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

NCT06602674 | Completed DMC

• 1 other identifier: RuijinH 2024-70
• Study Type: observational
• Target: 647
• Locations: 1 country
• Sites: 1

Brief 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

Keywords

positron emission tomography/computed tomography; pulmonary lymphoma; lung cancer; deep learning; radiomics

Outcome Measures

Primary Outcomes (1)

• Imaging/radiomics/deep learning features of 18F-FDG PET/CT image

  Baseline

Secondary Outcomes (2)

• Efficiency of the segmentation model

  immediately after the development and testing of models

• Efficiency of the classification model

  immediately after the development and testing of models

Study Arms (4)

Pulmonary lymphoma

(1) Adult patients (≥18 years). (2) Patients with primary or recurrent lymphoma, ≥6 months from last treatment. (3) Baseline assessment at hospital revealed PET-positive pulmonary lesions, CT-measured maximum diameter ≥3mm, visible across ≥2 image layers. (4) Pathological results within 3 months of exam date, confirmed lung lesion types via tracheoscopy, lung puncture, or surgery. Or baseline pulmonary lesions of lymphoma diagnosed by lymph node and external lung puncture, remains considered to be pulmonary lymphoma based on follow-up clinical and imaging evaluation.

Other: Observe the medical images; Other: Feature extraction

Lung cancer

(1) Adult patients (≥18 years). (2) Patients with primary lung cancer patients without prior malignancy (3) Baseline assessment at hospital revealed PET-positive pulmonary lesions, CT-measured maximum diameter ≥3mm, visible across ≥2 image layers. (4) Pathological results within 3 months of exam date, confirmed lung lesion types via tracheoscopy, lung puncture, or surgery.

Other: Observe the medical images; Other: Feature extraction

Benign

(1) Adult patients (≥18 years). (2) Patients with benign solid lung lesions, without prior malignancy. (3) Baseline assessment at hospital revealed PET-positive pulmonary lesions, CT-measured maximum diameter ≥3mm, visible across ≥2 image layers. (4) Pathological results within 3 months of exam date, confirmed lung lesion types via tracheoscopy, lung puncture, or surgery.

Other: Observe the medical images; Other: Feature extraction

Metastasis

(1) Adult patients (≥18 years). (2) Pulmonary metastatic patients, untreated with lung radiotherapy or particle implantation. (3) Baseline assessment at hospital revealed PET-positive pulmonary lesions, CT-measured maximum diameter ≥3mm, visible across ≥2 image layers. (4) Pathological results within 3 months of exam date, confirmed lung lesion types via tracheoscopy, lung puncture, or surgery. Or baseline pulmonary lesions of metastases diagnosed by lymph node and external lung puncture, remains considered to be pulmonary metastases based on follow-up clinical and imaging evaluation.

Other: Observe the medical images; Other: Feature extraction

Interventions

Observe the medical images OTHER

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

Benign; Lung cancer; Metastasis; Pulmonary lymphoma

Feature extraction OTHER

Extracting image feature via radiomics or deep learning methods

Benign; Lung cancer; Metastasis; Pulmonary lymphoma

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

This retrospective study enrolled patients who underwent PET/CT exams at the Nuclear Medicine Department from January 2015 to Feburary 2024 in 5 institutions: Ruijin Hospital, Proton Center of Ruijin North Hospital, Shanghai Pulmonary Hospital, Lu\'an People\'s Hospital of Anhui Province and the Affiliated Hospital of Nanjing University of Traditional Chinese Medicine.

You may qualify if:

• Adult patients (≥18 years);
• Primary or recurrent lymphoma, ≥6 months from last treatment; primary lung cancer patients without prior malignancy;
• Benign solid lung lesions, without prior malignancy;
• Pulmonary metastasis, untreated with lung radiotherapy or particle implantation;
• Baseline assessment revealing PET-positive pulmonary lesions.
• Pathological results within 3 months of exam date, confirmed lung lesion types via tracheoscopy, lung puncture, or surgery.
• Baseline pulmonary lesions remaining considered to be pulmonary lymphoma (or metastases) based on follow-up clinical and imaging evaluation.

You may not qualify if:

• Poor image quality;
• Inability to delineate the boundaries of lung lesions on CT images;
• Artifacts caused by nearby devices such as stents or drainage tubes.

Sponsors & Collaborators

• Ruijin Hospital: lead
• Shanghai Pulmonary Hospital, Shanghai, China: collaborator
• Jiangsu Province Hospital of Traditional Chinese Medicine: collaborator
• Ruijin North Hospital: collaborator
• Luan people's hospital: collaborator

Study Sites (1)

Ruijin Hospital affiliated to Shanghai Jiao Tong University of Medicine

Shanghai, Shanghai Municipality, 200025, China

Location

MeSH Terms

Conditions

Lung Neoplasms

Condition Hierarchy (Ancestors)

Respiratory Tract Neoplasms; Thoracic Neoplasms; Neoplasms by Site; Neoplasms; Lung Diseases; Respiratory Tract Diseases

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Nuclear Medicine

Study Record Dates

• First Submitted: September 11, 2024
• First Posted: September 19, 2024
• Study Start: April 1, 2024
• Primary Completion: July 20, 2024
• Study Completion: April 30, 2025
• Last Updated: July 23, 2025

Record last verified: 2025-07

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)