Department of Pulmonary Medicine and Thoracic Oncology, FHU OncoAge, IHU RespirERA, Centre Hospitalier Universitaire de Nice, Nice, France.
Laboratory of Clinical and Experimental Pathology, FHU OncoAge, IHU RespirERA, Universite Cote d'Azur, Centre hospitalier Universitaire de Nice, Nice, France.
BMJ Open. 2024 Feb 13;14(2):e074680. doi: 10.1136/bmjopen-2023-074680.
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.
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.
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.
NCT05704920.
肺癌（LC）是全球癌症相关死亡的最常见原因。通过 CT 扫描可以早期发现肺癌。两项大型随机试验证明了低剂量 CT（LDCT）为基础的肺癌筛查（LCS）在高危人群中的有效性。特异性死亡率降低 20%-25%。然而，由于适合筛查的人群中胸放射科医生和 CT 扫描数量有限，假阳性筛查结果频率较高，以及结节的不确定性长达 24 个月（导致焦虑时间延长，需要进行多次可能有副作用的昂贵检查），大规模实施 LCS 面临障碍。深度学习作为一种人工智能解决方案，在回顾性试验中检测和描述肺结节方面显示出了良好的效果。然而，到目前为止，还没有前瞻性研究证明其在现实环境中的重要性。
这项开放标签的随机对照研究侧重于 50-80 岁、吸烟超过 20 包年、无论是否仍在吸烟或戒烟时间不足 15 年的人群的 LCS。其目的是确定在多学科团队（MDT）中使用基于 3D 卷积网络的筛查胸部 CT 扫描分析是否能加速将结节明确分类为恶性或良性。将纳入 2722 名患者，目标是证明在肺结节检测和明确分类为良性或恶性之间的时间延迟减少 3 个月。
该研究的赞助商是尼斯大学医院。该研究于 2023 年 12 月获得法国南部和海外医学伦理委员会 CPP（保护个人委员会）（No. 2022-A01543-40）和法国国家药品和保健品管理局（卫生部）的批准。该试验的结果将通过同行评审期刊和国家及国际会议报告进行传播。
NCT05704920。
