Clinical trial · Observational
PET/CT-Based Image Analysis and Machine Learning of Hypermetabolic Pulmonary Lesions
PET/CT Imaging-Based Distinction of Pulmonary Lymphoma and Other Hypermetabolic Lesions Via Imaging Manifestations and Machine Learning Techniques: a Multicenter Retrospective Study
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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
Conditions (4)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Benign Pulmonary Diseases | — | UNRESOLVED | — |
| Lung Cancers | Malignant Lung Neoplasm | CURATED_EXACT | 0.92 |
| Pulmonary Lymphomas | Lung Lymphoma | ALIAS | 0.90 |
| Pulmonary Metastases | Lung Neoplasm | PROBABILISTIC | 0.70 |
Interventions
Interventions (2)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Feature extraction | Other | — | UNRESOLVED |
| Observe the medical images | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (4)
- label
- Pulmonary lymphoma
- description
- (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.
- interventionNames
- Other: Observe the medical images
- Other: Feature extraction
- label
- Lung cancer
- description
- (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.
- interventionNames
- Other: Observe the medical images
- Other: Feature extraction
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
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.
References
Publications (0)
Data not yet available