학술논문
Integrating artificial intelligence into lung cancer screening: a randomised controlled trial protocol
Document Type
article
Author
Paul Hofman; Sylvie Leroy; Jonathan Benzaquen; Bernard Padovani; Charles Hugo Marquette; Fontas Eric; Eric Fontas; Stephanie Lopez; Nesrine Rouis; Jacques Boutros; Allegra Maryline; Amamou-Elhani Faten; ARFI Thierry; Baque Jean; Baque-Juston Marie; Barel Remy; Barrios Baretto Deisy; Baudin Guillaume; Beck Camille; Bellmann Laurent; Benchetrit Maxime; Benkirane Mohamed-Taib; Benyoussef Sid Ali; Benzaquen Jonathan; Berthet Jean Philippe; Bonnard Eric; Bordone Olivier; Boutros Jacques; Boyer Guy-René; Bulsei Julie; Caillon Cynthia; Castelnau Olivier; Chalmin Jérémy; Chebib Ralph; Cohen Charlotte; Cruzel Coralie; Degoutte Aurélien; Delin Margot; Diascorn Yann; Doux Nathalie; Durand Lorraine; Duval Yannick; El Hemweh Omar; Fayada Julien; Felderhoof Eric; Feliciello Stéphane:; Femenia Richard; Ferrari Victoria; Francisci Marc Paul; Ghalloussi Hannah; Gomez-Caro-Andres Abel; Gora Assia; Griffonnet Jennifer; Gubeno Marie Christine; Guigay Joël; Hamila Marame; Harrathi Mohamed-Ali; Henaut Quentin; Herin Edouard; Hofman Paul; Hofman Véronique; ILIE Marius; Korzeniewski Sylvia; Lalvee Salomé; Lassalle Sandra; Le Heron Charles; Leray Loïc; Leriche Julien; Lerousseau Lionel; Leroy Sylvie; Lespinet Fabre Virginie; Lestrez Roxane; Leyssalle Axelle; Long Mira Elodie; Lopez Stephanie; Mahler Valentin; Maniel Charlotte; Marcano Xavier; Marco Roucayrol Sabine; Marquette Charles-Hugo; Martin Nicolas; Mistri Aurélie; Nicolle Isabelle; Novellas Sébastien; Oddo Frédéric; Otto Josiane; Padovani Bernard; PERQUIS Marie Pierre; Philibert Lorène; Pop Daniel; Pottier Héloïse; Raguin Olivier; Rolland Fabien; Rouis Nesrine; Rousset Johanna; Ruitort Frédéric; Sanfiorenzo Céline; Selva Eric; Tanga Virginie; Tardy Magalie; Thomas Olivier; Varenio Sophie; Verdoire Paul; Vigny Isabelle; Washetine Kévin; Zurlinden Olivier; Tarhini Adam; Perrotin Cédric
Source
BMJ Open, Vol 14, Iss 2 (2024)
Subject
Language
English
ISSN
2044-6055
Abstract
Introduction Lung cancer (LC) is the most common cause of cancer-related deaths worldwide. Its early detection can be achieved with a CT scan. Two large randomised trials proved the efficacy of low-dose CT (LDCT)-based lung cancer screening (LCS) in high-risk populations. The decrease in specific mortality is 20%–25%.Nonetheless, implementing LCS on a large scale faces obstacles due to the low number of thoracic radiologists and CT scans available for the eligible population and the high frequency of false-positive screening results and the long period of indeterminacy of nodules that can reach up to 24 months, which is a source of prolonged anxiety and multiple costly examinations with possible side effects.Deep learning, an artificial intelligence solution has shown promising results in retrospective trials detecting lung nodules and characterising them. However, until now no prospective studies have demonstrated their importance in a real-life setting.Methods and analysis This open-label randomised controlled study focuses on LCS for patients aged 50–80 years, who smoked more than 20 pack-years, whether active or quit smoking less than 15 years ago. Its objective is to determine whether assisting a multidisciplinary team (MDT) with a 3D convolutional network-based analysis of screening chest CT scans accelerates the definitive classification of nodules into malignant or benign. 2722 patients will be included with the aim to demonstrate a 3-month reduction in the delay between lung nodule detection and its definitive classification into benign or malignant.Ethics and dissemination The sponsor of this study is the University Hospital of Nice. The study was approved for France by the ethical committee CPP (Comités de Protection des Personnes) Sud-Ouest et outre-mer III (No. 2022-A01543-40) and the Agence Nationale du Medicament et des produits de Santé (Ministry of Health) in December 2023. The findings of the trial will be disseminated through peer-reviewed journals and national and international conference presentations.Trial registration number NCT05704920.