Macro-vasculature: A Novel Image Biomarker of Lung Cance

摘要 肺癌仍然是美国和全世界癌症相关死亡的主要原因。的 与肺癌相关的高死亡率部分是由于对肺癌的利用不足和有限的获取 筛查阻碍了早期诊断。一些临床医生和政策制定者对肺的担忧 采用低剂量计算机断层扫描（LDCT）检查进行癌症筛查是基于对提前期偏倚的担忧 假阳性率高。开发一种可靠的肺癌生物标志物， 假阳性和改进不确定结节的分类将减轻一些相关的担忧， 到肺癌筛查。虽然研究表明，用LDCT扫描筛查可能会减少肺癌 死亡率降低20%，据报道，约96%的可疑发现（大多数不确定） 结节）变成非癌性的（假阳性）。筛查不确定性的临床管理 结节通常导致不必要的、昂贵的和潜在有害的后续程序（例如，随访CT 扫描、正电子发射断层扫描（PET）/CT检查、侵入性活组织检查）。我们开发了一个令人兴奋的， 新的基于图像的宏观血管特征，以区分良性和恶性结节。我们建议 进一步开发该功能，并在一系列CT协议和其他机构的扫描中对其进行验证。我们 还将大脉管系统特征与临床信息（例如，年龄，性别，吸烟史， 肺功能），并评估模型区分良性与恶性筛查检测的能力。 不确定的结节我们将调查放射科医生对不确定结节的分类是否随着 与没有集成模型的分类相比，集成模型的输出。的成功 该项目可能导致一种新的和强大的肺癌生物标志物，以准确评估筛查检测到的肺癌。 不确定的结节，可以显着减少肺移植期间不必要的后续程序的数量 癌症筛查

ABSTRACT Lung cancer remains the leading cause of cancer related deaths in the United States and worldwide. The high mortality associated with lung cancer is in part due to underutilization of and limited access to lung cancer screening that impedes early diagnosis. The apprehension of some clinicians and policymakers towards lung cancer screening with low-dose computed tomography (LDCT) exams is based on concerns of lead-time bias and high false-positive rate. The development of a robust lung cancer biomarker that reduces screen-detected false positives and improves classification of indeterminate nodules would relieve some of the concerns related to lung cancer screening. Although investigations show that screening with LDCT scans may reduce lung cancer mortality by 20% compared to chest x-ray, it is reported that ~96% of suspicious findings (mostly indeterminate nodules) turn out to be non-cancerous (false positives). Clinical management of screen-detected indeterminate nodules often leads to unnecessary, costly, and potentially harmful follow-up procedures (e.g., follow-up CT scan, positron emission tomography (PET)/CT exam, invasive biopsies). We have developed an exciting and novel image-based macro-vasculature feature to discriminate benign from malignant nodules. We propose to further develop the feature and validate it across a range of CT protocols and scans from other institutions. We will also integrate the macro-vasculature features with clinical information (e.g., age, gender, smoking history, lung function) and evaluate the model's ability to discriminate benign from malignant screen-detected indeterminate nodules. We will investigate if a radiologist's classification of indeterminate nodules improves with the output of the integrative model compared to classification without the integrative model. The success of this project may lead to a novel and robust lung cancer biomarker to accurately assess screen-detected indeterminate nodules that can significantly reduce the number of unnecessary follow-up procedures during lung cancer screening.