Role of Apparent Diffusion Coefficient (Adc) and Mri-Derived Parameters in Identifying P53-Abnormal Subtypes of Endometrial Cancer Publisher



Parviz S ; Sepidbar S ; Sarmadi S ; Nili F ; Zeinalkhani F ; Tahamtan M ; Shakiba M ; Malek M
Source: European Journal of Radiology Open Published:2026

Abstract

Background: Non-invasive imaging techniques such as magnetic resonance imaging (MRI) offer advantages over repeated biopsies for assessing tumor characteristics, though histological and molecular analysis remain the diagnostic gold standard. Since P53 expression is an important prognostic factor linked to the histopathological patterns of endometrial cancer (EC), using MRI-derived indices to distinguish between TP53-mutated (P53abn) and non-P53abn subtypes is clinically practical. This study aimed to determine the predictive value of MRI-based markers, especially apparent diffusion coefficients (ADC), for differentiating P53abn and non-P53abn subtypes in patients with EC. Methods: This retrospective study was performed on 115 patients with known EC. All MR imaging studies were performed on 1.5 T MR imaging units. The presence of p53abn subtype was determined by immunohistochemical staining (IHC). Data were analyzed using R version 4.4.1. Results: A statistically significant difference was observed in mean ADC values between patients with and without p53abn by IHC, with notably lower ADC values in the P53abn group. However, no significant association was identified between P53abn status and other imaging parameters, such as mass T2, outer myometrium T2, or the mass-to-psoas intensity ratio. Multivariable logistic regression analysis identified ADC value and tumor’s histological subtype as the primary determinants of p53abn EC. ROC curve analysis further demonstrated that ADC measurement could effectively predict p53abn. The best cutoff point of ADC to discrimination was 695, yielding a sensitivity of 100% and specificity of 83%. Conclusion: In patients with EC, assessing tumor ADC values on MRI can serve as a valuable tool for predicting p53abn subtype. © 2026 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license. http://creativecommons.org/licenses/by/4.0/