T2WI and ADC radiomics combined with a nomogram based on clinicopathologic features to quantitatively predict microsatellite instability in colorectal cancer.

Rationale and objectives: Microsatellite instability (MSI) stratification can guide the clinical management of patients with colorectal cancer (CRC). This study aimed to establish a radiomics model for predicting the MSI status of patients with CRC before treatment.

Materials and methods: This retrospective study was performed on 366 patients diagnosed with CRC who underwent preoperative magnetic resonance imaging (MRI) and immunohistochemical staining between February 2016 and September 2023. The participants were divided randomly into training and testing cohorts in a 7:3 ratio. The tumor volume of interest (VOI) was manually delineated on T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) sequences using 3D Slicer software, and radiomics features were extracted. Feature selection was performed using the least absolute shrinkage and selection operator method. A radiomics nomogram was developed using multiple logistic regression, and the predictive performance of the models was evaluated and compared using receiver operating characteristic curves. The calibration curve, clinical decision curve analysis (DCA) and clinical impact curve (CIC) were used to evaluate the clinical application value of the model.

Results: The radiomics normogram combined with history of chronic enteritis, tumor location, MR-reported inflammatory response, D2-40, carcinoembryonic antigen, tumor protein 53, and monocyte was an excellent predictive tool. The area under the curve for the training and testing cohorts were 0.927 and 0.984, respectively. The DCA and CIC demonstrated favorable clinical application and net benefit.

Conclusions: A radiomics nomogram based on T2WI and ADC sequences and clinicopathologic features can effectively and noninvasively predict the MSI status in CRC. This approach helps clinicians in stratifying CRC patients and making clinical decisions for personalized treatment.