AUTHOR=Tarbaeva N. V. , Manaev A. V. , Ivashchenko K. V. , Platonova N. M. , Beltsevich D. G. , Pachuashvili N. V. , Urusova L. S. , Mokrysheva N. G. 

TITLE=The value of CT texture analysis in predicting mitotic activity and morphological variants of adrenocortical carcinoma

JOURNAL=Frontiers in Radiology

VOLUME=Volume 5 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/radiology/articles/10.3389/fradi.2025.1635425

DOI=10.3389/fradi.2025.1635425

ISSN=2673-8740

ABSTRACT=IntroductionAdrenocortical carcinoma presents significant diagnostic challenges due to its histological heterogeneity and variable clinical behavior. This study aimed to evaluate the diagnostic value of radiomic features in predicting mitotic activity (low/high-grade) and morphological variants (conventional, oncocytic, myxoid) of adrenocortical carcinoma.Materials and methodsA retrospective analysis of 32 patients with histologically confirmed ACC (18 conventional, 9 oncocytic and 5 myxoid cases) was performed, with mitotic data available for 25 cases (13 low-grade and 12 high-grade cases). Radiomic features including Gray-Level Co-occurrence Matrix (GLCM), Run-Length (GLRLM), Size-Zone (GLSZM), Dependence (GLDM), Neighboring-Tone (NGTDM) and first order features were extracted from four-phase CT using PyRadiomics after manual 3D segmentation. Statistical analysis included Mann–Whitney U, Kruskal–Wallis tests, ROC curve (AUC, sensitivity, specificity) and PPV, NPV assessment.ResultsOur analysis demonstrated statistically significant differences between tumor grades with firstorder_Skewness (AUC = 0.924, 95% CI: 0.819–0.986; p = 0.005) showing high predictive performance in the venous phase. Radiomic features did not show statistically significant differences between morphological variants of ACC after adjustment for multiple comparisons.ConclusionOur results confirm the value of CT radiomics for preoperative stratification of ACC grade, but the question of differentiation of morphological variants remains unresolved and requires further validation in larger cohorts.