PanCanAID – Pancreas Cancer Artificial Intelligence Driven Diagnosis in CT Scan Imaging: A Protocol for a Multicentric Ambispective Diagnostic Study

Introduction Pancreatic cancer is thought to have an extremely dismal prognosis. Most cancer-related deaths occur from metastasis rather than the primary tumor, although individuals with tumors smaller than 1 cm in diameter have more than 80% 5-year survival. Thus, the current protocol introduces PanCanAID project which intends to develop several computer-aided-diagnosis (CAD) systems to enhance pancreatic cancer diagnosis and management using CT scan imaging. Methods and analysis Patients with pathologically confirmed pancreatic ductal adenocarcinoma (PDAC) or pancreatic neuroendocrine tumor (PNET) will be included as pancreatic cancer cases. The controls will be patients without CT evidence of abdominal malignancy. A data bank of contrast-enhanced abdominopelvic CT scans, survival data, and demographics will be collected from ten medical centers in four provinces. Endosonography images and clinical data, if available, will be added to the data bank. Annotation and manual segmentation will be handled by radiologists and confirmed by a second expert radiologist in abdominal imaging. PanCanAID intelligent system is designed to (1) detect abdominopelvic CT scan phase, (2) segment pancreas organ, (3) diagnose pancreatic cancer and its subtype in arterial phase CT scan, (4) diagnose pancreatic cancer and its subtype in non-contrast CT scan, (5) carry out prognosis (TNM stage and survival) based on arterial phase CT scan, (6) and estimate tumor resectability. A domain adaptation step will be handled to use online data and provide pancreas organ segmentation to reduce the segmentation time. After data collection, a state-of-the-art deep learning algorithm will be developed for each task and benchmarked against rival models. Conclusion PanCanAID is a large-scale, multidisciplinary AI project to assist clinicians in diagnosing and managing pancreas cancer. Here, we present the PanCanAID protocol to assure the quality and replicability of our models. In our experience, the effort to prepare a detailed protocol facilitates a positive interdisciplinary culture and the preemptive identification of errors before they occur. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work is supported by the Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences (Grant No: 0480/650 Research No: 1224 granted to HAA and SAASN). HRR was partially supported by IR National Science Foundation (INSF), Grant No. 96006077. This research was partially funded by individual donations made in memory of Dr. Amanolah Safavi Naini. The funders did not and will not have a role in study design, data collection and analysis, neither in decision to publish or preparation of the manuscript. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The Institutional Review Board of Research Institute for Gastroenterology and Liver Diseases (RIGLD), Shahid Beheshti University of Medical Sciences review board approved this ambispective study after consideration of data anonymization and security (code: IR.SBMU.RIGLD.REC.1401.043 link: https://ethics.research.ac.ir/EthicsProposalViewEn.php?id=323598). This protocol and future studies adhere to Helsinki Declaration of 1975, as revised in 2008, which provides ethical guidelines for medical research involving human subjects. We have taken necessary measures to protect the privacy and confidentiality of all participants and their personal data. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The model will be published for non-commercial future use of researchers under certain license. The data will be published and stored in a renowned repository for future non-commercial use of researcher.