Deep learning for identification of spread through air spaces (STAS) and prediction of survival of lung adenocarcinoma using pathology images
In this project we plan to use lung pathology images from three different datasets to build deep learning models for identification of STAS, including the NLST pathology images, images from The Cancer Genome Atlas (TCGA) lung adenocarcinoma cohort, and images from our collaboration hospital (Taipei Veterans General Hospital). We first perform image labeling of STAS and tumor areas to provide proper annotations of data for building deep learning models. In addition, we plan to adopt two kinds of deep learning strategies for effective identification of STAS on lung pathology images: (1) object detection based models, such as Fast-RCNN, YOLO, RetinaNet, CornorNet, CenterNet, etc . (2) segmentation based approaches, e.g., Mask-RCNN, YOLOACT, UNet, DeepLab, etc. Note that, the segmentation models can be further adopted to identify tumor areas. Although those models are built based on sampling region of interest (ROI) on whole slide images (WSI), we shall apply the models to the usage of WSI. Furthermore, since STAS is a well-established prognostic marker in lung adenocarcinoma patients, we also plan to perform quantitative analysis of STAS, and investigate their correlation with patient prognosis, using clinical outcome data from NLST, TCGA, and our collaboration hospital (Taipei Veterans General Hospital).
Aim #1: Review pathology whole slide images from NLST, TCGA and our collaboration hospital and perform image labeling to annotate STAS and tumor areas
Aim #2: Use the annotated images and adopt various image object detection and segmentation approaches to train deep learning models for identification of STAS
Aim #3: Explore the relationship between STAS on pathology images and survival data
Yi-Chen Yeh, M.D., Taipei Veterans General Hospital, National Yang-Ming University