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

• 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
• 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

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 早期检测，治疗和戒烟计划的进步。国家肺报道 筛查试验（NLST），低剂量计算机断层扫描（LDCT）扫描可能会减少肺 与胸部X射线相比，癌症的死亡率增加了20％。这个结论最终导致了批准， 在有烟草病史的无症状成年人中使用LDCT进行肺癌筛查的报销 吸烟。但是，基于LDCT的筛选通常会导致大量不确定的结节 原来是非癌性的。随着美国基于LDCT的肺癌筛查的实施， 在肺癌筛查期间检测不确定的肺结节可能会增加。减少 不必要的诊断程序，例如随访CT扫描，正电子发射断层扫描（PET）/CT考试， 和侵入性活检，该工具可以轻松，准确地评估其恶性和侵入性 不确定的发现将是临床实践的热门补充。不同水平的侵入性通常 指出不同的治疗计划，通常可以预测治疗结果。与调查相比 致力于区分良性与恶性结节的努力，非常有限的努力集中在 评估可疑结节的侵入性，并探索与 侵入性。我们建议开发和验证一种新型的计算机工具，以非侵入性评估 使用大型肺癌数据库中的LDCT扫描对腺癌的侵入性 病理结果。我们将研究并确定基于图像的特征如何促进侵入性。我们的 令人兴奋的初步结果证明了开发和实施这种工具及其高度的可行性 翻译潜力。我们认为，我们的计算机工具将是临床上非常有用的补充 肺癌诊断和治疗的实践。它的可用性将：（1）启用及时准确的诊断 （2）限制对进一步成像，活检和可能的手术的需求，（3）促进最佳 选择治疗方法（例如，手术切除或放射疗法）。最终，我们想改进 肺癌患者的生存和生活质量。