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Trial Title:
Advanced Data-Aided Medicine Part Lung Cancer
NCT ID:
NCT05783024
Condition:
Lung Cancer
Conditions: Official terms:
Lung Neoplasms
Conditions: Keywords:
AI
ML
Lung Cancer
Prediction
Study type:
Observational
Overall status:
Recruiting
Study design:
Time perspective:
Retrospective
Intervention:
Intervention type:
Other
Intervention name:
Data collection
Description:
retrospective data collection
Summary:
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.
Detailed description:
The aim of this study is to prepare and unlock siloed real-world-data (RWD) for analysis
and artificial model construction with the goal to derive real-world-evidence (RWE).
Nowadays physicians and nurses are gathering data from patients and register the data in
the electronic health record system. Datapoints are often not available in a structured
format, so data gathering and unlocking is done manually upon request. Careful analysis
of the collected data using artificial intelligence tools might also help to predict
which patients are at the highest risk of unscheduled health care use, emergency
department visits and hospital admissions. Therefore, a model able to predict the
relevant outcomes would be of significant help to the physicians' daily practice.
Primary Objective 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 and later on a deep patient. Model input data sources are
hospital data like demographics, baseline health status, prior treatments, tumour
characteristics, comorbidities , imaging data & physiological data, detailed treatment
data.
Secondary Objectives
- Automatic unlocking, collection & transformation of lung cancer datapoints to OMOP
common data model so that data is readily available for further research & analysis
- Training and validating supervised machine learning models with a limited feature
set as input to predict lung cancer patient outcomes
- Constructing a digital patient by training an AI model fed with all data available
in OMOP common data model
- Validating a digital patient & optimal feature selection to enhance AI model
performance via unsupervised learning techniques
The potential of applying transformers to represent patients is truly personalized and
even predictive medicine. The reason is that transformers are an instrument which make it
possible to deal with millions of interacting and non-linearly behaving parameters. Hence
data sources can be extended to include genetic information and so on. Optimal feature
selection from a digital patient can enhance AI model performance via unsupervised
learning techniques, and so further finetune prediction models for daily practice.
To build a virtual twin to represent a patient in detail so population analysis and model
building is clinically relevant.
Criteria for eligibility:
Study pop:
Lung cancer patients included in the lung cancer patient pathway from 20/04/2018
Sampling method:
Non-Probability Sample
Criteria:
Inclusion Criteria:
- Lung cancer patients included in the lung cancer patient pathway
Exclusion Criteria:
- None specified
Gender:
All
Minimum age:
N/A
Maximum age:
N/A
Healthy volunteers:
No
Locations:
Facility:
Name:
AZ Delta
Address:
City:
Roeselare
Zip:
8800
Country:
Belgium
Status:
Recruiting
Contact:
Last name:
Peter De Jaeger, PhD, Prof
Phone:
003251237650
Email:
peter.dejaeger@azdelta.be
Investigator:
Last name:
Ingel Demedts, Prof
Email:
Principal Investigator
Investigator:
Last name:
Hannelore Bode, MD
Email:
Sub-Investigator
Investigator:
Last name:
Bernard Bouckaerts, MD
Email:
Sub-Investigator
Investigator:
Last name:
Kris Carron, MD
Email:
Sub-Investigator
Investigator:
Last name:
Stephanie Dobbelaere, MD
Email:
Sub-Investigator
Investigator:
Last name:
Ulrike Himpe, MD
Email:
Sub-Investigator
Investigator:
Last name:
Heidi Mariën, MD
Email:
Sub-Investigator
Investigator:
Last name:
Peter Van Haecke, MD
Email:
Sub-Investigator
Investigator:
Last name:
Wim Verbeke, MD
Email:
Sub-Investigator
Investigator:
Last name:
Peter De Jaeger, Prof, PhD
Email:
Sub-Investigator
Investigator:
Last name:
Pieter-Jan Lammertyn
Email:
Sub-Investigator
Investigator:
Last name:
Louise Berteloot
Email:
Sub-Investigator
Investigator:
Last name:
Kim Denturck, Msc ir
Email:
Sub-Investigator
Start date:
September 27, 2021
Completion date:
December 31, 2024
Lead sponsor:
Agency:
AZ Delta
Agency class:
Other
Source:
AZ Delta
Record processing date:
ClinicalTrials.gov processed this data on November 12, 2024
Source: ClinicalTrials.gov page:
https://clinicaltrials.gov/ct2/show/NCT05783024
