Taiwanese team develops AI system that can detect pancreatic cancer
Taipei, Oct. 28 (CNA) A team at National Taiwan University Hospital (NTUH) has developed an artificial intelligence (AI) system that can identify tumors in the pancreas with an accuracy of over 90 percent.
At a press conference on Tuesday where the team introduced the technology, NTUH doctor Liao Wei-chih (廖偉智) said that pancreatic cancer was the seventh deadliest type of cancer in Taiwan in 2019, causing nearly 2,500 deaths that year.
The disease is extremely hard to detect, however, as patients experience no symptoms during the early stages, and studies have found that 40 percent of pancreatic tumors that are smaller than 2 centimeters are missed when doctors use CT scans, Liao said.
This is because these small tumors do not look like lumps, but appear to be a thin layer of gray film, Liao explained, which is a challenge for even the most experienced of experts to identify.
As a result of these difficulties, patients are often only diagnosed when the cancer has spread to other parts of the body, thus complicating treatment, he said.
To see if artificial intelligence could play a role in detecting pancreatic tumors and help radiologists interpret CT scan images, Liao and his team used a system based on convolutional neural networks (CNNs) to analyze images of such tumors.
The deep-learning system developed by Liao and his team was introduced in a study published in The Lancet Digital Health in June 2020.
According to the study, Liao and his team used the CT images of 295 patients with pancreatic cancer and 256 control images of non-cancerous patients to train the AI system to distinguish between the two.
It then tested the accuracy of the system against CT images from 176 patients with pancreatic cancer and compared them to how radiologists judged them.
The study found that "CNN can accurately differentiate pancreatic cancer from non-cancerous pancreas....CNN holds promise for developing computer-aided detection and diagnosis tools for pancreatic cancer to supplement radiologist interpretation."
According to the study, the system's detection of tumors under 2 centimeters was 92 percent (35 of 38 detected) and 100 percent for tumors between 2cm and 4cm (92 of 92) and over 4 cm (46 of 46).
That compared favorably to NTUH radiologist detection rates of 89.5 percent for tumors under 2cm (34 of 38) and 90.8 percent for tumors between 2cm and 4cm (79 of 87).
The study warned against reading too much into the higher sensitivity achieved by the CNN-driven software than the radiologists, saying the CNN system examined whether pancreatic cancer existed in specific areas chosen by radiologists for examination.
"Collectively, our results suggest that the CNN could supplement radiologists to reduce the miss rates rather than outperform or replace radiologists," the study said.
That data above was based on images from NTUH, but tests were also done using images from the United States, where the results were not quite as good.
The system's sensitivity was 63.1 percent for tumors under 2 centimeters (41 of 65), 82.3 percent for tumors between 2cm and 4cm (153 of 186) and 93.3 percent (28 of 30) for tumors bigger than 4 cm.
"The decreased accuracy of our CNN in an external test set suggested that important differences in CT images might exist between different races and ethnicities, and such diversity needs to be included in the training data for the trained CNN to have good generalisability across diverse populations," the study said.
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