Generalizable deep learning models for epithelial ovarian carcinoma classification

Ovarian carcinoma is the deadliest cancer of the female reproductive system in North America. There are five major histological subtypes which require different treatments. Pathologists diagnose these histotypes by examining hematoxylin and eosin (H&E)-stained whole slide images (WSIs) of tissue. However, histotype diagnosis is not simple, with poor interobserver agreement between general pathologists (Cohen’s kappa 0.54-0.67). We hypothesize that latest machine learning (ML)-based image classification models may be able to recognize ovarian carcinoma histotype sufficiently well that they could aid pathologists in diagnosis. However, the color variation of H&E-stained tissues, especially those from different centers/hospitals, is a longstanding challenge for applications of AI in digital pathology. First, we investigate eight color normalization algorithms as a preprocessing step for artificial intelligence (AI)-based classification. Using multiple datasets of different cancer types, reference images, and cross-validation splits, we show that color normalization significantly improves the classification accuracy of WSIs when the train and test data are from separate institutions (ovarian cancer: 0.25 AUC increase, p = 1.6 e-05, pleural cancer: 0.21 AUC increase, p = 1.4 e-10). Furthermore, we introduce a novel augmentation strategy by mixing color-normalized images using three easily accessible algorithms that consistently improves the diagnosis of test images from external institutions. Secondly, we train four different deep convolutional neural networks to automatically classify H&E-stained images of epithelial ovarian carcinoma using the largest training dataset to date (948 slides corresponding to 485 patients). Performance is assessed on an independent test set of 60 patients from another institution. The best performing model achieves a mean diagnostic concordance of 80.97% (Cohen’s kappa 0.7547). As well, in 4 of 8 cases misclassified by ML from the external dataset, two expert subspecialty pathologists rendered diagnoses, based on blind review of the WSIs, that agree with AI rather than the integrated reference diagnosis. Our results indicate that color normalization can reliably improve AI-based diagnosis of WSIs sourced from multiple centers, and specifically that an ML-based ovarian carcinoma classifier is ready for clinical validation studies as an adjunct for informing histotype diagnosis, thereby supporting histotype-specific ovarian cancer treatment and accordingly reduce the deadliness of this disease.Applied Science, Faculty ofBiomedical Engineering, School ofGraduat