Nomogram; Radiomics

摘要：Rationale and Objectives: Microsatellite instability (MSI) stratification can guide the clinical management of patients with colorectal cancer (CRC). This study aimed to establish a radiomics model for predicting the MSI status of patients with CRC before treatment. Materials and Methods: This retrospective study was performed on 366 patients diagnosed with CRC who underwent preoperative magnetic resonance imaging (MRI) and immunohistochemical staining between February 2016 and September 2023. The participants were divided randomly into training and testing cohorts in a 7:3 ratio. The tumor volume of interest (VOI) was manually delineated on T2- weighted imaging (T2WI) and apparent diffusion coefficient (ADC) sequences using 3D Slicer software, and radiomics features were extracted. Feature selection was performed using the least absolute shrinkage and selection operator method. A radiomics nomogram was developed using multiple logistic regression, and the predictive performance of the models was evaluated and compared using receiver operating characteristic curves. The calibration curve, clinical decision curve analysis (DCA) and clinical impact curve (CIC) were used to evaluate the clinical application value of the model. Results: The radiomics normogram combined with history of chronic enteritis, tumor location, MR-reported inflammatory response, D2-40, carcinoembryonic antigen, tumor protein 53, and monocyte was an excellent predictive tool. The area under the curve for the training and testing cohorts were 0.927 and 0.984, respectively. The DCA and CIC demonstrated favorable clinical application and net benefit. Conclusions: A radiomics nomogram based on T2WI and ADC sequences and clinicopathologic features can effectively and non- invasively predict the MSI status in CRC. This approach helps clinicians in stratifying CRC patients and making clinical decisions for personalized treatment.