Research of the Application of Pancreatic Cancer Screening Artificial Intelligence Model "PANDAPro"

Summary

The purpose of this study is to build upon the previously developed deep learning-based non-contrast CT pancreatic cancer screening model, PANDA. The model will first undergo training and enhancement, followed by external validation across multiple centers. Subsequently, a large-scale real-world validation will be conducted at Zhejiang University's First Affiliated Hospital , the study will be divided into two rounds. In the first round, the performance of the PANDA model and the upgraded PANDA Pro model will be compared on consecutive retrospective real-world CT scans. In the second round, physicians will record the PANDA Pro results in real time to identify potential pancreatic lesions that may have been clinically missed. By leveraging clinical big data across different scenarios at Zhejiang University's First Affiliated Hospital, the study aims to validate the model's role in prompting and supplementing the diagnosis of PDAC in clinical practice, thereby laying the foundation for large-scale opportunistic screening of PDAC.

Eligibility

Inclusion Criteria:

* Subjects who have undergone chest and/or abdominal 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.

Conditions2

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