Multimodal Deep Learning Model Predicts Pancreatic Cancer Prognosis

Study Overview

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.

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.

Pancreatic Adenocarcinoma

DIAGNOSTIC_TEST: No Interventions
DIAGNOSTIC_TEST: No Interventions

PCPAI

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 Registration Dates	Results Reporting Dates	Study Record Updates
First Submitted 2024-12-29	Results First Submitted N/A	Last Update Submitted that met QC Criteria 2025-01-07
First Submitted that Met QC Criteria 2024-12-29	Results First Submitted that Met QC Criteria N/A	Last Update Posted (ACTUAL) 2025-01-09
First Posted (ACTUAL) 2025-01-06	Results First Posted N/A	Last Verified 2024-07

Study Plan

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

Design Details

Primary Purpose:
N/A

Allocation:
N/A

Interventional Model:
N/A

Masking:
N/A

Arms and Interventions

Participant Group/Arm	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	DIAGNOSTIC_TEST: No Interventions The high-throughput extraction of quantitative image features from medical images DIAGNOSTIC_TEST: No Interventions 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	DIAGNOSTIC_TEST: No Interventions The high-throughput extraction of quantitative image features from medical images DIAGNOSTIC_TEST: No Interventions Immunohistochemical analysis

Participant Group/Arm

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

DIAGNOSTIC_TEST: No Interventions

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

DIAGNOSTIC_TEST: No Interventions

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

DIAGNOSTIC_TEST: No Interventions

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

DIAGNOSTIC_TEST: No Interventions

Immunohistochemical analysis

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

Secondary Outcome Measures	Measure Description	Time Frame

Contacts and Locations

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

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.

Ages Eligible for Study:
ALL

Sexes Eligible for Study:
18 Years

Accepts Healthy Volunteers:

Collaborators and Investigators

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

the fourth affiliated hospital, Zhejiang university school of medcine
Hangzhou Hospital of Traditional Chinese Medicine

Investigators

PRINCIPAL_INVESTIGATOR: Yulian Wu, PhD., Second Affiliated Hospital of Zhejiang University School of Medicine

Publications

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

General Publications

No publications available

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Clinical Trial Record