Precision Nanomedicine · Published 2026-07-18 · DOI 10.33218/001c.165246
Federico Greco, Chiara Noemi Greco, Chiara Pallara, Antonia Marini, Roberta Montefrancesco, Luca Giordano, Arnaldo Scardapane
Ovarian cancer is frequently associated with recurrence and chemoresistance, representing a major clinical challenge. Among the mechanisms underlying treatment resistance, ATP-binding cassette subfamily G Member 2 (ABCG2) plays an important role in drug efflux and tumor adaptation. At the same time, radiomics has emerged as a non-invasive approach capable of identifying imaging biomarkers that may reflect the molecular characteristics of tumors. This study investigated the relationship between computed tomography (CT)-derived radiomic features and ABCG2 expression in ovarian cancer. This retrospective radiogenomic study included 70 patients from the Cancer Genome Atlas with available contrast-enhanced CT images and matched gene expression data. A single axial CT slice showing the largest tumor area was selected for analysis. Tumors were manually segmented using Horos, and the statistical analysis included univariate testing with false discovery rate correction and exploratory multivariable modeling using LASSO and ridge logistic regression. Five radiomic features showed nominally significant differences between ABCG2-positive and ABCG2-negative tumors, mainly involving tumor shape and attenuation characteristics. However, none remained significant after correction for multiple comparisons. Multivariable models showed limited predictive performance, with area under the curve values of 0.508 for LASSO and 0.598 for ridge regression. Although predictive performance was modest, CT-derived radiomic features appeared to capture subtle imaging differences associated with ABCG2 expression. These findings support the potential role of radiomics as a non-invasive tool for exploring molecular tumor phenotypes in ovarian cancer.
Abstract from DOAJ. Public domain (CC0 1.0).
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Greco, F., Greco, C., Pallara, C., et al. (2026). Radiogenomic Analysis of Chemoresistance in Ovarian Serous Cystadenocarcinoma Using CT-Derived Radiomic Features: Associations with ABCG2 Expression. Precision Nanomedicine. https://doi.org/10.33218/001c.165246