Identification of histological features of ovarian high-grade serous carcinoma with homologous recombination deficiency using artificial intelligence: A retrospective analysis

Purpose This study aimed to identify the morphological features of ovarian high-grade serous carcinoma (HGSC) based on homologous recombination (HR) using artificial intelligence (AI). Methods Seventy-seven patients with HGSC who underwent HR status testing and surgery between 2006 and 2024 were included. One hematoxylin and eosin-stained slide per case, containing a sufficient volume of tumor tissue, was digitized. Tumor areas were automatically detected and annotated using AI. Nuclei in the tumor area were detected using AI. The area of each nucleus and the total number of nuclei were calculated automatically. A trained classifier determined the ratio of the tumor area to HR deficiency (HRD). Receiver operator characteristic curve established optimum cutoff value for average nucleus size (µm ² ), average nucleus count per area (count/mm ² ), and HRD area ratio (%). Results The area under the curve of average nucleus size, average nucleus count per tumor area, and HRD area ratio to the tumor area for the diagnosis of HRD were 0.704, 0.668, and 0.470, respectively, with cut-offs of 52.4 µm ² , 5,610 count/mm ² , and 40.0%, respectively. The HRD group had a smaller average nucleus size and larger average nucleus count per area than the HR proficiency group ( p  < 0.01 and p  = 0.03, respectively). The sensitivity and specificity for diagnosing HRD using the combined cutoff values for the average nucleus size and average nucleus count were 43.1% and 100.0%, respectively. Conclusion AI can identify the morphological features of HGSC with HRD and detect subtle tumor differences related to different genetic backgrounds.