An Integrating of Clinical, Pathological, and Radiomic Approach to Predict the Microsatellite Instability in Rectal Carcinoma

Abstract

Objective: To develop an integrative model with clinical, pathological, and radiomic characteristics to predict the status of microsatellite instability (MSI) in rectal carcinoma (RC). Methods: A cohort of 788 RCs with 97 high MSI status (MSI-H) and 691 microsatellite stable status (MSS) were enrolled. The clinical and pathological characteristics were recorded. The radiomic features were calculated after segmentation of volume of interests and then patients were divided into the training set and validation set with a random proportion of 7:3. The logistic models of simple clinical characteristics (LM-Clin), pathological characteristics (LM-Patho), and radiomic features (LM-Radio) were constructed to distinguish MSI-H from MSS. The relevant radiomic score was calculated. Finally, a integrative nomogram (LM-Nomo) including significant clinical, pathological characteristics, and radiomics was developed. The area under receiver operator curve (AUC) was calculated to evaluate the efficacy of prediction. Results: The AUC of simple LM-Clin including variables of CEA and hypertension and LM-Patho including characteristics of gross type and lymph node metastasis ratio (LNR) was 0.584 (95%CI, 0.549-0.619) and 0.585 (95%CI, 0.550-0.619), which was lower than that of LM-Radio including 12 radiomic features with AUC of 0.737 (95%CI, 0.675-0.799). The LM-Nomo contained CEA, hypertension, LNR, and radiomic score, and the AUC was 0.757 (95%CI, 0.726-0.787). Conclusion: The AUCs of LM-Clin and LM-Patho were disappointing and lower than that of LM-Radio. The LM-Nomo demonstrated the best performance in predicting MSI-H status.

Clinicopathological characteristics of high microsatellite instability/mismatch repair-deficient colorectal cancer: A narrative review

“…In contrast, another study involving the development and validation of a model to predict MSI status by integrating clinical, pathological, and radiomics data from a large cohort of patients with rectal cancer (n = 788) showed that the nomogram provided a moderate ability to predict MSI status. However, the model provided a higher AUC than clinical, pathological, and radiomic features alone (AUCs: 0.757, 0.584, 0.585, and 0.737, respectively) (58). In contrast to previous efforts to develop MSI-prediction models based on tumoral CT-based radiomics, Ma et al (59) developed a machinelearning model to predict MSI status using both tumoral and peritumoral radiomic signatures.…”