Project Summary/Abstract:
The US Surgeon General has declared pulmonary embolism (PE) a major national health problem, causing more deaths
than breast, colon, and lung cancers. The current diagnostic standard for suspected PE is CT pulmonary angiography
(CTPA). However, the number of CTPA examinations is increasing dramatically, and incorrect CTPA interpretations are
frequent in general practice (10-14% over/under-diagnosis). There is a clinical need to improve the efficiency and
accuracy of PE diagnosis at CTPA. Our central hypothesis is that this clinical need can be addressed by exploiting
computer-radiologist synergy. However, existing computer-aided diagnosis (CAD) methods for PE have serious
deficiencies: they are limited in sensitivity and specificity, incapable of handling PE over-diagnosis, and operating only at
the embolus level¿localizing individual emboli, but PE diagnosis is rendered at the patient-level¿excluding non-PE
patients and dispatching PE-patients to treatment. Therefore, our objective is to overcome these deficiencies with a new
methodology. We have built a strong interdisciplinary team, developed an innovative prototype, and evaluated it through
our pilot clinical studies, demonstrating outstanding performance. This proposed research has three specific aims: 1) boost
our current system’s embolus-level performance with our newly proposed strategies, assisting radiologists in accurately
localizing emboli and facilitating precision medicine through risk stratification; 2) achieve patient-level diagnosis through
our newly developed algorithms, assisting radiologists in quickly excluding negative patients and improving diagnostic
efficiency; and 3) demonstrate clinical benefits of our system by testing specific clinical hypotheses. This research is
innovative because (1) our approach to embolus-level detection fundamentally differs from prior approaches in that it
requires no vessel segmentation, overcoming their limitations; (2) we are pioneering two uncharted areas: PE patient-level
diagnosis and over-diagnosis prevention; we do not perceive any similar objectives in existing NIH grants or publications
in the literature; and (3) this project utilizes our original algorithms and will yield multiple novel algorithms. Our project
is significant because it (1) addresses a major national health problem; (2) develops a new methodology that transcends
the current paradigm from mere detection of emboli to simultaneous patient-level diagnosis, embolus-level detection, and
over-diagnosis prevention, overcoming the deficiencies of the current PE CAD systems; and (3) delivers a next-
generation, high-performance PE CAD system that quickly excludes non-PE patients, accurately localizes emboli, and
actively prevent PE over-diagnosis, thereby enhancing radiologists’ diagnostic capabilities and supporting precision
medicine through risk stratification. Successful completion of the project is expected because (1) we have already made
good progress in algorithm development and clinical evaluation; (2) our approach is carefully crafted on solid algorithmic
and mathematical foundations; (3) our clinical evaluation is rigorously designed; and (4) our team is uniquely capable and
well prepared to conduct this project, which builds upon our innovative research in CAD, pioneering research in
deformable models, and world-renowned PIOPED trials. This research is expected to have important impact on PE-
related clinical practice, development of decision support systems for many diseases, and medical education.