A radiomics model fusing clinical features to predict microsatellite status preoperatively in colorectal cancer liver metastasis

• Published in: BMC gastroenterology Vol. 23; no. 1; pp. 1 - 8
• Main Authors: Wang, Xuehu, Liu, Ziqi, Yin, Xiaoping, Yang, Chang, Zhang, Jushuo
• Format: Journal Article
• Language: English
• Published: London BioMed Central Ltd 12.09.2023; BioMed Central; BMC
• Subjects: Biopsy; Cancer; Classification; Colorectal cancer; Colorectal carcinoma; Computed tomography; CT imaging; Gastroenterology; Genetic aspects; Immunotherapy; Liver; Liver cancer; Liver metastasis of colorectal cancer; Logistic regression; Medical imaging; Medical prognosis; Medical research; Medicine, Experimental; Metastases; Metastasis; Microsatellite instability; Nomogram; Nomograms; Noninvasive evaluation; Patients; Performance evaluation; Physicians; Prediction models; Radiomics; Regression analysis; Tomography; Tumors; China; Taiwan
• ISSN: 1471-230X; 1471-230X
• DOI: 10.1186/s12876-023-02922-0

Abstract Purpose To 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. Methods The 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). Results The model including radiomic features and clinical features in the expanded data worked best in the validation group. Conclusion A 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.