Radiomics-based prediction of microsatellite instability in stage Ⅱ and Ⅲ rectal cancer patients based on T2WI MRI and diffusion-weighted imaging.
10.3760/cma.j.cn112139-20230315-00106
- VernacularTitle:基于高分辨T2WI及弥散加权成像的放射组学模型预测Ⅱ~Ⅲ期直肠癌患者微卫星稳定性状态
- Author:
Shuai XIANG
1
;
Long Bo ZHENG
1
;
Ling ZHU
2
;
Yuan GAO
1
;
Dong Sheng WANG
1
;
Shang Long LIU
1
;
Shuai ZHANG
3
;
Tong Yu WANG
3
;
Yun LU
1
Author Information
1. Department of Gastrointestinal Surgery, Affiliated Hospital of Qingdao University, Qingdao 266000, China.
2. Shandong Provincial Key Laboratory of Digital Medicine and Computer-Assisted Surgery, Qingdao 266000, China.
3. Department of Radiology, Affiliated Hospital of Qingdao University, Qingdao 266000, China.
- Publication Type:Journal Article
- From:
Chinese Journal of Surgery
2023;61(9):784-790
- CountryChina
- Language:Chinese
-
Abstract:
Objective: To examine the radiomics model based on high-resolution T2WI and diffusion weighted imaging (DWI) in predicting microsatellite stability in patients with stage Ⅱ and Ⅲ rectal cancer. Methods: From February 2016 to October 2020, 175 patients with stage Ⅱ and Ⅲ rectal cancer who met the inclusion criteria were retrospectively collected. There were 119 males and 56 females, aged (63.9±9.4) years (range: 37 to 85 years), including 152 patients with microsatellite stability and 23 patients with microsatellite instability. All patients were randomly divided into the training group (n=123) and the validation group (n=52) with a ratio of 7∶3. The region of interest was labeled on the T2WI and DWI images of each patient using the ITK-SNAP software, and PyRadiomics was used to extract seven kinds of radiomics features. After removing redundant features and normalizing features, the least absolute shrinkage and selection operation were used for feature selection. One clinical model, three radiomics models and one clinical-radiomics model were constructed in the training group based on a support vector machine. The area under receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy were used to evaluate the performance of the models in the verification group. Results: Three clinical features (age, degree of tumor differentiation, and distance from the lower edge of the tumor to the anal edge) and six radiomics features (two DWI-related features and four T2WI-related features) most related to microsatellite status of rectal cancer patients were selected. The AUC of the clinical-radiomics model in the training group was 0.95. In the validation group, the AUC was 0.81, better than the clinical model (0.68, Z=0.71, P=0.04), and equivalent to the T2WI+DWI model (0.82, Z=0.21, P=0.83). Conclusions: Radiomic features based on preoperative T2WI and DWI were related to microsatellite stability in patients with stage Ⅱ and Ⅲ rectal cancer and showed a high classification efficiency. The model based on the features provided a noninvasive and convenient tool for preoperative determination of microsatellite stability in rectal cancer patients.
