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Predicting cancer-specific mortality using histopathology slides

Principal Investigator
Name
Shu Jiang
Degrees
PhD
Institution
Washington University in St. Louis
Position Title
Associate professor
Email
About this CDAS Project
Study
PLCO (Learn more about this study)
Project ID
PLCOI-1532
Initial CDAS Request Approval
Apr 18, 2024
Title
Predicting cancer-specific mortality using histopathology slides
Summary
We have developed an AI model trained using the TCGA pan-cancer dataset to predict cancer-specific mortality. The AI algorithm intakes the digital histopathology slides under 20x or 40x magnification, and outputs a personal-level survival estimate for the next 5 or 10 years. Evaluation of the accuracy for prediction is assessed using AUC measure. We aim to utilize the PLCO pathology images for multiple cancer sites as an external validation to our pre-trained model. This will generate insights on risk stratification for patients whereby a personalized treatment and followup strategy can be generated.
Aims

1. Externally validate cancer-specific mortality using the PLCO histopathology images, report AUC measure for 5- and 10-year survival.
2. Subgroup analysis to identify cluster of patients who are at very high and very low risk of mortality

Collaborators

Dr. Graham A. Colditz (Washington University school of medicine)
Dr. Henry Zhou (Harvard TH Chan School of Public Health)