Early Detection Methods for Pancreatic Ductal Adenocarcinoma and What Lies Ahead

Human pancreas, abstract illustration.
Currently available early detection methods for PDAC vary in diagnostic accuracy, which has researchers exploring alternatives for helping patients at high risk.

With exceedingly high mortality rates and a continuing increase in cases expected, there is a critical need for strategies to improve the prognosis for patients with pancreatic ductal adenocarcinoma (PDAC). Diagnosis is often delayed in this patient population due to the late appearance of cancer-specific symptoms, such as abdominal pain, back pain, and jaundice, according to Suresh T. Chari, MD, professor in the department of gastroenterology, hepatology, and nutrition at The University of Texas MD Anderson Cancer Center in Houston, and co-principal investigator of the Diabetes-Pancreatic Cancer Working Group. “Once these symptoms appear, diagnosis is usually made within a few weeks,” he told Gastroenterology Advisor.

Numerous recent studies and reviews have examined available and emerging methods to detect PDAC in the early stages of the disease.1,2

Current Detection Methods for PDAC

Imaging modalities, including abdominal ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), and endoscopic ultrasound (EUS) comprise the main methods currently used in the detection of early-stage PDAC.2 However, there is substantial variability in the diagnostic accuracy of each method, based on factors such as image quality and tumor size.

For instance, the role of conventional abdominal ultrasound, which has a real-world accuracy of 50% to 70% in pancreatic tumor diagnosis, may be limited in detecting small lesions due to interference of bowel gas or adipose tissue.3

We need to Define novel high-risk groups, Enrich them further using biomarkers or clinical risk prediction models … and Find the high-risk lesion using novel approaches to finding small PDAC.

While contrast-enhanced CT has an overall sensitivity of 76% to 92% for detecting early-stage pancreatic cancer, the sensitivity has been shown to decline significantly (63%-77%) in analyses that include tumors less than 2 cm in diameter.2

Conversely, MRI combined with CT has both a sensitivity and specificity of 89% in pancreatic cancer diagnosis.2

Findings have demonstrated a higher sensitivity for pancreatic cancer diagnosis with EUS compared to CT, with a 2019 review reporting a sensitivity of 98% with EUS vs 74% with CT.4 Other results have shown detection rates of 45.5% and 81.8% with EUS in stage 0 and stage 1 pancreatic cancer, respectively, compared to rates of 9.7% and 63% with CT and 9.7% and 39.1% with MRI.2

Although EUS is considered the most sensitive test for early detection of PDAC, this method “is invasive, so it is done in a small subset of patients who are identified to be at high risk, including those with familial pancreatic cancer or cystic lesions,” Dr Chari noted. “But for sporadic pancreatic cancer, which represents 90% of PDAC, there is no established high-risk group.”5

New-Onset Diabetes and PDAC Risk

As widespread PDAC screening for the general population may be infeasible, Dr Chari and colleagues have proposed a D-E-F (define, enrich, find) paradigm that calls for PDAC surveillance in certain groups of high-risk, asymptomatic patients.6 “We need to Define novel high-risk groups, Enrich them further using biomarkers or clinical risk prediction models, such as the DNA-based tests or the Enriching New-Onset Diabetes for Pancreatic Cancer (ENDPAC) score now being tested, and Find the high-risk lesion using novel approaches to finding small PDAC,” he explained.

Researchers are currently investigating patients with new-onset diabetes after age 50 as a high-risk group for PDAC. In contrast to many common types of cancer, including lung, breast, colorectal, and prostate cancers, patients with PDAC commonly demonstrate glucose abnormalities.7 Approximately 80% of these individuals have shown glucose intolerance or impaired fasting glucose at the time of diagnosis, and studies have shown that nearly one-half of PDAC patients had diabetes at the time of diagnosis.8,6

One study found similar fasting blood glucose levels between PDAC patients and matched control participants until 30 to 36 months before diagnosis, when levels began to increase in PDAC patients and continued to increase until the time of diagnosis.9 Additionally, diabetes in PDAC patients is typically diagnosed less than 3 years before the PDAC and improves after surgical resection. In an earlier study, diabetes resolved after pancreaticoduodenectomy in PDAC patients with new-onset diabetes but not in those with long-standing diabetes.10

Taken together, such findings point to new-onset diabetes as an early warning sign of pancreatic ductal adenocarcinoma.8

Future Directions

Researchers have also identified multiple other subgroups that may be considered to have PDAC risk, including patients with mucinous pancreatic cysts, those with familial risk due to germline mutations, and patients with a history of pancreatitis.6 Research in these groups is ongoing, along with studies exploring a range of potential diagnostic methods in detecting early-stage PDAC.

Scientists have reported promising results in the use of artificial intelligence (AI) to improve early detection of PDAC. For example, Liu et al demonstrated an area under the curve of 0.963 for PDAC diagnosis using an AI platform to read CT images, and they subsequently observed higher sensitivity in detecting PDAC with AI (0.983) compared to radiologists (0.929).11,12 In preliminary results of a recent study, researchers at Johns Hopkins University found a sensitivity of 94% and a specificity of 99% using deep learning methods to distinguish PDAC cases from healthy control individuals.1

Various serum biomarkers, including microRNAs and metabolites, as well as biomarkers derived from pancreatic juice and pancreatic cyst fluid, saliva, and urine, represent additional areas of inquiry in the push to improve early PDAC detection and prognosis for this vulnerable patient population.1


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  12. Liu KL, Wu T, Chen PT, et al. Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation. Lancet Digit Health. 2020;2(6):e303-e313. doi:10.1016/S2589-7500(20)30078-9