Multimodal Deep Learning Model Predicts Pancreatic Cancer Prognosis

Active, not recruiting

• Conditions: Pancreatic Adenocarcinoma

• Registration Number: NCT06760234
• Lead Sponsor: Second Affiliated Hospital, School of Medicine, Zhejiang University

Brief Summary

This study describes the development and validation of a deep learning prediction model, which extracts deep learning features from preoperative enhanced CT scans and analyzes postoperative pathological specimens of pancreatic cancer patients. The aim is to predict patient prognosis and response to chemotherapy treatment.

Detailed Description

This study retrospectively collected enhanced CT scan data, pathological paraffin blocks, and clinical data from pancreatic cancer patients who underwent surgery at multiple centers between March 2013 and May 2024. The pathological paraffin blocks were stained using immunohistochemistry for prognostic immune microenvironment markers, and patients were classified based on these results. Subsequently, deep learning features were extracted from enhanced CT scans, and a multimodal prediction model was constructed using imaging features and clinical information. The model's performance was evaluated using metrics including area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.

Study Timeline

• Status Verified: 2024-07
• Study Start: 2024-07-05
• Primary Completion: 2024-12-15
• Study First Submitted: 2024-12-29
• Study First Submitted QC: 2024-12-29
• Study First Posted: 2025-01-06
• Last Update Submitted: 2025-01-07
• Last Update Posted: 2025-01-09
• Study Completion: 2029-07-01

Recruitment & Eligibility

• Status: ACTIVE_NOT_RECRUITING
• Sex: All
• Target Recruitment: 247

Inclusion Criteria

1. Patients with pancreatic cancer, diagnosed through pathology;
2. Patients underwent surgery and received adjuvant chemotherapy after surgery.

Exclusion Criteria

1. Missing or inadequate quality of CT,
2. Incomplete clinical or pathological data.
3. Multiple primary malignancies;
4. History of malignancy.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Performance of deep learning model	Baseline treatment	The model's performance was evaluated using metrics including area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.

Trial Locations

Locations (1)

the Second Affiliated Hospital Zhejiang University School of Medicine; 🇨🇳 Hangzhou, Zhejiang, China