Purpose: Tumor heterogeneity may be responsible for poor response to treatment and adverse prognosis in women with HGOEC. The purpose of this study is to propose an automated classification system that allows medical experts to automatically identify intratumoral areas of different cellularity indicative of tumor heterogeneity.
Methods: Twenty-two patients underwent dedicated pelvic MRI, and a database of 11,095 images was created. After image processing techniques were applied to align and assess the cancerous regions, two specific imaging series were used to extract quantitative features (radiomics). These features were employed to create, through artificial intelligence, an estimator of the highly cellular intratumoral area as defined by arbitrarily selected apparent diffusion coefficient (ADC) cut-off values (ADC < 0.85 × 10-3 mm2/s).
Results: The average recorded accuracy of the proposed automated classification system was equal to 0.86.
Conclusion: The proposed classification system for assessing highly cellular intratumoral areas, based on radiomics, may be used as a tool for assessing tumor heterogeneity.
Keywords: artificial intelligence classification; medical image processing; ovarian epithelial cancer; quantitative characteristics; radiomics; tumor heterogeneity.