Lung CancerADAMpartlungNCT05783024
Advanced Data-Aided Medicine Part Lung Cancer
An observational study of Lung Cancer, run by AZ Delta at 1 site in Belgium.
Trial 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. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Lung Cancer
- What is being tested?
- Data collection
- How many people
- 500 participants
- Where
- 1 siteBelgium
- Study dates
- 27 Sep 2021to 31 Dec 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: Lung cancer patients included in the lung cancer patient pathway from 20/04/2018
Who may be able to join
- Lung cancer patients included in the lung cancer patient pathway
Who cannot join
- None specified
Trial locations and contacts
1 site in 1 country.
Ingel Demedts, MD contact
Belgium1 site, 1 recruiting
- Recruiting
AZ Delta
Roeselare, West-Vlaanderen, 8800
Peter De Jaeger, PhD, Prof, 003251237650, peter.dejaeger@azdelta.be
Ingel Demedts, Prof
About this study
Official title: ADAM Substudy Luik 2: Observational Retrospective Single Centre Cohort Study on Constructing & Validating AI Prediction Models for Outcomes of Lung Cancer Patients
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.
Read the 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.
- Conditions
- Lung Cancer
- Being tested
- Data collection
- Keywords
- AI, ML, Lung Cancer, Prediction
- Medical subject headings
- Lung Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Data collectionOther
retrospective data collection
What is being measured
The primary outcome is the main question the study is designed to answer.
Data into international common data model ready for AI input
Lung cancer hospital data translated and clinically validated in UMLS concepts and stored in the OMOP common data model
Measured: 2022
Secondary outcomes (3)
Supervised machine learning
Training and validating supervised machine learning models with a limited set of selected features as input to predict lung cancer patient outcomes.
Measured: 2023
Digital patient construction
Constructing a digital patient by training an AI model
Measured: 2023
Construction & validation of predictive AI model for lung cancer patients
Construction \& validation of predictive AI model feasibility approach; Constructing predicted outcome AI models like 30 day mortality, 30- day ER visit, QoL evolution, acute treatment complications
Measured: 2024
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted24 Mar 2023
- Study start27 Sep 2021
- Primary completion27 Sep 2024 (estimated)
- Study completion31 Dec 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Lung CancerStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- AZ Delta
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Ingel Demedts, MD, Principal investigator, AZ Delta
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05783024
- Sponsor's protocol ID
- B1172021000032
- Acronym
- ADAMpartlung
- Status last verified
- May 2023
- First submitted
- 13 Mar 2023
- Last updated
- 10 May 2023
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 10 May 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- ADAM Substudy Luik 2: Observational Retrospective Single Centre Cohort Study on Constructing & Validating AI Prediction Models for Outcomes of Lung Cancer Patients
- ClinicalTrials.gov ID
- NCT05783024
- Acronym / sponsor ID
- ADAMpartlung / B1172021000032
- Conditions
- Lung Cancer
- Keywords
- AI, ML, Lung Cancer, Prediction
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 500 participants (estimated)
- Lead sponsor
- AZ DeltaUniversity, hospital or other
- Study start
- 27 Sep 2021 (actual)
- Primary completion
- 27 Sep 2024 (estimated)
- Study completion
- 31 Dec 2024 (estimated)
- First posted
- 24 Mar 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Lung cancer patients included in the lung cancer patient pathway from 20/04/2018
Key inclusion criteria (1 listed)
- Lung cancer patients included in the lung cancer patient pathway
Key exclusion criteria (1 listed)
- None specified
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Data collectionretrospective data collection | Other | – |
Primary outcome
Data into international common data model ready for AI input
Time frame: 2022
Lung cancer hospital data translated and clinically validated in UMLS concepts and stored in the OMOP common data model
3 secondary outcomes
Supervised machine learning
Time frame: 2023
Digital patient construction
Time frame: 2023
Construction & validation of predictive AI model for lung cancer patients
Time frame: 2024
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 10 May 2023.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Ingel Demedts, MD contact
Ingel Demedts, MD principal investigator
AZ Delta
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Lung Cancer, run by AZ Delta at 1 site in Belgium.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 10 May 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Belgium. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Peter De Jaeger, PhD (003251237650, peter.dejaeger@azdelta.be), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Data into international common data model ready for AI input (time frame: 2022). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 10 May 2023.