Lung CancersNCT06602674
PET/CT-Based Image Analysis and Machine Learning of Hypermetabolic Pulmonary Lesions
An observational study of Lung Cancers and Pulmonary Lymphomas, run by Ruijin Hospital at 1 site in China.
Trial 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… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Lung Cancers, Pulmonary Lymphomas, Pulmonary Metastases
- What is being tested?
- Observe the medical images and Feature extraction
- How many people
- 647 participants
- Where
- 1 siteChina
- Study dates
- 1 Apr 2024to 30 Apr 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
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.
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
China1 site
Ruijin Hospital affiliated to Shanghai Jiao Tong University of Medicine
Shanghai, Shanghai Municipality, 200025
About this study
Official title: PET/CT Imaging-Based Distinction of Pulmonary Lymphoma and Other Hypermetabolic Lesions Via Imaging Manifestations and Machine Learning Techniques: a Multicenter Retrospective Study
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.
Read the detailed description
- 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.
- 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.
- 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.
- Being tested
- Observe the medical images, Feature extraction
- Keywords
- positron emission tomography/computed tomography, pulmonary lymphoma, lung cancer, deep learning, radiomics
- Medical subject headings
- Lung Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Observe the medical imagesOther
Observe the medical images via work station or local image analysing software
Feature extractionOther
Extracting image feature via radiomics or deep learning methods
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: 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. Receives: 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. Receives: 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. Receives: Other: Observe the medical images; Other: Feature extraction |
What is being measured
The primary outcome is the main question the study is designed to answer.
Imaging/radiomics/deep learning features of 18F-FDG PET/CT image
Measured: Baseline
Secondary outcomes (2)
Efficiency of the segmentation model
The effectiveness of the segmentation model is evaluated by the detection rate of lesions and the Dice similarity coefficient (2(A∩B)/ (A+B), A=segmented voxel volume, B=ground truth volume), which both describes the accuracy of dividing the lesion and the background.
Measured: immediately after the development and testing of models
Efficiency of the classification model
The classification model is evaluated by the accuracy [ (TP+TN)/(TP+FP+TN+FN) ] , precision [TP/(TP+FP)], recall [TP/(TP+FN)], F1-score [2*precision*recall/(precision+recall)], which all describes the ratio of correctly or wrongly classified lesions of the samples from different aspects. While the receiver operating characteristic (ROC) curve can illustrate this more visuelly. Area under the curve (AUC) calculated the proportion of area under the ROC curve, ranging from 0 to 1, representing the overall efficiency of classification in each group.
Measured: immediately after the development and testing of models
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted19 Sep 2024
- Study start1 Apr 2024
- Primary completion20 Jul 2024
- Study completion30 Apr 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Lung CancersStudies of the same condition, open and under way.
- More trials run by Ruijin HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Retrospective
- Enrolment
- 647 participants (actual)
Sponsor and investigators
- Lead sponsor
- Ruijin Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- Shanghai Pulmonary Hospital, Shanghai, China, Jiangsu Province Hospital of Traditional Chinese Medicine, Ruijin North Hospital, Luan people's hospital
- Responsible party
- Principal investigator, Hu Jiajia, Ruijin Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06602674
- Sponsor's protocol ID
- RuijinH 2024-70
- Status last verified
- Jul 2025
- First submitted
- 11 Sep 2024
- Last updated
- 23 Jul 2025
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 23 Jul 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- PET/CT Imaging-Based Distinction of Pulmonary Lymphoma and Other Hypermetabolic Lesions Via Imaging Manifestations and Machine Learning Techniques: a Multicenter Retrospective Study
- ClinicalTrials.gov ID
- NCT06602674
- Acronym / sponsor ID
- RuijinH 2024-70
- Keywords
- positron emission tomography/computed tomography, pulmonary lymphoma, lung cancer, deep learning, radiomics
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 647 participants (actual)
- Lead sponsor
- Ruijin HospitalUniversity, hospital or other
- Collaborators
- Shanghai Pulmonary Hospital, Shanghai, China, Jiangsu Province Hospital of Traditional Chinese Medicine, Ruijin North Hospital, Luan people's hospital
- Study start
- 1 Apr 2024 (actual)
- Primary completion
- 20 Jul 2024 (actual)
- Study completion
- 30 Apr 2025 (actual)
- First posted
- 19 Sep 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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
- 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.
- 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.
- 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.
Study design
- Observational model
- Other
- Time perspective
- Retrospective
- Number of arms
- 4
| Arm | Type | Receives |
|---|---|---|
| Pulmonary lymphoma | – | Other: Observe the medical images, Other: Feature extraction(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. |
| Lung cancer | – | Other: Observe the medical images, Other: Feature extraction(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. |
| Benign | – | Other: Observe the medical images, Other: Feature extraction(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. |
| Metastasis | – | Other: Observe the medical images, Other: Feature extraction(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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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\&amp;#39;an People\&amp;#39;s Hospital of Anhui Province and the Affiliated Hospital of Nanjing University of Traditional Chinese Medicine.
Key inclusion criteria (7 listed)
- 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.
Key exclusion criteria (3 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Observe the medical imagesObserve the medical images via work station or local image analysing software | Other | Benign, Lung cancer, Metastasis, Pulmonary lymphoma |
| Feature extractionExtracting image feature via radiomics or deep learning methods | Other | Benign, Lung cancer, Metastasis, Pulmonary lymphoma |
Primary outcome
Imaging/radiomics/deep learning features of 18F-FDG PET/CT image
Time frame: Baseline
2 secondary outcomes
Efficiency of the segmentation model
Time frame: immediately after the development and testing of models
Efficiency of the classification model
Time frame: immediately after the development and testing of models
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 23 Jul 2025.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Lung Cancers and Pulmonary Lymphomas, run by Ruijin Hospital at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 7 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 23 Jul 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Imaging/radiomics/deep learning features of 18F-FDG PET/CT image (time frame: Baseline). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 23 Jul 2025.