FY 2023 SBIR TOPIC 402 PHASE II. ENHANCE THE PERFORMANCE OF THE AI FOR LYMPH NODE DETECTION, SEGMENTATION AND MEASUREMENTS AND DEVELOP ADDITIONAL AI MODELS FOR MALIGNANCY CLASSIFICATION LEVERAGING MU

2023 财年 SBIR 主题 402 第二阶段。

• 批准号: 10928777
• 负责人: XUE FENG, PH.D.
• 金额: $ 200万
• 依托单位: CARINA MEDICAL, LLC
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-18 至 2025-09-17
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10928777
• 关键词: Body Regions; Classification; Clinical; Computer software; Consumption; Detection; Diagnostic; Disease; Effectiveness; Enlargement of lymph nodes; Evaluation; Foundations; Goals; Head and Neck Cancer; Image; Institution; Interobserver Variability; Link; Magnetic Resonance Imaging; Malignant Neoplasms; Measurement; Modeling; Multimodal Imaging; Nodal; Oncology; Patients; Performance; Phase; Process; Safety; Small Business Innovation Research Grant; Time; cancer care; clinical practice; improved; lymph nodes; patient prognosis; success; treatment planning; usability; validation studies

The correct determination of nodal metastatic disease is imperative for patient management in oncology, since the patient’s prognosis and subsequent treatment are inherently linked to the stage of disease. Detection/segmentation of lymph node on imaging is a tedious, highly time-consuming process that is inherently subject to intra-/inter-observer variability. Malignancy classification of the lymph node improves both the diagnostic evaluation and treatment planning. An AI software, OncoAI, was successfully developed in Phase I that automatically detects and segments enlarged lymph nodes from MRI and CT and enables fully automated RECIST measurements. The overall goal of this Phase II proposal is to further enhance the performance of the AI models for lymph node detection, segmentation, and measurements and develop additional AI models for malignancy classification leveraging multi-modality imaging. Software functionality and usability will be further improved towards seamless incorporation within the clinical workflow. Finally, a multi-institutional validation study will be conducted to demonstrate the safety and effectiveness of OncoAI in clinical practice and obtain regulatory approval. The proposed aims will set a strong technical and regulatory foundation for OncoAI and contribute to not only commercial success, but also broader impact to the clinical practice of cancer care.

正确确定淋巴结转移性疾病对于肿瘤学中的患者管理至关重要，因为患者的预后和后续治疗与疾病的阶段有着内在的联系，影像学上淋巴结的检测/分割是一个繁琐且非常耗时的过程。本质上受观察者内/观察者间变异的影响，淋巴结的恶性分类改善了诊断评估和治疗计划，该软件已在第一阶段成功开发，可自动检测和分割。该 II 期提案的总体目标是进一步增强用于淋巴结检测、分割和测量的 AI 模型的性能，并开发用于恶性肿瘤的其他 AI 模型。利用多模态成像进行分类。软件功能和可用性将进一步改进，以实现与临床工作流程的无缝结合。最后，将进行多机构验证研究，以证明 OncoAI 在临床实践中的安全性和有效性，并获得监管部门的批准。目标将设定为 OncoAI 奠定了强大的技术和监管基础，不仅有助于商业成功，还对癌症治疗的临床实践产生更广泛的影响。