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Identifying histological features of lung cancer using deep learning

Principal Investigator
Name
Jennifer Beane
Degrees
Ph.D.
Institution
Trustees of Boston University
Position Title
Assistant Professor
Email
About this CDAS Project
Study
NLST (Learn more about this study)
Project ID
NLST-794
Initial CDAS Request Approval
May 11, 2021
Title
Identifying histological features of lung cancer using deep learning
Summary
Our group is interested in developing deep learning frameworks to learn histologic patterns in lung tissue. We have developed different methods that can be applied to H&E whole slide images to predict lung cancer and lung cancer subtype. We would like to gain access to the NLST pathology images to test the performance of our methods that were developed on the data from the NCI’s Clinical Proteomic Tumor Analysis consortium (CPTAC). As part of our work, we are also interested in understanding preinvasive lung lesions. Our overall goal is to look for lung cancer-associated histologic features in preinvasive lung lesions and assess whether or not they can enhance biomarker of preinvasive lesion progression.
Aims

Aim 1. Test the performance patch-based deep learning frameworks to predict lung cancer and lung cancer subtypes on H&E whole slide images of lung tissue from the NLST.

Aim 2. Test the performance whole slide and graph-based deep learning frameworks to predict lung cancer and lung cancer subtypes on H&E whole slide images of lung tissue from the NLST.

Collaborators

Vijaya Kolachalama, Ph.D.
Assistant Professor
Boston University School of Medicine

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