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

6 Nov 2017
NCRI Cancer Conference
Patients and public
Type of resource: 

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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