The value of Clinical-and DWI-based Radiomics Nomogram to predict Pathologic Upgrading in Biopsy-Proven Endometrial Cancer

Abstract

Abstract Background: It is important for biopsy formal endometrial cancer patients, especially young patients of childbearing age to determine the preservation of fertility and predict pathological escalation. Purpose: This study's goal was to determine the viability and effectiveness of a non-invasive quantitative imaging evaluation model built using the Diffusion Weighted Image (DWI) technique and based on Radiomics signatures and clinical parameters Analysis to evaluate Endometrial Cancer (EC) with Biopsy-Proven Pathologic Upgrading. Method: From January 2018 to December 2021, a total of 76 patients with endometrial cancer who had undergone surgery for the disease were retrospectively recruited (training cohort, n = 53; validation cohort, n = 23). The diffusion-weighted image (DWI) served as the source for the Radiomics features. All images were imported into 3D-slicer for whole tumor Segmentation and were used for radiomics feature extraction. Radiomic features were selected in target tumor volumes to build Radscore using the least absolute shrinkage and selection operator (LASSO) and Cox regression analysis Logistic regression, Next building a combined model incorporating rad-scores and clinical risk factors, compared with Radscore model, the clinical model. The models were evaluated by the receiver operating characteristic curve, and calibration curve as well as verified the model in the verification group. Results: AUC for identifying non-pathologic upgrading and pathologic upgrading in the training cohort was 0.606 and in the validation cohort was 0.708, Three of the 107 texture feature were retrieved and 3 parameters were preserved to create the Radscore. With the incorporation of clinical risk factors, the nomogram's AUC for the training and validation cohorts were 0.870 and 0.808, respectively. Both values were significantly higher than the AUC of the clinical model in these cohorts (0.830 and 0.815). The nomogram's training cohort and validation cohort's sensitivity and specificity were 0.938, 0.730, 0.900, and 0.769, respectively. The calibration curves for the nomogram had a good agreement. Conclusions: The Nomogram based on the Radiomics-clinical model in predicting Pathologic Upgrading in Biopsy-Proven Endometrial Cancer with high discriminatory ability.