Multi-Modality Imaging-Based Quantitative Pre/During/Post-Treatment Lymph Node Monitoring in Cancers

在肿瘤学中，正确确定淋巴结转移疾病对于患者的治疗是至关重要的，因为患者的预后和随后的治疗与疾病的分期有着内在的联系。在成像上检测/分割淋巴结是一个繁琐、非常耗时的过程，而且固有地受到观察者内/观察者间变异性的影响。对淋巴结进行恶性肿瘤分类，可提高诊断评估和治疗计划。在第一阶段成功开发了一款人工智能软件OncoAI，该软件可以自动检测和分割来自MRI和CT的增大的淋巴结，并实现全自动RECIST测量。这项第二阶段提案的总体目标是进一步提高用于淋巴结检测、分割和测量的人工智能模型的性能，并利用多模式成像开发更多用于恶性肿瘤分类的人工智能模型。软件功能和可用性将得到进一步改进，以实现临床工作流程中的无缝结合。最后，将进行一项多机构验证研究，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，不仅有助于商业上的成功，而且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.