Artificial Intelligence-powered Low-Dose Computed Tomography for Screening of Pancreatic Cancer

Summary

Pancreatic ductal adenocarcinoma (PDAC) has a poor prognosis, with early diagnosis crucial for improving survival. Due to the absence of effective screening methods, most patients are diagnosed at advanced stages. The population undergoing low-dose computed tomography (LDCT) screening significantly overlaps with those at high risk for PDAC; however, traditional imaging methods have limited sensitivity for detecting pancreatic lesions. This study utilizes the Pancreatic Cancer Detection with Artificial Intelligence (PANDA) system to enhance LDCT for pancreatic cancer screening in a prospective, multicenter, observational cohort. PANDA will analyze LDCT images, followed by a multidisciplinary team (MDT) reassessment of abnormal interpretations. Based on MDT evaluation, individuals will be recalled for further examination, placed under a personalized follow-up plan, or monitored for at least one year. The primary outcomes include pancreatic cancer detection rate, positive predictive value, consensus rate, and recall rate, while secondary outcomes focus on early-stage cancers, resectable tumors, and safety indicators such as false positive rates and unnecessary procedures. This study aims to assess the effectiveness and safety of AI-assisted LDCT for PDAC detection, providing a practical solution for improving public health and enhancing early diagnostic capabilities.

Eligibility

Inclusion Criteria:

1. Age 50 years and above.
2. Voluntary signing of informed consent.
3. Completion of LDCT examination.

Exclusion Criteria:

1. Previous history of pancreatic cancer.
2. Abdominal inflammation or diagnosis of acute pancreatitis within 6 months.
3. Poor image quality due to ascites, pancreatic trauma, thoracic/abdominal surgery, radiotherapy or chemotherapy.
4. Research subjects unable to complete follow-up due to physical or other reasons.

Conditions6

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