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Recruiting NCT07463300

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

— Observational Lung Cancer

For patients and families

In plain language

An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.

What is being studied
The protocol lists: PET imaging analysis, data mining, and AI model developing.
Who it may be relevant to
Registry conditions: Lung Cancer. Basic parameters: from 18 years · All.
What needs checking
Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
Where it takes place
China
Next step
Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →

Unsure about the terms? Read our patient guide →

Overview

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

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.

Interventions

  • Other PET imaging analysis, data mining, and AI model developing
    PET imaging analysis, data mining, and AI model developing

Primary outcome measures

  • Accurate differentiation between small cell lung cancer and non-small cell lung cancer [Time frame: 1 year]
Secondary outcome measures (1)
  • Histological subtyping of NSCLC, including adenocarcinoma, squamous cell carcinoma, and other NSCLC subtypes [Time frame: 1 year]

Eligibility criteria

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

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Observational model
Cohort

Study locations

China · 9 centers
  • Guangdong Second Provincial General Hospital — Guangzhou
  • Wuhan Tongji Hospital — Wuhan
  • Zhongnan Hospital — Wuhan
  • Northern Jiangsu People's Hospital — Yangzhou
  • First Hospital of China Medical University — Shenyang
  • West China Hospital — Chengdu
  • The First Affiliated Hospital of Zhejiang Chinese Medical University — Hangzhou
  • Department of Nuclear Medicine and PET/CT Center, The Second Affiliated Hospital, School o — Hangzhou
  • … and 1 more center

Identifiers

NCT: NCT07463300 · 2024-0689

Primary sources (government registries)

View this study on ClinicalTrials.gov ↗