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

University Hospital, Toulouse60 enrolled

Overview

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.

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.

Study Type

OBSERVATIONAL

Enrollment

Conditions

Non-small Cell Lung Cancer Stage IIIA

Interventions

Resected non small cell lung cancerOTHER

plasma sample, tissue sample and computed tomography scan images

Eligibility

Sex: ALLMin age: 18 Years

Medical Language ↔ Plain English

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

Outcomes

Primary Outcomes

Algorithm for disease free survival

Analysis on a training cohort of resected early-stage non small cell lung cancer

Time frame: 18 months