Extracting interpretable features for pathologists using weakly-supervised learning to predict p16 expression in oropharyngeal cancer
Shingo Sakashita, Masahiro Adachi, Tetsuro Taki, Naoya Sakamoto, Motohiro Kojima, Akihiko Hirao, Kazuto Matsuura, Ryuichi Hayashi, Keiji Tabuchi, Shumpei Ishikawa, Genichiro Ishii · Research Square · 2023
Abstract One drawback of existing artificial intelligence (AI)-based histopathological prediction models is the lack of interpretability. The objective of this study is to extract p16-positive oropharyngeal squamous cell carcinoma (OPSCC) features in a form that can be interpreted by pathologists using AI model. We constructed a model for predicting p16 expression using a dataset of whole-slide images from 114 OPSCC biopsy cases. We used the clustering-constrained attention-based multiple-instance learning (CLAM) model, a weakly supervised learning approach. To improve performance, we incorporated tumor annotation into the model (Annot-CLAM) and achieved high performance. Utilizing the image patches on which the model focused, we examined the features of model interest via histopathologic morphological analysis and cycle-consistent adversarial network (CycleGAN) image translation. By using the CycleGAN-converted images, we confirmed that the sizes and densities of nuclei are important features for prediction with strong confidence. This approach improves interpretability in histopathological morphology-based AI models and contributes to the advancement of clinically valuable histopathological morphological features.