Pre-treatment CT-based radiomics nomogram for predicting microsatellite instability status in colorectal cancer

Objectives Stratification of microsatellite instability (MSI) status in patients with colorectal cancer (CRC) improves clinical decision-making for cancer treatment. The present study aimed to develop a radiomics nomogram to predict the pre-treatment MSI status in patients with CRC. Methods A total of 762 patients with CRC confirmed by surgical pathology and MSI status determined with polymerase chain reaction (PCR) method were retrospectively recruited between January 2013 and May 2019. Radiomics features were extracted from routine pre-treatment abdominal pelvic computed tomography (CT) scans acquired as part of the patients' clinical care. A radiomics nomogram was constructed using multivariate logistic regression. The performance of the nomogram was evaluated using discrimination, calibration, and decision curves. Results The radiomics nomogram incorporating radiomics signatures, tumor location, patient age, high-density lipoprotein expression, and platelet counts showed good discrimination between patients with non-MSI-H and MSI-H, with an area under the curve (AUC) of 0.74 [95% CI, 0.68-0.80] in the training cohort and 0.77 [95% CI, 0.68-0.85] in the validation cohort. Favorable clinical application was observed using decision curve analysis. The addition of pathological characteristics to the nomogram failed to show incremental prognostic value. Conclusions We developed a radiomics nomogram incorporating radiomics signatures and clinical indicators, which could potentially be used to facilitate the individualized prediction of MSI status in patients with CRC.

Automated summary

The study developed a radiomics nomogram incorporating radiomics signatures and clinical indicators for predicting pre-treatment MSI status in CRC patients. The nomogram showed good discrimination between non-MSI-H and MSI-H patients with AUC of 0.74 in the training cohort and 0.77 in the validation cohort. Decision curve analysis demonstrated favorable clinical application of the nomogram, while the addition of pathological characteristics did not provide additional prognostic value.