Applied Chest Imaging Laboratory

Classification and Prognostication in Pulmonary Thromboembolism Using Computed Tomography Image Analytics

Grant support: 1R01HL164717

PI: Dr. Farbod Nicholas Rahaghi

Project Information:

Pulmonary thromboembolism remains a significant cause of morbidity and mortality in the western world. While many of the initial symptoms in acute pulmonary embolism (PE) resolves with appropriate treatment, there is increasing awareness of chronic impact of the disease ranging from development of chronic thromboembolic pulmonary hypertension (CTEPH) to persisting dyspnea and exercise impairment. Many patients initially diagnosed with PE may already have chronic disease and inappropriate treatment for acute disease in these cases may be harmful and delay referral to specialized centers with experience in treating chronic disease. On the other hand many patients with acute PE go on to develop chronic disease despite current treatment options and follow-up to insure resolution remains a challenge particularly without the ability to predict who will develop chronic disease. Furthermore, prognostication and selection of treatments can be difficult, particularly in submassive acute PE and CTEPH, particularly with newly emerging treatment choices. Quantitative methods are needed to help define disease trajectories early in presentation, help guide prognostication and treatment and improve our understanding of the pathophysiology of this condition. Computed Tomography (CT) imaging is the cornerstone of evaluation of pulmonary thromboembolism. In acute PE, it is the often the first imaging modality available for assessing treatment options. As the patient recovers, it is used to detect chronic or reoccurring clot guide interventions in chronic disease. Advances in CT imaging quality, image processing (including application of deep learning), coupled with increasing computation power make possible the extraction of a large number of novel features from CT imaging. In this proposal we seek to combine our team’s experience in CT image quantification with multi-center longitudinal data to develop CT imaging features that can identify and predict disease chronicity, its impact on the pulmonary circulation and its response to treatment. In Aim 1 we utilize longitudinal data from three academic hospitals (Brigham and Women’s Hospital, Massachusetts General Hospital, Northwestern University) to assess CT features at presentation that predict the presence or development of chronic disease. In Aim 2, we study both the presentation and follow-up image to build quantitative models of the impact of acute and chronic disease on the pulmonary circulation in order to help with prognostication and improve non-invasive methods of predicting the relevance of persistent disease to the clinical state of patients. In aim 3 we use a combination of longitudinal imaging in CTEPH patients having undergone surgery and patients with pulmonary arterial hypertension to identify patients that would have the most optimal surgical outcomes. We believe that the combination of the features and models developed in these complementary aims will advance our ability to use clinically available CT imaging to improve phenotyping, prognostication and treatment decisions and improve our understanding of the longitudinal progression of pulmonary thromboembolic disease.

People Involved:

Dr. Pietro Nardelli, Dr. Farbod Nicholas Rahaghi, Dr. Raúl San José Estépar, Rubén San José Estépar, Dr. George R Washko