O3C Glass-Class: A Machine-Learning Framework for Prognostic Prediction of Ovarian Clear-Cell Carcinoma

Abstract

Ovarian clear cell carcinoma (OCCC), one of the histopathological types of ovarian cancer, has a poor prognosis when it recurs; however, it is difficult to precisely predict the risk of recurrence. Here, we analyzed pathological images of OCCC to elucidate the relationship between pathological findings and recurrence, and using machine learning, we established a classifier to predict the recurrence and several other prognosis indicators of this disease. In total, 110 patients with OCCC treated with primary surgery at a single institution were enrolled in this study. We used the deep-learning neural networks to process the whole slide images of OCCC obtained by digitally scanning the original hematoxylin and eosin-stained glass slides. The images were preprocessed and used as input to the machine learning pipeline. We fine-tuned its parameters to predict the recurrence, progression-free survival, and the overall survival days of all patients. We predicted the recurrence of OCCC with an overall accuracy of 93%, area under the receiver operating characteristic curve of 0.98, and sensitivity/specificity above 0.92 using Resnet 34. Furthermore, we predicted progression-free survival/overall survival of the patients with ~90% accuracy. In conclusion, our study demonstrates the feasibility of using a machine learning system to predict different features of OCCC samples using histopathological images as input. This novel application provides accurate prognosis information and aids in the development of personalized treatment strategies.