Macro-vasculature: A Novel Image Biomarker of Lung Cancer

大血管系统：肺癌的新型图像生物标志物

基本信息

批准号：
10292493
负责人：
Jiantao Pu
金额：
$ 23.15万
依托单位：
UNIVERSITY OF PITTSBURGH AT PITTSBURGH
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-01-13 至 2023-01-01
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10292493
关键词：
Achievement Adenocarcinoma Adult Age Appearance Benign Biopsy Blood Vessels Body Composition Cancer Etiology Cancer Model Cancer Patient Cessation of life Characteristics Chest Chinese People Clinical Clinical Management Collaborations Computers Data Set Databases Detection Development Diagnostic Procedure Disease Early Diagnosis Ensure Excision Funding Opportunities Gender Goals Growth Healthcare High Resolution Computed Tomography Image Incidence Institution Intervention Investigation Lead Lobar Lung Lung Adenocarcinoma Lung Neoplasms Lung nodule Machine Learning Malignant - descriptor Malignant Neoplasms Malignant neoplasm of lung Metastatic Neoplasm to Lymph Nodes Metastatic Neoplasm to the Lung Morbidity - disease rate Morphology Nodule Nutrient Operative Surgical Procedures Outcome Oxygen Pathologic Pathology Patients Positron-Emission Tomography Procedures Process Quality of life Radiation exposure Radiation therapy Recording of previous events Reporting Research Risk Scanning Selection for Treatments Smoker Technology Testing Texture Thoracic Radiography Time Tobacco smoking behavior Treatment outcome United States United States National Institutes of Health Visual Withholding Treatment X-Ray Computed Tomography accurate diagnosis algorithm development base cancer diagnosis cancer invasiveness cancer therapy care burden clinical practice clinical translation computerized experience follow-up imaging biomarker improved interest low dose computed tomography lung basal segment lung cancer screening mortality multidisciplinary novel programs racial difference radiomics screening side effect smoking cessation success tool translational clinical trial treatment planning tumor

项目摘要

ABSTRACT Lung cancer remains the leading cause of cancer related deaths in the United States and worldwide despite advances in early detection, treatment, and smoking cessation programs. It was reported by the National Lung Screening Trial (NLST) that screening with low dose computed tomography (LDCT) scans may reduce lung cancer mortality by 20% compared to chest x-ray. This conclusion ultimately led to the approval and reimbursement for lung cancer screening using LDCT among asymptomatic adults with a history of tobacco smoking. However, LDCT-based screening often results in a large number of indeterminate nodules that later turn out to be non-cancerous. With increasing implementation of LDCT-based lung cancer screening in the U.S., the detection of indeterminate lung nodules during lung cancer screening is likely to increase. To reduce unnecessary diagnostic procedures, such as follow-up CT scan, positron emission tomography (PET)/CT exam, and invasive biopsies, a tool that can easily and accurately assess the malignancy and invasiveness of the indeterminate findings will be a welcomed addition to clinical practice. Different levels of invasiveness typically indicate different treatment plans and can often predict the treatment outcome. Compared to the investigative efforts dedicated to discriminating benign from malignant nodules, very limited effort has been focused on assessing the invasiveness of the suspicious nodules and explore the underlying factors associated with invasiveness. We proposed to develop and validate a novel computer tool to non-invasively assess the invasiveness of adenocarcinomas using LDCT scans from a large and diverse lung cancer database with pathology outcome. We will investigate and identify how image-based features contribute to invasiveness. Our exciting preliminary results demonstrate the feasibility of developing and implementing such a tool and its highly translational potential. We believe that our computer tool will be a tremendously useful addition to the clinical practice of lung cancer diagnosis and treatment. Its availability will: (1) enable a timely and accurate diagnosis of lung cancer, (2) limit the need for further imaging, biopsies, and possible surgery, and (3) facilitate an optimal selection of the treatment approach (e.g., surgical resection or radiotherapy). Ultimately, we want to improve survival and the quality of life of lung cancer patients.

抽象的尽管肺癌仍然是美国和全世界癌症相关死亡的主要原因早期检测、治疗和戒烟计划方面取得进展。据国家肺科报道筛查试验 (NLST) 表明，低剂量计算机断层扫描 (LDCT) 扫描可能会减少肺与胸部 X 光检查相比，癌症死亡率降低 20%。这一结论最终获得了批准并对有吸烟史的无症状成年人使用 LDCT 进行肺癌筛查的报销吸烟。然而，基于 LDCT 的筛查常常会导致大量不确定的结节，随后会出现这些结节。结果证明是非癌性的。随着美国越来越多地实施基于 LDCT 的肺癌筛查，肺癌筛查期间不确定性肺结节的检出率可能会增加。减少不必要的诊断程序，例如后续 CT 扫描、正电子发射断层扫描 (PET)/CT 检查、和侵入性活检，一种可以轻松准确地评估肿瘤恶性程度和侵袭性的工具不确定的发现将是临床实践中受欢迎的补充。通常具有不同程度的侵入性指示不同的治疗计划，并且通常可以预测治疗结果。与调查相比致力于区分良性结节和恶性结节的努力，集中于非常有限的努力评估可疑结节的侵袭性并探索与可疑结节相关的潜在因素侵袭性。我们建议开发并验证一种新颖的计算机工具来非侵入性地评估使用来自大型且多样化的肺癌数据库的 LDCT 扫描来评估腺癌的侵袭性病理结果。我们将调查并确定基于图像的特征如何导致侵入性。我们的令人兴奋的初步结果证明了开发和实施此类工具的可行性及其高度翻译潜力。我们相信我们的计算机工具将为临床提供非常有用的补充肺癌诊断和治疗实践。其可用性将：（1）实现及时、准确的诊断肺癌，(2) 限制进一步成像、活检和可能的手术的需要，(3) 促进最佳治疗选择治疗方法（例如手术切除或放射治疗）。最终，我们想要改进肺癌患者的生存率和生活质量。