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

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.