Clinical trial · Observational
Advanced Data-Aided Medicine Part Lung Cancer
ADAM Substudy Luik 2: Observational Retrospective Single Centre Cohort Study on Constructing & Validating AI Prediction Models for Outcomes of Lung Cancer Patients
- 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)
Developing and validating an AI model that supports physicians in their decision process for treating lung cancer patients. This AI model needs to predict the probability of (the evolution of) the outcomes, based on clinical data and a simulated lung cancer treatment plan. The outcome probabilities can be evaluated with different treatment plans to identify the optimal plan. Initially, the input data will be a limited set of selected features such as general patient information, tumour characteristics, laboratory measurement results, comorbidities and treatments. Finally, the goal is to use a deep patient as input to the models.This deep patient is an AI model on its own, trained on hospital data, as described in secondary objectives.
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 (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Data collection | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (0)
[]Primary outcomes (1)
- measure
- Data into international common data model ready for AI input
- timeFrame
- 2022
- description
- Lung cancer hospital data translated and clinically validated in UMLS concepts and stored in the OMOP common data model
Secondary outcomes (3)
- measure
- Supervised machine learning
- timeFrame
- 2023
- description
- Training and validating supervised machine learning models with a limited set of selected features as input to predict lung cancer patient outcomes.
- measure
- Digital patient construction
- timeFrame
- 2023
- description
- Constructing a digital patient by training an AI model
Eligibility
Eligibility (as posted)
- Sex
- All
Show eligibility criteria text
Inclusion Criteria: * Lung cancer patients included in the lung cancer patient pathway Exclusion Criteria: * None specified
References
Publications (0)
Data not yet available