Clinical trial · Observational
Imaging-based Deep Learning for Lung Cancer Diagnosis and Staging
The Role of CNN Architecture-based Transfer Learning of Medical Imaging in Lung Cancer Diagnosis and Staging
- 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)
Lung cancer diagnosis and staging are two fundamental and critical issue in clinical lung cancer management and therapeutic decision-making. Invasive procedures for pathologic analysis are gold standard for diagnosis and staging, however, invasive procedures related-complications are inevitable. Noninvasive medical imaging is a powerful tool, however there is almost no room for improvement just according to the experience of radiologist and clinician. The researchers will investigate the role of computer based deep learning of medical imaging in the diagnosis of lesion of lung, lymph node and other sites suspected with metastasis.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Lung Cancer | Malignant Lung Neoplasm | CURATED_EXACT | 0.92 |
Interventions
Interventions (2)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| punture | Procedure | — | UNRESOLVED |
| surgery | Procedure | — | UNRESOLVED |
Design
Arms and outcomes
Arms (2)
- label
- cancer cell involvement predicted by deep learning
- description
- the participants with lesions of lung, lymph node or other sites predicted as positive for cancer cell involvement by imaging based deep learning.
- interventionNames
- Procedure: surgery
- Procedure: punture
- label
- no cancer cell involvement predicted by deep learning
- description
- the participants with lesions of lung, lymph node or other sites predicted as negative for cancer cell involvement by imaging based deep learning.
- interventionNames
- Procedure: surgery
- Procedure: punture
Primary outcomes (1)
- measure
- pathologic result revealed cancer cell involvement in lesion
- timeFrame
- 1 month after the pathologic test
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
- Maximum age
- 75 Years
Show eligibility criteria text
Inclusion Criteria: * Pathological diagnosis of lung cancer * PET/CT or CT examination before any cancer-specific treatment Exclusion Criteria: * A history of other malignancies
References
Publications (0)
Data not yet available