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Deep Learning for Whole Slide Image and Clinical Outcome Analysis in Lung Cancer Patients

Principal Investigator
Name
Min Yuan
Degrees
Ph.D.
Institution
Anhui Medical University
Position Title
Professor
Email
About this CDAS Project
Study
NLST (Learn more about this study)
Project ID
NLST-771
Initial CDAS Request Approval
Mar 22, 2021
Title
Deep Learning for Whole Slide Image and Clinical Outcome Analysis in Lung Cancer Patients
Summary
Digital pathology, particularly with the use of digital whole slide images (WSIs), has emerged as a novel tool in clinical studies and has potential to accurately and objectivity predict various clinical endpoints and patient prognosis, providing new insights on pathology and cancer biology, and allowing further risk stratification of patients and refinement of treatment decisions in clinic. Digital pathology is primary based on deep learning (eg, convolutional neural networks (CNN) and graph neural networks (GNN), etc). Courtiol and co-workers developed a deep convolutional neural network based on whole-slide digitized images to accurately predict the overall survival of mesothelioma patients without annotations from pathologists. Saillard et al. have further refined this model and demonstrated that artificial intelligence can help improve the prediction of the prognosis of hepatocellular carcinoma.

Lung cancer is the most common cause of cancer-related death. There is an urgent need to develop reliable pathology image-based tools or biomarkers in routine clinical practice to further assist identification of high-risk patients in lung cancer patients. This study is important because it could utilize whole-slide histological images, and provide more accurate prediction than conventional clinical and biological approaches. This approach can be fully automated without pathologist's guidance and could be used in clinical studies.
Aims

The objective in this research will be to predict clinical outcomes (e.g., survival) and clinical characteristics (eg. cancer staging) in lung cancer patients using H&E whole slide images. Specifically,
a. To construct convolutional neural network (CNN or/and GNN) based deep learning models to extract features from H&E image slides
b. To train a deep-learning neural network (DNN) or/and statistical prediction models to predict clinical outcomes and clinical characteristics for lung cancer patients
c. To validate the prediction models using independent datasets
d. To identify imaging features/phenotypes that are of predictive values for lung cancer

Collaborators

Prof. Yaning Yang; University of Science and Technology of China
Prof. Hong Zhang; University of Science and Technology of China