Clinical trial · Observational
Deep Learning Signature for Predicting Complete Pathological Response to Neoadjuvant Chemoimmunotherapy in Non-small Cell Lung Cancer
An Integration of a Computed Tomography/Positron Emission Tomography/Whole Slide Image (CT/PET/WSI) Based Deep Learning Signature for Predicting Complete Pathological Response to Neoadjuvant Chemoimmunotherapy in Non-small Cell Lung Cancer: A Multicenter Study
NCT05925751CI-TRIAL-00067700unknownClinicalTrials.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)
The purpose of this study is to evaluate the performance of a CT/PET/ WSI-based deep learning signature for predicting complete pathological response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer
Conditions
Conditions (3)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Complete Pathological Response | — | UNRESOLVED | — |
| Neoadjuvant Chemoimmunotherapy | — | UNRESOLVED | — |
| Non-small Cell Lung Cancer | Lung Non-Small Cell Carcinoma | ALIAS | 0.90 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| CT/PET/WSI-based Deep Learning Signature | Diagnostic Test | — | UNRESOLVED |
Design
Arms and outcomes
Arms (0)
[]Primary outcomes (1)
- measure
- Area under the receiver operating characteristic curve
- timeFrame
- 2023.5.1-2023.10.31
- description
- The area under the receiver operating characteristic curve (ROC) of the deep learning model in predicting complete pathological response (CPR). CPR was defined as no residual tumor in both resected primary tumor and lymph nodes. Patients with non-small cell lung cancer receiving neoadjuvant chemoimmunotherapy will achieve either CPR or non-CPR, which can be confirmed by pathological examination after surgical resection. And the model will output the predictive value (CPR/non-CPR) for each patient receiving neoadjuvant chemoimmunotherapy.
Secondary outcomes (1)
- measure
- Sensitivity
- timeFrame
- 2023.5.1-2023.10.31
- description
- The sensitivity of the deep learning model in predicting complete pathological response. CPR was defined as no residual tumor in both resected primary tumor and lymph nodes. Patients with non-small cell lung cancer receiving neoadjuvant chemoimmunotherapy will achieve either CPR or non-CPR, which can be confirmed by pathological examination after surgical resection. And the model will output the predictive value (CPR/non-CPR) for each patient receiving neoadjuvant chemoimmunotherapy.
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 20 Years
- Maximum age
- 75 Years
Show eligibility criteria text
Inclusion Criteria: 1. Age ranging from 20-75 years; 2. Patients who underwent curative surgery after neoadjuvant chemoimmunotherapy for NSCLC; 3. Obtained written informed consent. Exclusion Criteria: 1. Missing image data; 2. Pathological N3 disease.
References
Publications (0)
Data not yet available
No reference posted for this study.