Multimodal Deep Learning Model Predicts Pancreatic Cancer Prognosis

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

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.

Study Overview

Status

Completed

Conditions

Intervention / 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 Type

Observational

Enrollment (Actual)

247

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

    • Zhejiang
      • Hangzhou, Zhejiang, China, 310009
        • The Second Affiliated Hospital Zhejiang University School of Medicine

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

Pancreatic cancer patients who were undergo surgery and received adjuvant chemotherapy after surgery.

Description

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 Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Training Cohort
Patients diagnosed with pancreatic cancer who underwent surgery and other treatments at the Second Affiliated Hospital, Zhejiang University School of Medicine
The high-throughput extraction of quantitative image features from medical images
Immunohistochemical analysis
Test Cohort
Patients diagnosed with pancreatic cancer who underwent surgery and other treatments at the Fourth Affiliated Hospital, Zhejiang University School of Medicine and Hangzhou Hosptial of Traditional Chinese Medicine
The high-throughput extraction of quantitative image features from medical images
Immunohistochemical analysis

What is the study measuring?

Primary Outcome Measures

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

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Collaborators

Investigators

  • Principal Investigator: Yulian Wu, PhD., Second Affiliated Hospital of Zhejiang University School of Medicine

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

July 5, 2024

Primary Completion (Actual)

December 15, 2024

Study Completion (Actual)

January 3, 2026

Study Registration Dates

First Submitted

December 29, 2024

First Submitted That Met QC Criteria

December 29, 2024

First Posted (Actual)

January 6, 2025

Study Record Updates

Last Update Posted (Actual)

January 7, 2026

Last Update Submitted That Met QC Criteria

January 5, 2026

Last Verified

July 1, 2024

More Information

Terms related to this study

Other Study ID Numbers

  • PCPAI

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

product manufactured in and exported from the U.S.

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.