Predicting Cancer in Pancreatic Cystic Lesions Through Artificial Intelligence

Deep Learning for Malignant Degeneration Prediction of Pancreatic Cystic Lesions - Beyond High-risk Stigmata

This international, multicenter retrospective study aims to develop a deep learning (DL)-based predictive model to identify malignant transformation in pancreatic cystic lesions, improving upon current clinical guidelines. The model will integrate clinical, biochemical, and multimodal imaging data. Several 3D convolutional neural networks will be trained using advanced preprocessing, data augmentation, and hybrid fusion techniques. Model performance will be compared to that of existing international guidelines. The study involves no additional procedures for patients and adheres to strict data anonymization and privacy regulations.

Study Overview

• Status: Not yet recruiting
• Conditions: Pancreatic Cancer; Pancreatic Cystic Lesions
• Intervention / Treatment: Procedure: Pancreatic surgery

• Study Type: Observational
• Enrollment (Estimated): 250

Contacts and Locations

• Study Contact: Name: Andrea CHIERICI; Phone Number: +33 0634799833; Email: chierici.ap@chu-nice.fr

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Probability Sample

Study Population

Patients who underwent pancreatic surgery for pancreatic cystic lesions, in the absence of preoperative evidence of cancer. Indication of surgery must be based on one or more current guidelines concerning pancreatic cystic lesions management.

Description

Inclusion Criteria:

• Patients diagnosed with PCL(s ) who underwent pancreatic surgery in one of the participant centers. Surgical indication must adhere to at least one of current guidelines on PCLs management (6), based on clinical, biochemical, and radiological (MR and/or EUS) features.
• Pancreatic surgery B83performed for supposed increased risk of cyst(s) malignant degeneration following current guidelines on PCLs management (6).
• Absence of clinical, biochemical, radiological, and anatomopathological evidence of pancreatic cancer at pancreatic surgery.
• Non-opposition to the anonymous data processing by the included patients.

Exclusion Criteria:

• Patients presenting with evidence of pancreatic cancer at surgery.
• PCL(s) diagnosis and treatment performed without one between EUS and pancreatic MR. surgery performed in the absence of the criteria proposed by current guidelines.
• Unavailability of both preoperative EUS and pancreatic MR data.
• Unavailability of postoperative PCL(s) anatomopathological analysis results.
• SBO diagnosis performed without CT-scan.

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
PCLs patients; Pancreatic resective surgery performed for pancreatic cystic lesions with high risk of malignant degeneration based on clinical, biochemical, and/or radiological features following current guidelines on pancreatic cystic lesions management.	Procedure: Pancreatic surgery; Pancreatic resective surgery performed for pancreatic cystic lesions with high risk of malignant degeneration based on clinical, biochemical, and/or radiological features following current guidelines on pancreatic cystic lesions management.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Prediction of malignant degeneration of pancreatic cystics lesions	Predict the presence of malignant degeneration (defined as: high grade dysplasia, in situ PADC, or T1 PADC) in pancreatic cystic lesion(s) using artificial intelligence model based on clinical, biochemical, and radiological features. This will be measured through Area Under the Receiver Operator Characteristic curve (AUROC) assesment. AUROC varies between 0.5 and 1, corresponding to no class separation capacity and full class separation capacity, respectively.	90 days from patients hospital discharge.

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Accuracy of performance evaluation	the number of true positives and true negatives among all predictions. It varies between 0 (no correct prediction) to 1 (full correct predictions).	90 days from patients hospital discharge.
Precision of performance evaluation	The number of true positives divided by all the positive predictions (true positives and false positives). It varies between 0 (no correct prediction) to 1 (full correct predictions).	90 days from patients hospital discharge.
Recall of performance evaluation	The number of true positives divided by the actual positive instances in the dataset (true positives and false negatives). It varies between 0 (no correct prediction) to 1 (full correct predictions).	90 days from patients hospital discharge.
Balanced accuracy	the aritmethic mean of sensitivity and specificity. It varies between 0 (no correct prediction) to 1 (full correct predictions).	90 days from patients hospital discharge.
F1-score	It combines precision and recall. It ranges from 0-100%, and a higher F1 score denotes a better quality classifier.	90 days from patients hospital discharge.
Confusion matrix	A visual representation of true positives, false positives, true negatives, and false negatives. It is depicted through a table.	90 days from patients hospital discharge.
Log-loss	It indicates how close the prediction probability is to the corresponding actual/true value (0 or 1 in case of binary classification). The more the predicted probability diverges from the actual value, the higher is the log-loss value.	90 days from patients hospital discharge.
Cohen's Kappa	A metric used to measure the level of agreement between two raters which can be a useful tool to gauge the performance of a classification model. It accounts for the fact that the raters may happen to agree on some items purely by chance. It varies between 0 (no correct prediction) to 1 (full correct predictions).	90 days from patients hospital discharge.

Collaborators and Investigators

• Sponsor: Centre Hospitalier Universitaire de Nice

Study record dates

Study Major Dates

• Study Start (Estimated): June 1, 2025
• Primary Completion (Estimated): June 1, 2026
• Study Completion (Estimated): December 31, 2026

Study Registration Dates

• First Submitted: April 24, 2025
• First Submitted That Met QC Criteria: April 30, 2025
• First Posted (Actual): May 2, 2025

Study Record Updates

• Last Update Posted (Actual): May 2, 2025
• Last Update Submitted That Met QC Criteria: April 30, 2025
• Last Verified: April 1, 2025

More Information

Terms related to this study

• Other Study ID Numbers: 25Chirdig01

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No