Predicting oesophageal cancer progression using genomic information in pre-malignant oesophageal tissues

Date: 
6 Nov 2017
Source: 
NCRI Cancer Conference
Audience: 
Patients and public
Professionals
Type of resource: 
Research

The model was shown to robustly classify both progressive and non-progressive samples with an AUC of 79% using default cutoffs. At the pathological endpoint (high-grade dysplasia) the model predicted 93% of early cancer samples correctly as progressive, but most important were the predictions for pathologies prior to the endpoint, these predictions included pathological grades prior to current diagnostic guidelines, particularly non-dysplastic BE (see table below). 

 

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