Hadi Nia

Ph.D.

Boston University

Assistant Professor

NLST (Learn more about this study)

NLST-471

Jan 18, 2019

A novel approach to lung cancer detection and prediction using biomechanics-enhanced artificial intelligence

Deep learning, the leading machine learning tool in medical image analysis, was named as one of the 10 breakthrough technologies in 2013. The majority of conventional research on lung tumor analytics is focused on the tumor segmentation in CT images. The main goals of the current methodologies are to utilize efficient artificial intelligence and image processing algorithms to automate tumor detection and process large amounts of imaging data rapidly. This approach suffers from two limitations: (i) the best achievable accuracy is the human accuracy since the models are calibrated – or “trained” as in machine learning terminologies – by images processed by radiologists; (ii) the existing algorithms act as a black box and lack intuition, a major obstacle for FDA approval. In the proposed project, we will utilize biophysical and computational modeling to enhance artificial intelligence algorithms to process low dose CT data to go beyond the mere “tumor segmentation” objective. Specifically, our goals are to (i) improve early detection and (ii) predict key outcomes including incidence and overall survival, beyond the prediction ability of trained pathologists. The biophysical model provides the personalized map of the mechanical properties of the lung from the input CT images. The biophysical model is also capable of predicting the progression of chronic obstructive pulmonary disease (COPD), a major risk factor for lung cancer, based on a network model, which has been previously validated and published. By utilizing the additional biophysics-based information that the computational model provides and feeding them along with the original CT images of the patient, we hypothesize that the prediction power of the deep learning algorithm will be improved significantly.