Classifying malignant pulmonary nodules using biophysics-enhanced artificial intelligence - SUMMARY: Lung cancer is the most common cause of cancer death in the United States with an estimated 140,000 deaths in 2020. While it has been demonstrated that lung screening reduces the mortality by 20%, accurate classification of malignant tumors remains an unmet need due to high rate of false positive cases. Current classification approaches by means of computed tomography (CT) screenings are based on statistical predictive models, and more recently artificial intelligence. Improving the classification accuracy of malignant tumors will reduce costs and the risk of mortality and guide clinical decision making. Here, we propose a novel framework to further improve the predictive power of current models by enriching the input information with biophysics-based computational models. The proposed computational model generates orthogonal information, which is based on laws of physics, and hence intrinsically unlearnable by artificial intelligence. We propose augmenting biophysical information since numerous studies have demonstrated that the tumor progression is strongly affected by the physical microenvironment in which they grow. Our overall objective here is to propose the physiological mechanical forces in lung as an informative and orthogonal biomarker to the existing input variables in state-of-the-art approaches to improve the prediction of malignancy risk in pulmonary nodules. Our central hypothesis is that coupling a biophysics-based computational model with the existing statistical models and artificial intelligence approaches will improve their prediction power in classifying malignant pulmonary nodules. Our hypothesis is based on published works and our preliminary data that the mechanical stresses in the lung are strongly correlated with both tumor incidence and growth. By coupling biophysics-based computational model, we plan to evaluate the improved classification performance in logistic regression model (Aim 1), as a highly interpretable model, and deep convolutional neural network (Aim 2), as a highly predictive model. Coupling biophysics-based computational model to artificial intelligence predictive tools will improve the prediction of power at no added financial and health burden to the patient. This novel approach, proposed here on lung cancer classification, has potential diagnostic and prognostic benefits in other pathological lung conditions such as chronic obstructive pulmonary disease (COPD), fibrosis, mechanical ventilation, and bacterial and viral infection of the lung.
