Multi-Parameter MR Radiomics Based Model to Predict 5-Year Progression-Free Survival in Endometrial Cancer

Abstract

BackgroundRelapse is the major cause of mortality in patients with resected endometrial cancer (EC). There is an urgent need for a feasible method to identify patients with high risk of relapse.PurposeTo develop a multi-parameter magnetic resonance imaging (MRI) radiomics-based nomogram model to predict 5-year progression-free survival (PFS) in EC.MethodsFor this retrospective study, 202 patients with EC followed up for at least 5 years after hysterectomy. A radiomics signature was extracted from T2-weighted imaging (T2WI), apparent diffusion coefficient (ADC) and a dynamic contrast-enhanced three-dimensional volumetric interpolated breath-hold examination (3D-VIBE). The radiomics score (RS) was calculated based on the least absolute shrinkage and selection operator (LASSO) regression. We have developed a radiomics based nomogram model (ModelN) incorporating the RS and clinical and conventional MR (cMR) risk factors. The performance was evaluated by the receiver operating characteristic curve (ROC), calibration curve and decision curve analysis (DCA).ResultsThe ModelN demonstrated a good calibration and satisfactory discrimination, with a mean area under the curve (AUC) of 0.840 and 0.958 in the training and test cohorts, respectively. In comparison with clinical prediction model (ModelC), the discrimination ability of ModelN showed an improvement with P < 0.001 for the training cohort and P=0.032 for the test cohort. Compared to the radiomics prediction model (ModelR), ModelN discrimination ability showed an improvement for the training cohort with P = 0.021, with no statistically significant difference in the test cohort (P = 0.106). Calibration curves suggested a good fit for probability (Hosmer–Lemeshow test, P = 0.610 and P = 0.956 for the training and test cohorts, respectively).ConclusionThis multi-parameter nomogram model incorporating clinical and cMR findings is a valid method to predict 5-year PFS in patients with EC.

An Integrated Clinical‐MR Radiomics Model to Estimate Survival Time in Patients With Endometrial Cancer

“…Most of the published studies have focused on addressing the classification problem in endometrial cancer using radiomics 17–19 . Studies have applied this radiomic technology to endometrial cancer survival prediction models 32,33 . Fasmer et al developed an MRI‐based whole‐volume tumor radiomic signature for the prediction of high‐risk features 19 .…”

“… 17 , 18 , 19 Studies have applied this radiomic technology to endometrial cancer survival prediction models. 32 , 33 Fasmer et al developed an MRI‐based whole‐volume tumor radiomic signature for the prediction of high‐risk features. 19 Radiomic features were studied to predict poor progression‐free survival.…”

Advances in Radiomics Research for Endometrial Cancer: A Comprehensive Review

“…A retrospective study encompassing 53 EC patients [64] integrated PET/CT radiomics features with certain clinical characteristics to formulate a model predicting disease progression, with the k-nearest neighbors (kNN) machine learning algorithm propelling it to the highest performance tier with an AUROC of 0.890. Liu and team [65] devised a column chart model to forecast 5-year progressionfree survival (PFS), utilizing radiomics features extracted from a retrospective analysis of 202 EC patients, outclassing a basic clinical prediction model with AUROCs of 0.840 (training set) and 0.958 (testing set). Furthermore, Li et al [66] selected three radiomics features from T2WI MRI and amalgamated them with two clinical variables to create a Cox proportional hazards (CPH) model for survival time prediction, which exhibited superior performance in testing compared to the clinical model, with an AUC of 0.727 against an AUROC of 0.624.…”

Utility of the Diffusion Weighted Sequence in Gynecological Imaging: Review Article

“…Quantitative biomarkers using DWI, dynamic contrast MRI, and IVIM refine diagnostic performance of MRI for risk stratification of endometrial cancer [ 47 ]. Another study by Liu et al, demonstrated that radiomics based on multiparametric MRI including DWI is helpful in predicting progression-free survival in endometrial cancer patients [ 48 ].…”