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[Radiomics-based prediction of microsatellite instability in stage Ⅱ and Ⅲ rectal cancer patients based on T2WI MRI and diffusion-weighted imaging].

医学 接收机工作特性 无线电技术 微卫星不稳定性 结直肠癌 磁共振成像 放射科 磁共振弥散成像 阶段(地层学) 癌症 核医学 内科学 微卫星 基因 古生物学 等位基因 化学 生物 生物化学
作者
Shuai Xiang,Lin Zheng,Li Zhu,Yuan Gao,D S Wang,Shaocheng Liu,Sam Zhang,T Y Wang,Yun Lu
出处
期刊:PubMed [National Institutes of Health]
卷期号:61 (9): 782-787
标识
DOI:10.3760/cma.j.cn112139-20230315-00106
摘要

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.
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