Research of the Application of Pancreatic Cancer Screening Artificial Intelligence Model "PANDA PLUS"

This study aims to utilize large-scale real-time CT（Computed Tomography） imaging data from multiple centers and scenarios, including the First Affiliated Hospital of Zhejiang University, Lishui Central Hospital, and the Second Affiliated Hospital of Nanchang University, within a prospective real-world cohort. Radiologists will be stratified by years of experience and randomly assigned into two groups. The experimental group will use PANDA（Pancreatic Cancer Detection with Artificial Intelligence） Plus results to assist in generating imaging reports. The study will record the radiologists' interpretations before and after reviewing the PANDA Plus reports and will track patients with imaging reports suggesting suspicion of PDAC（Pancreatic ductal adenocarcinoma） positivity from both groups. Follow-up will be conducted to verify whether patients with negative imaging reports in both groups develop pancreatic cancer. The study will assess the benefit of PANDA Plus in assisting clinical radiologists in diagnosing PDAC under non-contrast CT conditions. It will compare the sensitivity, specificity, and positive predictive value of PDAC detection with and without the assistance of the PANDA Plus model, as well as differences in the initial TNM staging, resectability rates, and long-term survival data.

This study aims to utilize large-scale real-time imaging data from multiple centers, including the First Affiliated Hospital of Zhejiang University, Lishui Central Hospital, and the Second Affiliated Hospital of Nanchang University, within a prospective real-world cohort. Radiologists will be stratified by years of experience and randomly assigned into two groups. One group will use the pancreatic cancer screening model, PANDA PLUS, while the other will use a pseudo-PANDA model to assist in diagnosis during routine clinical practice. Both groups will be tracked for cases with imaging reports suggesting possible PDAC positivity, and follow-up will verify the final outcomes of patients with negative imaging reports. Patients with positive reports will receive regular follow-up every three months to obtain clinical history, pathological gold standards, initial tumor markers, resectability classification, TNM staging, and long-term survival data. The study will evaluate the effectiveness of PANDA PLUS in assisting clinical radiologists in diagnosing PDAC under non-contrast CT conditions and compare the sensitivity, specificity, and positive predictive value of PDAC detection between the pseudo-PANDA and PANDA PLUS models. Additionally, it will compare differences in initial TNM staging, resectability rates, and long-term survival data. The study will also assess the detection efficiency of PANDA PLUS for triple-negative PDAC (negative clinical symptoms, negative tumor markers, and negative non-contrast CT imaging).

Eligibility Criteria:
Inclusion Criteria:

* Subjects who have undergone chest and/or abdominal non-contrast CT scans at outpatient clinics, inpatient departments, or physical examination centers;
* Age at the time of the scan between 18-90 years old, with no restriction on gender;

Exclusion Criteria:

* Chest CT scans that do not cover the pancreas;
* Non-contrast CT scans performed in emergency settings;
* Patients who have undergone thoracic/abdominal surgeries affecting or altering the anatomical display of the pancreas (e.g., post-esophageal, gastric, pancreatic, vascular surgeries, or post-ERCP);
* Non-standard scans (e.g., hands placed on either side of the body or abdomen, severe respiratory motion artifacts, perfusion contamination, etc.);
* CT scans ordered by hepatobiliary and pancreatic surgeons or oncologists;
* Patients referred to a higher-level hospital due to a pancreatic mass found during local hospital examination;
* Patients who, for personal reasons, did not follow up with pancreatic cancer diagnosis or treatment at the hospital, or were lost to follow-up midway;
* Patients with concurrent malignancies in other locations or those undergoing comprehensive cancer treatment for malignant tumors;
* Imaging reports made by radiologists without referring to AI during the image interpretation;
* Patients who underwent enhanced CT, MRI, or PET-CT examinations concurrently.