Advanced Data-Aided Medicine Part Lung Cancer (ADAMpartlung)

May 9, 2023 updated by: AZ Delta

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.

Study Overview

Status

Recruiting

Conditions

Lung Cancer

Intervention / Treatment

Other: Data collection

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 Type

Observational

Enrollment (Anticipated)

500

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Name: Peter De Jaeger, PhD
Phone Number: 003251237650
Email: peter.dejaeger@azdelta.be

Study Contact Backup

Name: Ingel Demedts, MD
Phone Number: 003251237111

Study Locations

Belgium
- West-Vlaanderen
  - Roeselare, West-Vlaanderen, Belgium, 8800
    - Recruiting
    - AZ Delta
    - Contact:
      
      Peter De Jaeger, PhD, Prof
      
      Phone Number: 003251237650
      
      Email: peter.dejaeger@azdelta.be
    - Principal Investigator:
      
      Ingel Demedts, Prof
    - Sub-Investigator:
      
      Hannelore Bode, MD
    - Sub-Investigator:
      
      Bernard Bouckaerts, MD
    - Sub-Investigator:
      
      Kris Carron, MD
    - Sub-Investigator:
      
      Stephanie Dobbelaere, MD
    - Sub-Investigator:
      
      Ulrike Himpe, MD
    - Sub-Investigator:
      
      Heidi Mariën, MD
    - Sub-Investigator:
      
      Peter Van Haecke, MD
    - Sub-Investigator:
      
      Wim Verbeke, MD
    - Sub-Investigator:
      
      Peter De Jaeger, Prof, PhD
    - Sub-Investigator:
      
      Pieter-Jan Lammertyn
    - Sub-Investigator:
      
      Louise Berteloot
    - Sub-Investigator:
      
      Kim Denturck, Msc ir

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

Child
Adult
Older Adult

Accepts Healthy Volunteers

Sampling Method

Non-Probability Sample

Study Population

Lung cancer patients included in the lung cancer patient pathway from 20/04/2018

Description

Inclusion Criteria:

Lung cancer patients included in the lung cancer patient pathway

Exclusion Criteria:

None specified

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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	2022

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Digital patient construction Time Frame: 2023	Constructing a digital patient by training an AI model	2023
Supervised machine learning Time Frame: 2023	Training and validating supervised machine learning models with a limited set of selected features as input to predict lung cancer patient outcomes.	2023
Construction & validation of predictive AI model for lung cancer patients Time Frame: 2024	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	2024

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

AZ Delta

Investigators

Principal Investigator: Ingel Demedts, MD, AZ Delta

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

September 27, 2021

Primary Completion (Anticipated)

September 27, 2024

Study Completion (Anticipated)

December 31, 2024

Study Registration Dates

First Submitted

March 13, 2023

First Submitted That Met QC Criteria