Abstract Introduction: Ovarian cancer is a lethal gynecological malignancy with a substantial economic burden. Accurate histopathological subtype classification is critical for precision therapy and prognostication. Traditional pathological diagnosis relies on subjective interpretation, which is vulnerable to significant interobserver variability. Convolutional neural networks leveraging residual architectures enable automated, objective feature extraction from histopathological images. This study investigates ResNet18-based ovarian cancer subtype classification to accelerate diagnostic accuracy and support evidence-based clinical decision-making. Methods: Anonymized histopathological images from the University of British Columbia Ovarian Cancer Subtype Classification dataset encompassing five ovarian cancer subtypes (n=513) were procured. The dataset was randomly proportioned into training (60%), validation (20%), and testing (20%) cohorts. Images underwent comprehensive preprocessing and augmentation prior to training using the ResNet18 architecture. The diagnostic performance was assessed using accuracy, precision-recall, F1 and F2-scores, and area under the receiver operating characteristic curve (AUROC) on both validation and test sets. The trained model was deployed in a universally accessible, cross-platform application for expert validation globally. Results: ResNet18 demonstrated robust discriminative capability across ovarian cancer subtypes, achieving 98% training accuracy and 82% validation accuracy. Precision-recall analysis showed excellent performance, especially for the endometrioid subtype (0.95). These results indicate ResNet18's potential to augment histopathology workflows, facilitating equitable ovarian cancer subtype stratification. Conclusion: ResNet18-based analysis provides automated, accurate ovarian cancer diagnosis. High validation accuracy and strong AUROC scores across histological subtypes indicate efficacy in standardizing diagnostic interpretation. Integration into clinical workflows could reduce diagnostic variability and support precision-guided therapy. Further validation in prospective multicenter cohorts will translate this into an effective diagnostic tool. Citation Format: Elangovan Krishnan, Jansi R. Sethuraj, Sophia Ahmed, Gowrishankar Palaniswamy, Muhammad Waqas Khan, Aravind Raghavan, Tayyiba Wasim. High-fidelity detection and classification of ovarian cancer from histopathological images using Artificial Intelligence abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 6341.
Krishnan et al. (Fri,) studied this question.