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

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内在联系。成像上的淋巴结的检测/分割是一个繁琐、非常耗时的过程，其固有地受到观察者内/观察者间变化的影响。对淋巴结的分类有助于提高诊断评估和治疗计划。AI软件OncoAI在第一阶段成功开发，可自动检测和分割MRI和CT中的肿大淋巴结，并实现全自动RECIST测量。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，并开发用于利用多模态成像进行恶性肿瘤分类的其他AI模型。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整合。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。拟议的目标将为OncoAI奠定坚实的技术和监管基础，不仅有助于商业成功，还将对癌症护理的临床实践产生更广泛的影响。

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.