An AI model (REDMOD) can pick up the very early subtle tissue changes of pancreatic ductal adenocarcinoma, the most common form of pancreatic cancer, which conventional imaging and the human eye find difficult to detect, finds research published online in the journal Gut.
The framework includes automated pancreatic segmentation—clear delineation of the borders of the pancreas from surrounding tissue/organs, obviating the need for this to be done manually with the attendant risk of variable accuracy.
REDMOD detected the "invisible" signature of pre-clinical pancreatic ductal adenocarcinoma an average of 475 days before clinical diagnosis.
REDMOD performed better than radiologists: it was nearly twice as sensitive—the ability to pick up true, rather than false, positive results—at accurately picking up "invisible" early malignant cellular changes: 73% compared with 39%.
It also correctly identified just over 81% of scans in an independent group (539 patients) drawn from several hospitals and 87.5% in the public US National Institutes of Health NIH-PCT dataset (80 patients) as free of pancreatic cancer.
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