Researchers have successfully developed a deep learning model that classifies pancreatic ductal adenocarcinoma (PDAC), the most common form of pancreatic cancer, into molecular subtypes using histopathology images.
Researchers have successfully developed a deep learning model that classifies pancreatic ductal adenocarcinoma , the most common form of pancreatic cancer , into molecular subtypes using histopathology images. This approach achieves high accuracy and offers a rapid, cost-effective alternative to current methods that rely on expensive molecular assays. The new study holds promise to advance personalized treatment strategies and improve patient outcomes.
The aggressiveness of PDAC poses a formidable challenge when using sequencing technologies to determine a patient care plan. The disease's rapid clinical deterioration demands swift action to identify eligible individuals for targeted therapies and inclusion in clinical trials. However, current turnaround times for molecular profiling, which range from 19 to 52 days from the time of biopsy, fall short of meeting these time-sensitive demands.
Co-lead investigator Ali Bashashati, PhD, School of Biomedical Engineering, and Department of Pathology and Laboratory Medicine, University of British Columbia, notes,"The sensitivity and specificity of the model was 85% and 100%, respectively, making this AI tool a highly applicable tool for triaging patients for molecular testing.
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