Advanced Data-Aided Medicine Part Lung Cancer

NCT05783024 | Unknown DMC

• 1 other identifier: B1172021000032
• Study Type: observational
• Target: 500
• Locations: 1 country
• Sites: 1

Brief 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.

Conditions

Lung Cancer

Keywords

AI; ML; Lung Cancer; Prediction

Outcome Measures

Primary Outcomes (1)

• 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 Outcomes (3)

• Supervised machine learning

  2023

• Digital patient construction

  2023

• Construction & validation of predictive AI model for lung cancer patients

  2024

Interventions

Data collection OTHER

retrospective data collection

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

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

You may qualify if:

• Lung cancer patients included in the lung cancer patient pathway

You may not qualify if:

• None specified

Sponsors & Collaborators

• AZ Delta: lead

Study Sites (1)

AZ Delta

Roeselare, West-Vlaanderen, 8800, Belgium

RECRUITING

MeSH Terms

Conditions

Lung Neoplasms

Interventions

Data Collection

Condition Hierarchy (Ancestors)

Respiratory Tract Neoplasms; Thoracic Neoplasms; Neoplasms by Site; Neoplasms; Lung Diseases; Respiratory Tract Diseases

Intervention Hierarchy (Ancestors)

Epidemiologic Methods; Investigative Techniques; Health Care Evaluation Mechanisms; Quality of Health Care; Health Care Quality, Access, and Evaluation; Public Health; Environment and Public Health

Study Officials

• Ingel Demedts, MD

  AZ Delta

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Peter De Jaeger, PhD

CONTACT

003251237650; peter.dejaeger@azdelta.be

Ingel Demedts, MD

CONTACT

003251237111

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: March 13, 2023
• First Posted: March 24, 2023
• Study Start: September 27, 2021
• Primary Completion: September 27, 2024
• Study Completion: December 31, 2024
• Last Updated: May 10, 2023

Record last verified: 2023-05

Data Sharing

• IPD Sharing: Will not share

Locations

BE (1)