Jennifer Beane

Ph.D.

Trustees of Boston University

Assistant Professor

NLST (Learn more about this study)

NLST-794

May 11, 2021

Identifying histological features of lung cancer using deep learning

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.

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.