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

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT06760234

Enrollment

247

Registered

2025-01-06

Start date

2024-07-05

Completion date

2026-01-03

Last updated

2026-01-07

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Pancreatic Adenocarcinoma

Keywords

Deep Learning Model, Pancreatic adenocarcinoma, prognosis

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.

Interventions

DIAGNOSTIC_TESTNo Interventions

The high-throughput extraction of quantitative image features from medical images

Study design

Observational model

COHORT

Time perspective

RETROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

18 Years to 90 Years

Healthy volunteers

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.

Design outcomes

Primary

Measure	Time frame	Description
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.

Countries

China

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026