A multicenter study in bronchoscopy combining Stimulated Raman Histology with Artificial intelligence for rapid lung cancer detection - The ON-SITE study

支气管镜检查结合受激拉曼组织学与人工智能快速检测肺癌的多中心研究 - ON-SITE 研究

• 批准号: 10698382
• 负责人: Allen Cole Burks
• 金额: $ 94.53万
• 依托单位: INVENIO IMAGING INC.
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10698382
• 关键词: Ablation; Address; Algorithms; Area Under Curve; Artificial Intelligence; Biomedical Engineering; Biopsy; Biopsy Specimen; Brain Neoplasms; Bronchoscopy; Cancer Detection; Cancer Patient; Cells; Certification; Cessation of life; Classification; Clinical; DNA Sequence Alteration; Data Set; Detection; Development; Devices; Diagnosis; Diagnostic; Disease; Epidermal Growth Factor Receptor; Evaluation; Forcep; Future; Grant; Hilar; Histology; Image; Image Analysis; Institution; KRAS2 gene; Location; Lung; Lung nodule; Malignant Neoplasms; Malignant neoplasm of lung; Mediastinal; Medical Device; Medicine; Modality; Molecular; Molecular Diagnosis; Molecular Profiling; Multicenter Studies; Mutation; Nature; Needles; Outcome; Pathologic; Pathologist; Pathology; Patients; Performance; Peripheral; Pharmacologic Substance; Procedures; ROC Curve; Research Design; Sampling; Science; Site; Specificity; System; Testing; Time; Tissues; Training; Translating; Validation; aspirate; cost; deep learning; deep learning algorithm; low dose computed tomography; lung imaging; lung lesion; lymph nodes; meetings; microscopic imaging; molecular marker; next generation sequencing; participant enrollment; prognostic; programmed cell death ligand 1; screening program; standard of care; tool; treatment planning

Project Summary & Abstract Lung cancer accounts for about 25% of all cancer deaths in the US, as it is often caught at an advanced stage when treatment options are limited. This has led to the institution of screening programs with low-dose CT, resulting in ~1.6 million pulmonary nodules detected every year, most of them (~80%) in the periphery of the lung. Tissue biopsy is the standard of care for establishing a definitive diagnosis for treatment planning. Rapid on-site evaluation (ROSE) of tissue biopsies in the procedure room by cytopathologists or cytotechnicians can be used to establish that: 1. Diagnostic quality tissue has been procured; identifying malignancy by ROSE has been shown to reduce the number of biopsy tools and repeat procedures 2. An adequate number of cells have been obtained to allow molecular profiling by next- generation sequencing and molecular testing 3. The absence of metastatic disease in the lymph nodes justifies the more-invasive peripheral biopsy. Nevertheless, ROSE is not the standard of care despite its advantages, because its quality is highly variable across sites, it can increase procedure times, and its costs are unlikely to be fully reimbursed. We propose the development of an FDA-cleared medical device that allows microscopic imaging of fresh, unprocessed tissue biopsies in the treatment room and provides accurate, near real-time diagnosis based on deep learning. Specifically, the proposed system uses Stimulated Raman Histology (SRH), which was pioneered by the PI, translated for intraoperative diagnosis in brain tumors, shown to be suitable for automated diagnosis via deep learning with a performance that is non-inferior to pathologists, and resulted in the first CE-certified device for identification of brain tumor margins by the company. Here, we address an urgent clinical need in lung cancer, giving doctors confidence in their intraoperative decisions, and reducing unnecessary biopsies/procedures for patients and payers. Further, we will investigate the potential to provide accurate intraoperative diagnosis of molecular markers that could enable local delivery of pharmaceuticals, ablation, and other therapies, in the biopsy procedure.

项目摘要及摘要