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

Defeng Liu; Linsha Yang; Dan Du; Tao Zheng; Lanxiang Liu; Zhanqiu Wang; Juan Du; Yanchao Dong; Huiling Yi; Yujie Cui

doi:10.3389/fonc.2022.813069

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

Front Oncol. 2022 Mar 31:12:813069. doi: 10.3389/fonc.2022.813069. eCollection 2022.

Authors

Defeng Liu¹, Linsha Yang¹, Dan Du¹, Tao Zheng¹, Lanxiang Liu¹, Zhanqiu Wang¹, Juan Du¹, Yanchao Dong², Huiling Yi¹, Yujie Cui¹

Affiliations

¹ Medical Imaging Center, First Hospital of Qinhuangdao, Qinhuangdao, China.
² Department of Intervention, First Hospital of Qinhuangdao, Qinhuangdao, China.

Abstract

Background: Relapse 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.

Purpose: To develop a multi-parameter magnetic resonance imaging (MRI) radiomics-based nomogram model to predict 5-year progression-free survival (PFS) in EC.

Methods: For 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 (Model^N) 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).

Results: The Model^N 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 (Model^C), the discrimination ability of Model^N 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 (Model^R), Model^N 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).

Conclusion: This multi-parameter nomogram model incorporating clinical and cMR findings is a valid method to predict 5-year PFS in patients with EC.

Keywords: endometrial cancer; magnetic resonance imaging; nomogram; progression-free survival; radiomics.