Skip to content

A Machine Learning Approach to Identify Patients With Resected Non-small-cell Lung Cancer With High Risk of Relapse

A Machine Learning Approach to Identify Patients With Resected Non-small-cell Lung Cancer With High Risk of Relapse

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT05732974
Acronym
MIRACLE
Enrollment
60
Registered
2023-02-17
Start date
2023-03-30
Completion date
2026-10-30
Last updated
2023-09-21

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Non-small Cell Lung Cancer Stage IIIA

Keywords

Machine-learning, Resected early stage Non-small Cell Lung Cancer

Brief summary

Early-stage non small cell lung cancer represents 20-30% of all non small cell lung cancer and is characterized by a high survival probability after surgical resection. However, considering stage IA-IIIA non small cell lung cancer, a relapse rate of about 50% is observed, with a different survival probability on the basis of tumor node metastasis status, although patients within the same tumor node metastasis stage exhibit wide variations in recurrence rate. There are currently no validated prognostic biomarkers able to identify patients with a high risk of relapse.

Detailed description

This study will use data from an already available cohort of patients enrolled in the Resting study (a project funded by TRANSCAN in 2018) as a training set and data from a new concurrent cohort as validation set.

Interventions

OTHERResected non small cell lung cancer

plasma sample, tissue sample and computed tomography scan images

Sponsors

University Hospital, Toulouse
Lead SponsorOTHER

Study design

Observational model
COHORT
Time perspective
PROSPECTIVE

Eligibility

Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No

Inclusion criteria

* Patient with an early stage of non small cell lung cancer * Indication of surgical resection * Patient able to understand and give his consent * Patient affiliated to the health insurance

Exclusion criteria

* Patient with another cancer in the last 5 years * Patient with an allergy to the contrast medium * Patient under legal protection

Design outcomes

Primary

MeasureTime frameDescription
Algorithm for disease free survival18 monthsAnalysis on a training cohort of resected early-stage non small cell lung cancer

Countries

France

Contacts

Primary ContactJulien MAZIERES, MD, PhD
mazieres.j@chu-toulouse.fr0567771837

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026