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

NCT ID: NCT06602674

Last Updated: 2025-07-23

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 647 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2024-04-01
• Study Completion Date: 2025-04-30

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.

Detailed Description

1. The local image feature extraction software (LIFEx, v 7.4.0, France) was employed for the image review and measurement of relevant data. Three observers independently interpreted the images. In cases of disagreement, the opinion of a senior doctor with over a decade of experience was given precedence. The imaging findings were recorded based on the baseline examinations. Lesion counts, locations, and descriptive labels were systematically logged in accordance with the norms set out in imaging report. The statistical software SPSS (v26.0) was used in data sorting and calculation. Chi-square test was employed to compare SPL and PPL based on categorical variables like CT findings, while T-test was used to assess continuous variables like glycemia and SUV. Given the predominance of categorical variables, chi-square, or Fisher's exact test (for samples <40 or >20% cells with <5 expected counts) was utilised to assess treatment response and imaging performance. Spearman's correlation coefficient was employed to analyse the relationship between categorical and SUV-based continuous variables.

2. In this study, the metabolic tumor volume at a relative threshold of 40% (MTV40%) was selected as the volume of interest (VOI) for image analysis. For feature extraction, we employed the Python (v3.11.7)-based radiomics feature extraction toolkit PyRadiomics (v3.1.0), along with the medical image processing library SimpleITK (v2.3.1), the numerical computation and data manipulation library Numpy (v1.26.2), and the wavelet transform library PyWavelet (v1.5.0). Feature selection was conducted using RStudio (v.2023.12.0+369) based on the R programming language (v4.2.0). To ensure computational efficiency and avoid overfitting, the number of features retained was limited to 10% or less of the number of lesions in the training set. Model analysis and validation were primarily performed using RStudio as well.

3. The deep learning study divides the task of identifying and classifying hypermetabolic lung lesions into two stages: segmentation and classification. In the segmentation stage, we first utilized the open-source 2D model Lungmask to automatically crop the lung region from whole-body PET/CT images, ensuring that subsequent processing is focused on the lung area. Next, we developed a 3D UNet model with residual modules specifically designed for segmenting hypermetabolic lung lesions. This model takes the cropped PET/CT images as input, efficiently extracting lesion information from the three-dimensional images and accurately segmenting the hypermetabolic lung lesion areas.The model was then applied to both internal test sets and external validation sets for inference, resulting in the extraction of lesion-containing ROIs.

Conditions

Lung Cancers; Pulmonary Lymphomas; Pulmonary Metastases; Benign Pulmonary Diseases

Study Design

• Observational Model Type: OTHER
• Study Time Perspective: RETROSPECTIVE

Study Groups

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.

Observe the medical images

Intervention Type: OTHER

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

Feature extraction

Intervention Type: OTHER

Extracting image feature via radiomics or deep learning methods

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.

Observe the medical images

Intervention Type: OTHER

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

Feature extraction

Intervention Type: OTHER

Extracting image feature via radiomics or deep learning methods

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.

Observe the medical images

Intervention Type: OTHER

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

Feature extraction

Intervention Type: OTHER

Extracting image feature via radiomics or deep learning methods

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.

Observe the medical images

Intervention Type: OTHER

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

Feature extraction

Intervention Type: OTHER

Extracting image feature via radiomics or deep learning methods

Interventions

Observe the medical images

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

Intervention Type: OTHER

Feature extraction

Extracting image feature via radiomics or deep learning methods

Intervention Type: OTHER

Eligibility Criteria

Inclusion Criteria

1. Adult patients (≥18 years);

2. Primary or recurrent lymphoma, ≥6 months from last treatment; primary lung cancer patients without prior malignancy;

3. Benign solid lung lesions, without prior malignancy;

4. Pulmonary metastasis, untreated with lung radiotherapy or particle implantation;

5. Baseline assessment revealing PET-positive pulmonary lesions.

6. Pathological results within 3 months of exam date, confirmed lung lesion types via tracheoscopy, lung puncture, or surgery.

7. Baseline pulmonary lesions remaining considered to be pulmonary lymphoma (or metastases) based on follow-up clinical and imaging evaluation.

Exclusion Criteria

1. Poor image quality;

2. Inability to delineate the boundaries of lung lesions on CT images;

3. Artifacts caused by nearby devices such as stents or drainage tubes.

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Shanghai Pulmonary Hospital, Shanghai, China

OTHER

Sponsor Role: collaborator

Jiangsu Province Hospital of Traditional Chinese Medicine

OTHER

Sponsor Role: collaborator

Ruijin North Hospital

UNKNOWN

Sponsor Role: collaborator

Luan people's hospital

UNKNOWN

Sponsor Role: collaborator

Ruijin Hospital

OTHER

Sponsor Role: lead

Responsible Party

Hu Jiajia

Nuclear Medicine

Responsibility Role: PRINCIPAL_INVESTIGATOR

Locations

Ruijin Hospital affiliated to Shanghai Jiao Tong University of Medicine

Shanghai, Shanghai Municipality, China

Countries

China

Other Identifiers

RuijinH 2024-70

Identifier Type: -

Identifier Source: org_study_id