A Hierarchical Multi-modal AI Framework for Pathological and Genetic Subtyping of Lung Cancer Based on PET/CT Imaging

March 6, 2026 updated by: Second Affiliated Hospital, School of Medicine, Zhejiang University
PET/CT imaging and clinical information (age, gender, smoking history, family history of cancer, history of present illness, and several tumor biomarkers, etc.) were used to establish a hierarchical multi-modal AI framework for pathological and genetic subtyping of lung cancer

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Detailed Description

The multi-modal AI framework is developed to facilitate a hierarchical and precise stratification process. The first level involves the accurate differentiation between small cell lung cancer and non-small cell lung cancer (NSCLC) in patients diagnosed with lung cancer. The second level entails the further categorization of NSCLC patients into adenocarcinoma, squamous cell carcinoma, and other less prevalent subtypes. The third level involves predicting the mutation status of the EGFR driver gene, which is most-commonly observed in patients with lung adenocarcinoma. The whole cohort was divided into the training cohort (retrospective), validation cohort (retrospective), test cohort (retrospective), and prospective cohort.

Study Type

Observational

Enrollment (Estimated)

5500

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 Contact Backup

Study Locations

  • China
    • Guangdong
      • Guangzhou, Guangdong, China, 510403
        • Recruiting
        • Guangdong Second Provincial General Hospital
        • Contact:
    • Hubei
      • Wuhan, Hubei, China, 430071
        • Recruiting
        • Zhongnan Hospital
        • Contact:
      • Wuhan, Hubei, China, 430030
        • Recruiting
        • Wuhan Tongji Hospital
        • Contact:
    • Jiangsu
      • Yangzhou, Jiangsu, China, 225001
        • Recruiting
        • Northern Jiangsu People's Hospital
        • Contact:
    • Liaoning
      • Shenyang, Liaoning, China, 110801
        • Recruiting
        • First Hospital of China Medical University
        • Contact:
    • Sichuan
      • Chengdu, Sichuan, China, 610041
    • Zhejiang
      • Hangzhou, Zhejiang, China, 310022
        • Recruiting
        • Zhejiang Cancer Hospital
        • Contact:
      • Hangzhou, Zhejiang, China, 310006
        • Recruiting
        • The First Affiliated Hospital of Zhejiang Chinese Medical University
        • Contact:
      • Hangzhou, Zhejiang, China, 310009
        • Recruiting
        • Department of Nuclear Medicine and PET/CT Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University
        • Contact:

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

The target population of this study is patients with lung cancer who have undergone pre-treatment 18F-FDG PET/CT. The study will assess the relationships between PET/CT-based imaging features and clinical variables with the pathological subtypes.

Description

Inclusion Criteria:

  • Newly diagnosed NSCLC confirmed pathologically
  • Age ≥18 y
  • Underwent pre-treatment 18F-FDG PET/CT scan
  • No prior anti-tumor treatments
  • No history of other malignancies

Exclusion Criteria:

▪ Pure ground-glass nodules with no FDG uptake

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
All patients underwent pre-treatment 18F-FDG PET/CT scan.
Other: PET imaging analysis, data mining, and AI model developing
PET imaging analysis, data mining, and AI model developing
Validation Cohort
All patients underwent pre-treatment 18F-FDG PET/CT scan.
Other: PET imaging analysis, data mining, and AI model developing
PET imaging analysis, data mining, and AI model developing
Test cohort
All patients underwent pre-treatment 18F-FDG PET/CT scan.
Other: PET imaging analysis, data mining, and AI model developing
PET imaging analysis, data mining, and AI model developing
Prospective cohort
All patients underwent pre-treatment 18F-FDG PET/CT scan.
Other: PET imaging analysis, data mining, and AI model developing
PET imaging analysis, data mining, and AI model developing

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Time Frame
Accurate differentiation between small cell lung cancer and non-small cell lung cancer
Time Frame: 1 year
1 year

Secondary Outcome Measures

Outcome Measure
Time Frame
Histological subtyping of NSCLC, including adenocarcinoma, squamous cell carcinoma, and other NSCLC subtypes
Time Frame: 1 year
1 year

Other Outcome Measures

Outcome Measure
Time Frame
Accurate identification of EGFR gene mutation status
Time Frame: 1 year
1 year

Collaborators and Investigators

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

Sponsor

Second Affiliated Hospital, School of Medicine, Zhejiang University

Collaborators

Zhejiang Cancer Hospital
West China Hospital
Zhongnan Hospital
First Hospital of China Medical University
Wuhan TongJi Hospital
Guangdong Second Provincial General Hospital
The First Affiliated Hospital of Zhejiang Chinese Medical University
Northern Jiangsu People's Hospital

Investigators

  • Principal Investigator: Hong Zhang, Department of Nuclear Medicine and PET/CT Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University

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)

August 1, 2024

Primary Completion (Estimated)

August 1, 2027

Study Completion (Estimated)

August 1, 2027

Study Registration Dates

First Submitted

March 6, 2026

First Submitted That Met QC Criteria

March 6, 2026

First Posted (Actual)

March 11, 2026

Study Record Updates

Last Update Posted (Actual)

March 11, 2026

Last Update Submitted That Met QC Criteria

March 6, 2026

Last Verified

March 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 2024-0689

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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