NCT06760234

Multimodal Deep Learning Model Predicts Pancreatic Cancer Prognosis

Prediction of Pancreatic Cancer Prognosis Using a Multimodal Deep Learning Model Based on Intratumoral Immune Microenvironment

• Status: Completed
• Phase: —
• Study type: Observational
• Enrollment: 247 (actual)
• Sponsor: Second Affiliated Hospital, School of Medicine, Zhejiang University · Academic / Other
• Sex: All
• Age: 18 Years – 90 Years
• Healthy volunteers: Not accepted

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.

Conditions

• Pancreatic Adenocarcinoma

Interventions

Type	Name	Description
DIAGNOSTIC_TEST	No Interventions	The high-throughput extraction of quantitative image features from medical images
DIAGNOSTIC_TEST	No Interventions	Immunohistochemical analysis

Timeline

• Start date: 2024-07-05
• Primary completion: 2024-12-15
• Completion: 2026-01-03
• First posted: 2025-01-06
• Last updated: 2026-01-07

Locations

1 site across 1 country: China