Network Evolution Model-based prediction of tumor mutation burden from radiomic-clinical features in endometrial cancers

BACKGROUND

Endometrial Cancer (EC) is one of the most prevalent malignancies that affect the female population globally. In the context of immunotherapy, Tumor Mutation Burden (TMB) in the DNA polymerase epsilon (POLE) subtype of this cancer holds promise as a viable therapeutic target.

METHODS

We devised a method known as NEM-TIE to forecast the TMB status of patients with endometrial cancer. This approach utilized a combination of the Network Evolution Model, Transfer Information Entropy, Clique Percolation (CP) methodology, and Support Vector Machine (SVM) classification. To construct the Network Evolution Model, we employed an adjacency matrix that utilized transfer information entropy to assess the information gain between nodes of radiomic-clinical features. Subsequently, using the CP algorithm, we unearthed potentially pivotal modules in the Network Evolution Model. Finally, the SVM classifier extracted essential features from the module set.

RESULTS

Upon analyzing the importance of modules, we discovered that the dependence count energy in tumor volumes-of-interest holds immense significance in distinguishing TMB statuses among patients with endometrial cancer. Using the 13 radiomic-clinical features extracted via NEM-TIE, we demonstrated that the area under the receiver operating characteristic curve (AUROC) in the test set is 0.98 (95% confidence interval: 0.95-1.00), surpassing the performance of existing techniques such as the mRMR and Laplacian methods.

CONCLUSIONS

Our study proposed the NEM-TIE method as a means to identify the TMB status of patients with endometrial cancer. The integration of radiomic-clinical data utilizing the NEM-TIE method may offer a novel technology for supplementary diagnosis.