In a recent study published in the journal Nature Medicine, a large team of researchers from China, the US, and the Czech Republic developed a deep learning-based approach to use non-contrast computed tomography (CT) scans for high-accuracy detection and classification of pancreatic lesions for the early detection and treatment of pancreatic ductal adenocarcinoma (PDAC), Report informs referring to the News Medical.
Pancreatic ductal adenocarcinoma is the most malignant form of solid carcinoma, with a mortality rate of over 450,000 each year. The high mortality rate, however, is largely because PDAC is often detected in the late stages when it is inoperable.
Cases where PDAC is detected incidentally or early have a better prognosis and early treatment often results in substantial improvements in the survival rates of patients.
In the present study, the team of scientists described an AI-based approach called pancreatic cancer detection with artificial intelligence (PANDA) that can be used to detect and diagnose non-PDAC and PDAC pancreatic lesions accurately using non-contrast CT scans.
This method was developed to use non-contrast CT scans of the chest and abdomen for the detection and diagnosis of PDAC and seven non-PDAC subtypes of lesions, namely, solid pseudopapillary tumor, pancreatic neuroendocrine tumor, mucinous cystic neoplasm, intraductal papillary mucinous neoplasm, chronic pancreatitis, serous cystic neoplasm, and a long list of other non-PDAC pancreatic lesions.
The researchers first internally evaluated the efficiency of PANDA in detecting and diagnosing pancreatic lesions using a set of non-contrast CT scans of the abdomen. PANDA’s performance was compared against that of two reader studies that used non-contrast and contrast CT scans.
In the first study, non-contrast CT pancreatic scans were read by radiology residents, general radiologists, and specialists in pancreatic imaging.
In the second reader study, the performance of PANDA in detecting pancreatic lesions was compared to the performances of specialists in pancreatic imaging who used contrast-enhanced CT scans.
Subsequently, the generalizability of PANDA for various settings was validated using a large multicenter test cohort. Furthermore, chest CT scans were used to test whether PANDA could be used on various patient populations.
The results showed that PANDA efficiently detected lesions in the multi-center large-scale validation cohort. Additionally, in specificity and sensitivity, the performance of PANDA was 6.3% and 34.1% greater, respectively, than the average performance of a radiologist in detecting and diagnosing pancreatic lesions.
Furthermore, in the large-scale validation using real-world scenarios for four settings, PANDA achieved 92.9% and 99.9% sensitivity and specificity, respectively.