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PET/CT-Based Image Analysis and Machine Learning of Hypermetabolic Pulmonary Lesions

NCT06602674 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 647

Last updated 2025-07-23

No results posted yet for this study

Summary

First, we analyse the types, imaging findings and relevant treatment responses based on PET/CT to complete a more comprehensive view of pulmonary lymphomas.

Then, some models based on radiomics features will be developed to verify the possibility of differentiating pulmonary lymphomas via machine learning and develop a multi-class classification model.

The final objective of this study is to develop a set of deep learning models for preliminary lung lesion segmentation and multi-class classification. The models will classify FDG-avid lung lesions into four groups, each defined by their pathological origin, primary therapy and relevant clinical department.

Conditions

  • Lung Cancers
  • Pulmonary Lymphomas
  • Pulmonary Metastases
  • Benign Pulmonary Diseases

Interventions

OTHER

Observe the medical images

Observe the medical images via work station or local image analysing software

OTHER

Feature extraction

Extracting image feature via radiomics or deep learning methods

Sponsors & Collaborators

  • Shanghai Pulmonary Hospital, Shanghai, China

    collaborator OTHER

  • Jiangsu Province Hospital of Traditional Chinese Medicine

    collaborator OTHER

  • Ruijin North Hospital

    collaborator UNKNOWN

  • Luan people's hospital

    collaborator UNKNOWN

  • Ruijin Hospital

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2024-04-01
Primary Completion
2024-07-20
Completion
2025-04-30

Countries

  • China

Study Locations

More Related Trials

Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06602674 on ClinicalTrials.gov