4.6 Article

Clinical Analysis of Early-Stage Pancreatic Cancer and Proposal for a New Diagnostic Algorithm: A Multicenter Observational Study

Journal

DIAGNOSTICS
Volume 11, Issue 2, Pages -

Publisher

MDPI
DOI: 10.3390/diagnostics11020287

Keywords

early diagnosis; pancreatic ductal adenocarcinoma; multicenter study; endoscopic retrograde cholangiopancreatography; endoscopic ultrasonography

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Early diagnosis of PDAC is crucial for improving prognosis, with 70% of patients being asymptomatic. EUS is the most sensitive for detecting small PDAC, with MRI showing the highest positivity rate for main pancreatic duct stenosis. ERCP-associated pancreatic juice cytology has a sensitivity of 84% for preoperative pathological diagnosis.
Early diagnosis of pancreatic ductal adenocarcinoma (PDAC) is challenging but essential for improving its poor prognosis. We established a multicenter study to clarify the clinicopathological features, and to propose new algorithm for early diagnosis of PDAC. Ninety-six patients with stage 0 and IA PDAC were enrolled from 13 high-volume centers. Overall, 70% of the patients were asymptomatic. The serum pancreatic enzyme levels were abnormal in half of the patients. The sensitivity of endoscopic ultrasonography (EUS) for detecting small PDAC was superior to computed tomography and magnetic resonance imaging (MRI) (82%, 58%, and 38%, respectively). Indirect imaging findings were useful to detect early-stage PDAC; especially, main pancreatic duct stenosis on MRI had the highest positive rate of 86% in stage 0 patients. For preoperative pathological diagnosis, the sensitivity of endoscopic retrograde cholangiopancreatography (ERCP)-associated pancreatic juice cytology was 84%. Among the stage IA patients, EUS-guided fine-needle aspiration revealed adenocarcinoma in 93% patients. For early diagnosis of PDAC, it is essential to identify asymptomatic patients and ensure close examinations of indirect imaging findings and standardization of preoperative pathological diagnosis. Therefore, a new diagnostic algorithm based on tumor size and imaging findings should be developed.

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