Predicting Cancer in Pancreatic Cystic Lesions Through Artificial Intelligence
Trial statusNot yet recruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18+
SponsorCentre Hospitalier Universitaire de Nice
About this trial
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.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
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.
Disqualifiers
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.
Trial 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.
Trial design
Design model
Cohort
Time perspective
Retrospective
Treatments tested in this trial
Pancreatic surgery
Procedure/SurgeryPancreatic 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.
Treatment groups
250 Participants
are divided into 1 treatment groupGroup A: PCLs patients1 intervention
Trial outcomes
Primary outcomes
1
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.
Time frame
90 days from patients hospital discharge.Secondary outcomes
1
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).
Time frame
90 days from patients hospital discharge.2
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).
Time frame
90 days from patients hospital discharge.3
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).
Time frame
90 days from patients hospital discharge.4
Balanced accuracy
the aritmethic mean of sensitivity and specificity. It varies between 0 (no correct prediction) to 1 (full correct predictions).
Time frame
90 days from patients hospital discharge.Locations
Sponsors and contacts
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Source ClinicalTrials.gov
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