Clinical trial · Observational
Pathological Classification of Pulmonary Nodules in Images Using Deep Learning
Pathological Classification of Pulmonary Nodules From Gross Images of Tumor Using Deep Learning
NCT05221814CI-TRIAL-00056311unknownClinicalTrials.gov clinicaltrialsProvenance
- 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)
This study aimed to develop a deep-learning model to automatically classify pulmonary nodules based on white-light images and to evaluate the model performance. Besides, suitable operation could be chosen with the help of this model, which could shorten the time of surgery.
Conditions
Conditions (2)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Artificial Intelligence | — | UNRESOLVED | — |
| Lung Cancer | Malignant Lung Neoplasm | CURATED_EXACT | 0.92 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| gross pathologic photo based deep learning model | Diagnostic Test | — | UNRESOLVED |
Design
Arms and outcomes
Arms (0)
[]Primary outcomes (2)
- measure
- 1. Pathological subtype
- timeFrame
- through study completion, an average of 2 year
- description
- According to WHO classification of pulmonary tumors in 2020, this study classify pulmonary tumors into adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC). We would collect the reports of pathological type of pulmonary nodules after surgery.
- measure
- Area Under the Curve (AUC)
- timeFrame
- through study completion, an average of 2 year
- description
- The area under the ROC curve based the predicton efficency of model
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
- Maximum age
- 80 Years
Show eligibility criteria text
Inclusion Criteria: 1. Male or female，18 years and older. 2. Patients haven't undergone any therapy. 3. The pulmonary nodules were confirmed AIS, MIA or IAC. 4. The sizes of pulmonary nodules were less than 3cm. 5. The images were jpg format. Exclusion Criteria: 1. Suffering from other tumor disease before or at the same time. 2. Images with poor quality or low resolution that precluded proper classification.
References
Publications (0)
Data not yet available
No reference posted for this study.