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Machine Learning Analysis for Lung Cancer Progression

Principal Investigator
Name
Anderson Nnewihe
Degrees
Ph.D.
Institution
Johnson & Johnson Enterprise Innovation, Inc.
Position Title
Director, Early Detection and Diagnostic Solutions
Email
About this CDAS Project
Study
NLST (Learn more about this study)
Project ID
NLST-973
Initial CDAS Request Approval
Oct 18, 2022
Title
Machine Learning Analysis for Lung Cancer Progression
Summary
A deeper understanding of the progression of lung cancer can help to guide clinical management towards precision medicine. This will require large, rich, longitudinal datasets spanning multiple modalities to capture and incorporate features relevant to disease progression into computational models. This study will combine pathology and radiology images with longitudinal clinical outcomes from the National Lung Screening Trial (NLST) to explore models for lung cancer progression and benchmark them against common clinical classifications.
Aims

- Characterize population subgroups by clinical features and patient trajectories.
- Build models for disease progression using imaging and clinical data
- Assess the clinical utility of the models

Collaborators

Trishan Arul: Amrit.ai Inc., DBA Picture Health