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ORIGINAL RESEARCH article

Front. Oncol.
Sec. Cancer Imaging and Image-directed Interventions
Volume 14 - 2024 | doi: 10.3389/fonc.2024.1406858
This article is part of the Research Topic Emerging Fast Medical Imaging Techniques in Radiology View all articles

Multiparametric MRI Radiomics for Predicting Disease-free Survival and High-Risk Histopathological Features for Tumor Recurrence in Endometrial Cancer

Provisionally accepted
Mary Renton Mary Renton Mina Fakhriyehasl Mina Fakhriyehasl Jessica Weiss Jessica Weiss Michael Milosevic Michael Milosevic Stephane Laframboise Stephane Laframboise Marjan Rouzbahman Marjan Rouzbahman Kathy Han Kathy Han Kartik Jhaveri Kartik Jhaveri *
  • University Health Network (UHN), Toronto, Ontario, Canada

The final, formatted version of the article will be published soon.

    Background: Current preoperative imaging is insufficient to predict survival and tumor recurrence in endometrial cancer (EC), necessitating invasive procedures for risk stratification.Purpose: To establish a multiparametric MRI radiomics model for predicting disease-free survival (DFS) and high-risk histopathologic features in EC.Methods: This retrospective study included 71 patients with histopathology-proven EC and preoperative MRI over a 10-year period. Clinicopathology data were extracted from health records. Manual MRI segmentation was performed on T2-weighted (T2W), apparent diffusion coefficient (ADC) maps and dynamic contrast-enhanced T1-weighted images (DCE T1WI). Radiomic feature (RF) extraction was performed with PyRadiomics. Associations between RF and histopathologic features were assessed using logistic regression.Associations between DFS and RF or clinicopathologic features were assessed using the Cox proportional hazards model. All RF with univariate analysis p-value < 0.2 were included in elastic net analysis to build radiomic signatures.The 3-year DFS rate was 68% (95% CI = 57%-80%). There were no significant clinicopathologic predictors for DFS, whilst the radiomics signature was a strong predictor of DFS (p<0.001, HR 3.62, 95% CI 1.94, 6.75). From 107 RF extracted, significant predictive elastic net radiomic signatures were established for deep myometrial invasion (p=0.0097, OR 4.81, 95% CI 1.46, 15.79), hysterectomy grade (p=0.002, OR 5.12, 95% CI 1.82, 14.45), hysterectomy histology (p=0.0061, OR 18.25, 95% CI 2.29,145.24) and lymphovascular space invasion (p<0.001, OR 5.45, 95% CI 2.07, 14.36).Multiparametric MRI radiomics has the potential to create a non-invasive a priori approach to predicting DFS and high-risk histopathologic features in EC.

    Keywords: endometrial cancer, High-risk endometrial cancer, Radiomics, MRI radiomics, Disease-Free Survival

    Received: 25 Mar 2024; Accepted: 22 Jul 2024.

    Copyright: © 2024 Renton, Fakhriyehasl, Weiss, Milosevic, Laframboise, Rouzbahman, Han and Jhaveri. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Kartik Jhaveri, University Health Network (UHN), Toronto, M5G 2C4, Ontario, Canada

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