A Novel Approach for Estimating Ovarian Cancer Tissue Heterogeneity through the Application of Image Processing Techniques and Artificial Intelligence

Simple Summary High-grade ovarian epithelial cancer (HGOEC) is considered to be among the most fatal gynecological cancers, and it is associated with poor response to treatment and adverse prognosis, possibly due to marked intratumoral heterogeneity. The aim of this study is to present a novel technique that can assess intratumoral cellularity based on quantitative features extracted from medical images, denoted as radiomics, advanced image processing and artificial intelligence algorithms, in an attempt to offer biomedical engineers and health professionals a tool for personalized medicine. According to our results, the average accuracy rating of the proposed method in our study population (n = 22) was over 85%. 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 x 10(-3) mm(2)/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.