TOPLINE:
An artificial intelligence (AI) model shows potential for detecting early-stage, "hidden" pancreatic cancer on scans of asymptomatic individuals, paving the way for surgical intervention and cure, new research suggests.
METHODOLOGY:
The researchers utilized a diverse dataset of 3014 CT scans: 1105 diagnostic CT scans with pancreatic ductal adenocarcinoma (PDA) and 1909 control CT scans.
Of the total, 696 diagnostic CT scans with PDA and 1080 control CT scans were used as an AI model training subset, and 409 CT scans with PDA and 829 control CT scans were used as an intramural hold-out test subset.
The model was also tested on a simulated cohort that evaluated the risk for PDA in new-onset diabetes; multicenter public datasets (194 CT scans with PDA and 80 controls); and a cohort of 100 prediagnostic CT scans, incidentally acquired 3-36 months prior to PDA being diagnosed, and 134 controls.
TAKEAWAY:
The model correctly classified 360 CT scans with PDA (88%) and 783 control CT scans (94%) in the intramural test subset. The mean accuracy was 0.92, the area under the receiver operating characteristic (AUROC) curve was 0.97, sensitivity was 0.88, and specificity was 0.95.
On heat maps, activation areas overlapped with the tumor in 350 of 360 CT scans (97%).
