Computer-Aided Diagnosis of Pulmonary Embolism

项目概要/摘要： 美国外科医生宣布肺栓塞（PE）是一个主要的国家健康问题，造成更多的死亡 比乳腺癌结肠癌和肺癌都要多目前对疑似PE的诊断标准是CT肺血管造影 （CTPA）。然而，CTPA检查的数量正在急剧增加，并且不正确的CTPA解释也在增加。 常见于全科实践（10-14%诊断过度/不足）。临床上需要提高效率， CTPA时PE诊断的准确性。我们的中心假设是，这种临床需求可以通过利用 计算机和放射科医生的协同作用然而，现有的用于PE的计算机辅助诊断（CAD）方法具有严重的局限性。 缺陷：它们的灵敏度和特异性有限，无法处理PE过度诊断，并且仅在 栓子水平-定位单个栓子，但在患者水平进行PE诊断-不包括非PE 患者和派遣PE患者进行治疗。因此，我们的目标是用一种新的 方法论我们建立了一支强大的跨学科团队，开发了一个创新的原型，并通过 我们的试点临床研究，表现出色。这项研究有三个具体目标：1）促进 我们目前的系统的栓塞水平的性能与我们新提出的战略，协助放射科医生准确地 通过风险分层定位栓塞并促进精准医疗; 2）通过以下方式实现患者水平的诊断： 我们新开发的算法，帮助放射科医生快速排除阴性患者，并提高诊断 效率;和3）通过测试特定的临床假设来证明我们的系统的临床益处。本研究是 创新，因为（1）我们的栓塞水平检测方法与现有方法的根本不同之处在于， 不需要血管分割，克服了其局限性;（2）我们正在开拓两个未知领域：PE患者级 诊断和过度诊断预防;我们在现有的NIH赠款或出版物中没有看到任何类似的目标 在文献中;和（3）这个项目利用我们的原始算法，并将产生多个新的算法。我们的项目 重要的是，它（1）解决了一个重大的国家健康问题;（2）开发了一种新的方法， 当前的模式从单纯检测栓子到同时进行患者级诊断、栓子级检测， 防止过度诊断，克服现有PE CAD系统的不足;（3）提供下一个- 一代高性能PE CAD系统，可快速排除非PE患者，准确定位栓子， 积极防止PE过度诊断，从而提高放射科医生的诊断能力， 通过风险分层进行治疗。预计该项目将顺利完成，因为（1）我们已经 在算法开发和临床评价方面取得了良好进展;（2）我们的方法是在坚实的算法基础上精心制作的 和数学基础;（3）我们的临床评估是严格设计的;（4）我们的团队是独一无二的， 充分准备进行这个项目，它建立在我们在CAD的创新研究，开拓性的研究， 可变形模型和世界知名的PIOPED试验。该研究对我国体育教育的发展具有重要意义。 相关的临床实践，许多疾病的决策支持系统的开发，以及医学教育。

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.