Deep Learning Algorithm‑Based MRI Radiomics and Pathomics for Predicting Microsatellite Instability Status in Rectal Cancer: A Multicenter Study.

RATIONALE AND OBJECTIVES

To develop and validate multimodal deep-learning models based on clinical variables, multiparametric MRI (mp-MRI) and hematoxylin and eosin (HE) stained pathology slides for predicting microsatellite instability (MSI) status in rectal cancer patients.

MATERIALS AND METHODS

A total of 467 surgically confirmed rectal cancer patients from three centers were included in this study. Patients from center 1 were randomly divided into a training set (242 patients) and an internal validation (invad) set (105 patients) in a 7:3 ratio. Patients from centers 2 and 3 (120 patients) were included in an external validation (exvad) set. HE and immunohistochemistry (IHC) staining were analyzed, and MSI status was confirmed by IHC staining. Independent predictive factors were identified through univariate and multivariate analyses based on clinical evaluations and were used to construct a clinical model. Deep learning with ResNet-101 was applied to preoperative MRI (T2WI, DWI, and contrast-enhanced T1WI sequences) and postoperative HE-stained images to calculate deep-learning radiomics score (DLRS) and deep-learning pathomics score (DLPS), respectively, and to DLRS and DLPS models. Receiver operating characteristic (ROC) curves were plotted, and the area under the curve (AUC) was used to evaluate and compare the predictive performance of each model.

RESULTS

Among all rectal cancer patients, 82 (17.6%) had MSI. Long diameter (LD) and pathological T stage (pT) were identified as independent predictors and were used to construct the clinical model. After undergoing deep learning and feature selection, a final set of 30 radiomics features and 30 pathomics features were selected to construct the DLRS and DLPS models. A nomogram combining the clinical model, DLRS, and DLPS was created through weighted linear combination. The AUC values of the clinical model for predicting MSI were 0.714, 0.639, and 0.697 in the training, invad, and exvad sets, respectively. The AUCs of DLPS and DLRS ranged from 0.896 to 0.961 across the training, invad, and exvad sets. The nomogram achieved AUC values of 0.987, 0.987, and 0.974, with sensitivities of 1.0, 0.963, and 1.0 and specificities of 0.919, 0.949, and 0.867 in the training, invad, and exvad sets, respectively. The nomogram outperformed the other three models in all sets, with DeLong test results indicating superior predictive performance in the training set.

CONCLUSION

The nomogram, incorporating clinical data, mp-MRI, and HE staining, effectively reflects tumor heterogeneity by integrating multimodal data. This model demonstrates high predictive accuracy and generalizability in predicting MSI status in rectal cancer patients.