PurposeTo study the combined model of radiomic features and clinical features based on enhanced CT images for noninvasive evaluation of microsatellite instability (MSI) status in colorectal liver metastasis (CRLM) before surgery.MethodsThe study included 104 patients retrospectively and collected CT images of patients. We adjusted the region of interest to increase the number of MSI-H images. Radiomic features were extracted from these CT images. The logistic models of simple clinical features, simple radiomic features, and radiomic features with clinical features were constructed from the original image data and the expanded data, respectively. The six models were evaluated in the validation set. A nomogram was made to conveniently show the probability of the patient having a high MSI (MSI-H).ResultsThe model including radiomic features and clinical features in the expanded data worked best in the validation group.ConclusionA logistic regression prediction model based on enhanced CT images combining clinical features and radiomic features after increasing the number of MSI-H images can effectively identify patients with CRLM with MSI-H and low-frequency microsatellite instability (MSI-L), and provide effective guidance for clinical immunotherapy of CRLM patients with unknown MSI status.
基金：
1.Natural Science Foundation of Hebei Province, General Project, Grant/Award
Number H2020201021.
2.China Postdoctoral Fund, Grant/Award Number: 2018M631755.
3.Hebei Province High-level Talent Funding Project-Post-doctoral Research
Projects Selective Funding, Grant/Award Number: B2018003002.
4.Hebei University improve comprehensive strength special funds in the
Midwest, Grant/Award Number: 801260201011.
5.National Natural Science Foundation of China, Grant/Award Number:
61401308 and 61572063;
6.Natural Science Foundation of Gansu Province, Grant/
AwardNumber:18JR3RA029.
7.Hebei provincial government funded Provincial Medical Talents Project in
2020,CT radiomics study on the correlation between CRLM and microsatellite
instability, 361007.
8.Medical discipline cultivation project of Hebei University in 2020,CT
radiomics study on the correlation between CRLM and microsatellite
instability,2020B05.
9.Outstanding young scientific research and innovation team of Hebei
University,605020521007.
10.Nature Science Foundation of Hebei, Grant/Award Number: H202101017.
第一作者机构：[1]Hebei Univ, Coll Elect & Informat Engn, Baoding 071002, Peoples R China[2]Res Ctr Machine Vis Engn & Technol Hebei Prov, Baoding 071002, Peoples R China[3]Key Lab Digital Med Engn Hebei Prov, Baoding 071002, Peoples R China
通讯作者：
通讯机构：[1]Hebei Univ, Coll Elect & Informat Engn, Baoding 071002, Peoples R China[2]Res Ctr Machine Vis Engn & Technol Hebei Prov, Baoding 071002, Peoples R China[3]Key Lab Digital Med Engn Hebei Prov, Baoding 071002, Peoples R China
推荐引用方式(GB/T 7714)：
Wang Xuehu,Liu Ziqi,Yin Xiaoping,et al.A radiomics model fusing clinical features to predict microsatellite status preoperatively in colorectal cancer liver metastasis[J].BMC GASTROENTEROLOGY.2023,23(1):doi:10.1186/s12876-023-02922-0.
APA：
Wang, Xuehu,Liu, Ziqi,Yin, Xiaoping,Yang, Chang&Zhang, Jushuo.(2023).A radiomics model fusing clinical features to predict microsatellite status preoperatively in colorectal cancer liver metastasis.BMC GASTROENTEROLOGY,23,(1)
MLA：
Wang, Xuehu,et al."A radiomics model fusing clinical features to predict microsatellite status preoperatively in colorectal cancer liver metastasis".BMC GASTROENTEROLOGY 23..1(2023)
