Clinical trial · Observational
Deep Learning Model to Predict the Recurrence of Stage IA Invasive Lung Adenocarcinoma After Sub-lobar Resection
NCT06659601CI-TRIAL-00081975DL-Rec-ILAcompletedClinicalTrials.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 aims to develop a deep learning model based on noncontrast CT images to predict the recurrence risk of stage IA invasive lung adenocarcinoma after sub-lobar resection，which can serve as potential tool to assist thoracic surgeons in making optimal treatment decisions.The study will use existing CT data to train and validate the model, without requiring any additional intervention for the participants.
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 |
|---|---|---|---|
| Focus on Developing a Deep Learning Model to Predict the Recurrence Risk of Stage IA Invasive Lung Adenocarcinoma After Sub-lobar Resection | Lung Neoplasm | ONTOLOGY_EXACT | 0.90 |
Interventions
Interventions (0)
Data not yet available
No intervention recorded.
Design
Arms and outcomes
Arms (3)
- label
- Training Cohort
- description
- Patients in this cohort diagnosed with stage IA invasive lung adenocarcinoma who underwent sub-lobar resection. This cohort is used to train the 3D deep learning model to predict recurrence risk.
- label
- Validation Cohort
- description
- Patients in this cohort with stage IA ILADC. It is used to validate the model performance internally and assess its generalization within the same institution。
- label
- Testing Cohort
- description
- Patients in this cohort from other institution. It is used to test the generalizability of the model in predicting recurrence risk in an independent dataset.
Primary outcomes (1)
- measure
- Recurrence Prediction Accuracy
- timeFrame
- October 2024
- description
Eligibility
Eligibility (as posted)
- Sex
- All
Show eligibility criteria text
Inclusion Criteria:(i) pathological confirmation of LADC; (ii) undergoing sub-lobar resection (wedge resection or segmentectomy); (iii) CT scanning prior to surgery; (iv) pathological staging of IA; and (v) complete clinical and follow-up data. \- Exclusion Criteria:(i) multiple primary LADC; and (ii) other pulmonary lesions that might interfere with the morphological assessment of tumors. \-
References
Publications (0)
Data not yet available
No reference posted for this study.