Rachel Jennings

PhD Mathematics

UHG R&D

Director of Data Science

NLST (Learn more about this study)

NLST-529

Jun 21, 2019

Image Classification of LDCT for Computer-aided Lung Cancer Diagnosis

Lung cancer accounts for one-fourth of cancer-related deaths in the United States, whereas pancreatic, breast, and colorectal cancers each account for about one-tenth. Fortunately, chest screening procedures have the capabilities to find pre-cancerous lung nodules—both squamous dysplasia (SD) and atypical adenomatous hyperplasia (AAH)—early enough for removal, resulting in the drastic reduction of lung cancer mortality. Low-dosage computed tomography (LDCT) has become the recommended procedure for screening high-risk patients (current or past smokers aged 55-74 with >30 pack-years and less than 15 years since quitting) annually or more frequently as it demonstrates improved detection capabilities over chest x-rays while maintaining less radiation exposure than a full CT chest scan. However, the high false positive rate (95%) of LDCT interpretation leads to unnecessary biopsies and other tests.

With the recent rise of machine learning in image classification, medicine, cancer detection, and all of these simultaneously (e.g., breast cancer), the field seems ripe for image classification of LDCT scans as either cancerous or non-cancerous. Via standard computer-aided diagnosis (CADx) practices, such classification results could aid medical professionals in determining diagnosis and next steps. Using this dataset, we will train and optimize this model in an effort to maintain high sensitivity, while reducing the false positive rate, as demonstrated on the hold-out set.

Literature and past work in this space suggest convolution-based approaches as a natural and intelligent direction. We will apply neural networks designed from a partial differential equations (PDE) perspective (a subfield with recent promise). We will build a PDE-based machine learning model and other approaches for comparison to classify LDCT images of pulmonary nodules as cancerous or non-cancerous to assist in the early diagnosis of lung cancer