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NCT Number: NCT07463300

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

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

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Key information

Conditions

Lung Cancer Disease Lung Diseases Lung Neoplasms Neoplasms Neoplasms by Site Pathologic Processes Pathological Conditions, Signs and Symptoms Respiratory Tract Diseases Respiratory Tract Neoplasms Thoracic Neoplasms

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Guangdong Second Provincial General Hospital, Guangzhou, Guangdong, China

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About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

PET imaging analysis, data mining, and AI model developing

Other

PET imaging analysis, data mining, and AI model developing

Primary outcomes

  1. Accurate differentiation between small cell lung cancer and non-small cell lung cancer

    Time frame: 1 year

Secondary outcomes

  1. Histological subtyping of NSCLC, including adenocarcinoma, squamous cell carcinoma, and other NSCLC subtypes

    Time frame: 1 year

Other outcomes

  1. Accurate identification of EGFR gene mutation status

    Time frame: 1 year

Study contacts

Contact information is provided by the study sponsor or research team.

Hong Zhang

CONTACT

[email protected]

0086-571-87767138

Xiaohui Zhang

CONTACT

[email protected]

0086-571-87767138

Sponsors and collaborators

Lead sponsor

Second Affiliated Hospital, School of Medicine, Zhejiang University

Other

Collaborators

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

Registry information

Important dates

Study start
2024
Primary completion
2027
Study completion
2027
First posted
Mar 11, 2026
Registry last updated
Mar 11, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

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This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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