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.

摘要 肺癌仍然是美国和世界范围内与癌症相关的死亡的主要原因，尽管 早期发现、治疗和戒烟计划的进展。据《国家肺报》报道， 使用低剂量计算机断层扫描(LDCT)进行筛查的筛查试验(NLST)可能会减少肺部 与胸部X光相比，癌症死亡率降低了20%。这一结论最终导致了对 有吸烟史的无症状成年人中使用LDCT进行肺癌筛查的报销 抽烟。然而，基于LDCT的筛查通常会导致大量不确定的结节，随后 结果证明是非癌症的。随着美国越来越多地实施基于LDCT的肺癌筛查， 在肺癌筛查中，不确定肺结节的检测可能会增加。减少 不必要的诊断程序，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，这是一种可以轻松准确地评估肿瘤的恶性度和侵袭性的工具 不确定的发现将是临床实践中受欢迎的补充。不同级别的入侵通常 提示不同的治疗计划，通常可以预测治疗结果。与调查性的 致力于区分良性和恶性结节的努力，一直集中在非常有限的努力上 评估可疑结核的侵袭性，并探讨与之相关的潜在因素 侵犯性。我们建议开发和验证一种新的计算机工具来非侵入性地评估 用LDCT扫描从大型和多样化的肺癌数据库中评估腺癌的侵袭性 病理结果。我们将调查并确定基于图像的特征是如何导致入侵的。我们的 令人振奋的初步结果证明了开发和实施这样一个工具的可行性和高度的 翻译潜力。我们相信，我们的计算机工具将是临床上非常有用的补充 肺癌诊疗实习。它的可用性将：(1)实现及时和准确的诊断 肺癌，(2)限制进一步的成像、活检和可能的手术的需要，以及(3)促进最佳的 治疗方法的选择(如手术切除或放射治疗)。最终，我们想要改进 肺癌患者的生存和生活质量。