Lung Imaging based Risk Score (LunIRIS): Decision support tool for screening CT

基于肺部影像的风险评分 (LunIRIS)：筛查 CT 的决策支持工具

• 批准号: 10171399
• 负责人: Anant Madabhushi
• 金额: --
• 依托单位: LOUIS STOKES CLEVELAND VA MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10171399
• 关键词: 3-Dimensional; Adopted; Anxiety; Architecture; Area; Benign; Biopsy; Blood Vessels; Bronchoscopy; Cancerous; Carcinogens; Chest; Clinical; Clinical Trials; Clinics and Hospitals; Computer software; Computer-Assisted Diagnosis; Data; Detection; Diagnosis; Diagnostic; Discrimination; Dose; Evaluation; Excision; Exposure to; General Population; Glean; Health Promotion; Human; Image; Image Analysis; Lung; Lung CAT Scan; Lung nodule; Machine Learning; Malignant - descriptor; Malignant Neoplasms; Malignant neoplasm of lung; Measurement; Medicare; Morbidity - disease rate; Morphology; Nodule; Operative Surgical Procedures; Outcome; Patients; Pattern; Pattern Recognition; Phenotype; ROC Curve; Reader; Research Personnel; Risk; Role; Scanning; Shapes; Smoke; Smoking; Smoking History; Specificity; Technology; Texture; United States Department of Veterans Affairs; University Hospitals; Validation; Veterans; Visual; X-Ray Computed Tomography; base; care systems; chest computed tomography; cohort; computed tomography screening; computerized; cost; diagnostic accuracy; disorder prevention; follow-up; imaging biomarker; improved; innovation; learning classifier; lung cancer screening; lung imaging; male; military service; military veteran; novel; programs; radiologist; radiomics; routine screening; screening; success; support tools; tool; tumor

ABSTRACT: Recent data from the National Lung Screening Trial (NLST) suggest that annual low-dose chest CT scans in patients who smoke, leads to early detection of lung cancer and improves survival. CMS/Medicare has consequently approved CT scans for lung cancer screening, and the VA National Center for Health Promotion and Disease Prevention has adopted a similar approach. The Veteran (VA) population is at increased risk of developing lung cancer as compared to the general population because of higher smoking rates and increased likelihood of exposure to other carcinogens during their military service. The VA system cares for some 6.7 million mostly older male veterans each year, many of whom have long smoking histories. In a recent study, investigators from eight VA centers across the U.S. screened more than 2,000 Veterans over two years using criteria from the NLST. Among the 2,106 Veterans screened, a total of 1,257 (59.7%) had nodules, of which 1,184 (56.2%) required tracking. Nearly all of the positive results were negative for cancer, producing a false- positive rate of 97.5% for human-based interpretation. In the general population, many of the lung nodules identified by human readers as “indeterminate” or “suspicious” on chest CT trigger additional surgical interventions (~$5K-$25K/patient) and CT exams, but >30% of these nodules on subsequent biopsies or resection are identified as being benign. The current low false positive rate in diagnosis of nodules on screening CT exams results in patient anxiety, and one of the reasons for poor compliance in lung cancer screening. As a result, there is an urgent need for better image based decision support tools for improving lung cancer screening. PI Anant Madabhushi and his team have developed novel computerized image analysis and pattern recognition tools for improved discrimination of cancerous from non-cancerous nodules on routine screening chest CT scans. A significant breakthrough has been in developing a novel imaging marker called “vessel tortuosity” for quantitatively characterizing the architectural complexity of the vasculature of a lung nodule on chest CT scans; measurements of vessel tortuosity being significantly different between benign and malignant lung nodules. Additionally our group has also identified other highly predictive image features that aim to capture (1) subtle textural patterns of the microarchitecture within and immediately outside the nodule, and (2) subtle 3D shape patterns of the nodule. Each of these imaging markers has been independently shown to have an area under the receiver operating characteristic curve (AUC) ranging from 77-87% in distinguishing malignant from benign nodules in a validation set of N=145 patients. By contrast, on this cohort an expert chest radiologist and pulmonologist had a maximum AUCs of 69-72%. More interestingly, on this cohort combining machine based interpretations with human readers resulted in an improvement of 30% in the AUC value for the human readers. Building on our current impressive results, in this study we propose to continue to optimize our computerized decision support technology (Lung Imaging based Risk Score (LunIRiS)) to assign a risk score of malignancy to a nodule on a chest CT scan. In Aim 1 we will identify the best combination of intra- and peri- nodule texture, 3D shape, margin sharpness and vessel tortuosity measurements for constructing the LunIRiS software program by employing a cohort of over N=300 patients. In Aim 2, LunIRiS will be independently validated on N=300 retrospective cases from the Cleveland VA. We will then deploy the LunIRiS program at the Cleveland VA in Aim 3 to quantitatively evaluate its role as a decision support tool. On an independent cohort of N=250 CT screening exams from Veteran patients, radiologists and pulmonologists at the Cleveland VA will first independently read the scans; following a wash out period they will perform a second interpretation with LunIRiS. Interpretation results with and without LunIRiS will then be compared to evaluate additional benefit of LunIRiS.

摘要：国家肺部筛查试验 (NLST) 的最新数据表明，每年进行低剂量胸部筛查 对吸烟患者进行 CT 扫描可以早期发现肺癌并提高 CMS/医疗保险的生存率。 因此批准 CT 扫描用于肺癌筛查，退伍军人管理局国家健康中心 促进和疾病预防也采取了类似的方法，退伍军人 (VA) 人口也在增加。 与一般人群相比，由于吸烟率较高和罹患肺癌的风险较高 退伍军人管理局系统关心一些人在服兵役期间接触其他致癌物的可能性。 每年有 670 万老年男性退伍军人，其中许多人有长期吸烟史。 来自美国八个退伍军人事务中心的调查人员在两年内对 2,000 多名退伍军人进行了筛查，使用 根据 NLST 的标准，在接受筛查的 2,106 名退伍军人中，共有 1,257 名（59.7%）患有结节，其中 1,184 例 (56.2%) 需要追踪，几乎所有阳性结果均为癌症阴性，从而产生了错误结果。 人性化判读的阳性率为97.5%。在一般人群中，肺部结节很多。 胸部 CT 被人类读者识别为“不确定”或“可疑”，触发额外的手术 干预措施（约 5,000 美元至 25,000 美元/患者）和 CT 检查，但在随后的活检中发现超过 30% 的结节或 目前，结节筛查诊断的假阳性率较低。 CT检查导致患者焦虑，也是肺癌筛查依从性差的原因之一。 因此，迫切需要更好的基于图像的决策支持工具来改善肺癌筛查。 PI Anant Madabhushi 和他的团队开发了新颖的计算机图像分析和模式 用于在常规筛查中改善癌性结节与非癌性结节区分的识别工具 胸部 CT 扫描的一项重大突破是开发一种称为“血管”的新型成像标记。 “迂曲度”用于定量表征肺结节脉管系统的结构复杂性 胸部 CT 扫描；良性和恶性之间血管迂曲度的测量存在显着差异 我们的小组还确定了其他高度预测的图像特征，旨在 捕获 (1) 结节内部和外部微结构的微妙纹理图案，以及 (2) 结节的微妙 3D 形状图案中的每一个都已独立显示。 在区分方面，受试者工作特征曲线 (AUC) 下的面积范围为 77-87% 在 N = 145 名患者的验证集中，从良性结节到恶性结节相比之下，在该队列中，专家胸部。 更有趣的是，在该队列中，放射科医生和肺科医生的最大 AUC 为 69-72%。 基于机器和人类读者的解释使 AUC 值提高了 30% 人类读者。 基于我们目前令人印象深刻的成果，在这项研究中，我们建议继续优化我们的 计算机化决策支持技术（基于肺部成像的风险评分 (LunIRiS)）可分配风险评分 在目标 1 中，我们将确定胸部 CT 扫描中结节的最佳组合。 用于构建 LunIRiS 的结节纹理、3D 形状、边缘清晰度和血管弯曲度测量 在目标 2 中，LunIRiS 将由超过 N=300 名患者组成的队列独立开发。 我们将在克利夫兰 VA 的 N=300 个回顾性案例中进行验证。 克利夫兰弗吉尼亚州在目标 3 中定量评估其作为决策支持工具的作用。 N=250 名来自克利夫兰 VA 的退伍军人患者、放射科医生和肺科医生的 CT 筛查检查将 首先独立读取扫描结果；经过一段时间后，他们将进行第二次解读 然后将对使用和不使用 LunIRiS 的解释结果进行比较，以评估 LunIRiS 的额外优势。 LunIRiS。