Advanced Data-Aided Medicine Part Lung Cancer (ADAMpartlung)

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

• 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

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: No

• 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

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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	2022

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Digital patient construction	Constructing a digital patient by training an AI model	2023
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.	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	2024

Collaborators and Investigators

• Sponsor: AZ Delta
• Investigators: Principal Investigator: Ingel Demedts, MD, AZ Delta

Study record dates

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: March 13, 2023
• First Posted (Actual): March 24, 2023

Study Record Updates

• Last Update Posted (Actual): May 10, 2023
• Last Update Submitted That Met QC Criteria: May 9, 2023
• Last Verified: May 1, 2023

More Information

Terms related to this study

• Keywords: Lung Cancer; AI; Prediction; ML
• Additional Relevant MeSH Terms: Respiratory Tract Diseases; Neoplasms; Lung Diseases; Neoplasms by Site; Respiratory Tract Neoplasms; Thoracic Neoplasms; Lung Neoplasms
• Other Study ID Numbers: B1172021000032

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No