Researchers have recently developed an artificial intelligence tool that can spot pancreatic cancer on CT in its earliest stages.
Specifically, the computer-aided detection (CAD) tool is capable of identifying lesions that are less than 2 cm, before the cancer has progressed to the point of diminishing a positive prognosis, experts recently reported in Radiology.
“Because the prognosis worsens precipitously once the tumor grows larger than 2 cm, early detection represents the most effective strategy to improve the dismal prognosis of PC,” co-senior author of the study Weichung Wang, of the Taipei, Taiwan; Institute of Applied Mathematical Sciences, and colleagues explained. “CT is the major imaging modality used to help detect PC, but its sensitivity for small tumors is modest, with approximately 40% of tumors smaller than 2 cm being missed.”
The CAD tool was developed using an internal test set of 546 patients with pancreatic cancer and 733 control patients. On this test set, the tool achieved 90% sensitivity and 96% specificity.
Next, the tool was validated on the internal test set, in addition to 1,473 CT exams from multiple facilities in Taiwan. The tool again performed well in distinguishing pancreatic cancer from controls, at 90% sensitivity and 93% specificity.
In addition to detecting pancreatic cancer on par with radiologists, the tool also achieved 75% sensitivity for identifying lesions less than 2 cm in size—a feat that researchers involved in the study suggested could render the tool a valuable assistive device for radiologists in the future.
“This CAD tool could provide a multitude of information to assist clinicians. Besides determining whether the images showed PC, the tool could indicate the region of suspicion to expedite radiologist interpretation,” the authors wrote.
The experts noted that the tool also appeared to recognize the secondary signs of pancreatic cancer, such as pancreatic duct dilatation, upstream pancreatic parenchymal atrophy and abrupt cutoff of the pancreatic duct. The authors suggested that this finding merits further research.
The study abstract can be viewed here.
In addition to her background in journalism, Hannah also has patient-facing experience in clinical settings, having spent more than 12 years working as a registered rad tech. She joined Innovate Healthcare in 2021 and has since put her unique expertise to use in her editorial role with Health Imaging.