Development and validation of magnetic resonance imaging-based radiomics models for preoperative prediction of microsatellite instability in rectal cancer.
机构：[1]Department of Radiology, West China Hospital, Sichuan University, Chengdu, China四川大学华西医院[2]Department of Radiology, Sichuan Provincial Corps Hospital, Chinese People’s Armed Police Forces, Leshan, China[3]Department of Pathology, West China Hospital, Sichuan University, Chengdu, China四川大学华西医院[4]Institute of Advanced Research, InferVision, Beijing, China
Microsatellite instability (MSI) is a predictive biomarker for response to chemotherapy and a prognostic biomarker for clinical outcomes of rectal cancer. The purpose of this study was to develop and validate radiomics models for preoperative prediction of the MSI status of rectal cancer based on magnetic resonance (MR) images.
This study retrospectively recruited 491 rectal cancer patients with pathologically confirmed MSI status. Patients were randomly divided into a training cohort (n=327) and a validation cohort (n=164). The most predictive radiomics features were selected using intraclass correlation coefficient (ICC) analysis, the two-sample t test, and the least absolute shrinkage and selection operator (LASSO) method. XGBoost models were constructed in the training cohort to discriminate the MSI status using clinical factors, radiomics features, or a combined model incorporating both the radiomics signature and independent clinical characteristics. The diagnostic performance of these three models was evaluated in the validation cohort based on their area under the curve (AUC), sensitivity, specificity, and accuracy.
Among the 491 rectal cancer patients, the prevalence of MSI was 10.39% (51/491). Following ICC analysis, two-sample t test, and LASSO regression, six radiomics features were selected for subsequent analysis. The combined model, which incorporated both the clinical factors and radiomics features achieved an AUC of 0.895 [95% confidence interval (CI), 0.838-0.938] in the validation cohort, and showed better performance in predicting MSI status than the other two models using either clinical factors (P=0.015) or radiomics features (P=0.204) alone.
Radiomics features based on preoperative T2-weighted MR imaging (MRI) are associated with the MSI status of rectal cancer. Combinational analysis of clinical factors and radiomics features may improve predictive performance and potentially contribute to noninvasive personalized therapy selection.
2021 Annals of Translational Medicine. All rights reserved.
基金：
Research Grant of National Nature Science Foundation of China (Grant number 81971571).
语种：
外文
PubmedID：
中科院(CAS)分区：
出版当年[2021]版：
大类|4 区医学
小类|4 区肿瘤学4 区医学：研究与实验
最新[2023]版：
无
第一作者：
第一作者机构：[1]Department of Radiology, West China Hospital, Sichuan University, Chengdu, China[2]Department of Radiology, Sichuan Provincial Corps Hospital, Chinese People’s Armed Police Forces, Leshan, China
共同第一作者：
通讯作者：
通讯机构：[1]Department of Radiology, West China Hospital, Sichuan University, Chengdu, China[*1]Department of Radiology, West China Hospital, Sichuan University. No. 37, GUOXUE Alley, Chengdu 610041, China
推荐引用方式(GB/T 7714)：
Zhang Wei,Huang Zixing,Zhao Jian,et al.Development and validation of magnetic resonance imaging-based radiomics models for preoperative prediction of microsatellite instability in rectal cancer.[J].Annals of translational medicine.2021,9(2):134.doi:10.21037/atm-20-7673.
APA：
Zhang Wei,Huang Zixing,Zhao Jian,He Du,Li Mou...&Song Bin.(2021).Development and validation of magnetic resonance imaging-based radiomics models for preoperative prediction of microsatellite instability in rectal cancer..Annals of translational medicine,9,(2)
MLA：
Zhang Wei,et al."Development and validation of magnetic resonance imaging-based radiomics models for preoperative prediction of microsatellite instability in rectal cancer.".Annals of translational medicine 9..2(2021):134
