Predicting Tumor Origin Based on Deep Learning of Lymph Node Puncture Cytology

January 30, 2025 updated by: Jianyong Lei, West China Hospital

A Multicenter Study on Predicting Tumor Origin Based on Deep Learning of Lymph Node Puncture Cytology

In this study, the investigators aimed to construct a deep learning diagnostic model that uses cytological images to predict primary unknown tumor origins in patients with tumors combined with lymph node metastases. After the model is constructed, the model will be validated by a large-scale test set to test the model performance. The investigators also propose to compare the performance of the constructed model in diagnosing cytology smears compared to human pathologists.

Study Overview

Status

Recruiting

Conditions

Study Type

Observational

Enrollment (Estimated)

10000

Contacts and Locations

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

Study Contact

Study Locations

  • China
    • Sichuan
      • Chengdu, Sichuan, China, 610041
        • Recruiting
        • West China Hospital of Sichuan University

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

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Sampling Method

Non-Probability Sample

Study Population

Lymph node cytology smear imaging data and clinical data in the training cohort were obtained from West China Hospital of Sichuan University (October 1, 2008 to August 31, 2024).

Lymph node cytology smear imaging data and clinical data in the validation cohort were obtained from the Department of Pathology of the First Affiliated Hospital of Zhengzhou University, Sichuan Cancer Hospital, and Cancer Hospital of the Chinese Academy of Medical Sciences (January 1, 2020 to August 31, 2024).

Description

Inclusion Criteria:

  • From West China Hospital of Sichuan University (October 1, 2008-August 31, 2024) with corresponding clinical data, including age, sex, specimen puncture site, pathologic diagnosis, pathologic type, whether immunocytochemistry was added, clinical diagnosis, lesion site, co-morbidities, history of malignancy, treatment modality, occurrence of postoperative complications, total number of days of hospitalization postoperatively, and survival time;
  • From the Department of Pathology of the First Affiliated Hospital of Zhengzhou University, the Sichuan Provincial Cancer Hospital, and the Cancer Hospital of the Chinese Academy of Medical Sciences (January 1, 2020-August 31, 2024) with corresponding clinical data, including age, sex, specimen puncture site, pathologic diagnosis, pathologic type, whether immunocytochemistry was added, clinical diagnosis, lesion site, co-morbidities, history of malignancy, treatment modality, occurrence of postoperative complications, total number of days of hospitalization postoperatively, and survival time.

Exclusion Criteria:

  • Images lacking any supporting clinical or pathologic evidence to support a primary origin and its corresponding clinical information;
  • Blank, poorly focused, and low-quality images containing severe artifacts and their corresponding clinical information.

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
a training cohort and a validation cohort

Training cohort: Lymph node cytology smear imaging data and corresponding clinical data from October 1, 2008-August 31, 2024 in West China Hospital of Sichuan University.

Validation cohort: Lymph node cytology smear imaging data and corresponding clinical data from January 1, 2020-August 31, 2024 in the First Affiliated Hospital of Zhengzhou University, Sichuan Provincial Cancer Hospital, and the Cancer Hospital of China Academy of Medical Sciences.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Model performance metrics
Time Frame: 1 year
Model performance was evaluated by Positive Predictive Value (PPV), Negative Predictive Value (NPV), Accuracy, Sensitivity and Specificity.
1 year

Collaborators and Investigators

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

Sponsor

West China Hospital

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)

November 11, 2024

Primary Completion (Estimated)

December 1, 2025

Study Completion (Estimated)

December 1, 2025

Study Registration Dates

First Submitted

January 24, 2025

First Submitted That Met QC Criteria

January 30, 2025

First Posted (Actual)

March 25, 2025

Study Record Updates

Last Update Posted (Actual)

March 25, 2025

Last Update Submitted That Met QC Criteria

January 30, 2025

Last Verified

January 1, 2025

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 2024 Audit No. (1041)

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

IPD Plan Description

The availability of the large amount of cytology image imaging data from the study is limited due to its oversized storage and therefore not publicly available.

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

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.

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