A Machine Learning Approach to Identify Patients With Resected Non-small-cell Lung Cancer With High Risk of Relapse
- Conditions
- Non-small Cell Lung Cancer Stage IIIA
- Interventions
- Other: Resected non small cell lung cancer
- Registration Number
- NCT05732974
- Lead Sponsor
- University Hospital, Toulouse
- 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.
Recruitment & Eligibility
- Status
- RECRUITING
- Sex
- All
- Target Recruitment
- 60
- 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
- Patient with another cancer in the last 5 years
- Patient with an allergy to the contrast medium
- Patient under legal protection
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Arm && Interventions
Group Intervention Description Resected early stage non small cell lung cancer Resected non small cell lung cancer Early stage (IA-IIIA) resectable non small cell lung cancer patients receiving surgery
- Primary Outcome Measures
Name Time Method Algorithm for disease free survival 18 months Analysis on a training cohort of resected early-stage non small cell lung cancer
- Secondary Outcome Measures
Name Time Method
Trial Locations
- Locations (1)
Julien MAZIERES
🇫🇷Toulouse, France