Integrating artificial intelligence into lung cancer screening: a randomised controlled trial protocol.

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.

Experimental ventilator-associated pneumonia: distribution of lung infection and consequences for lung aeration

Ventilator-associated pneumonia (VAP) has been described in humans and in experimental animals. The most severe lesions are located in dependent lung segments along a sterno-vertebral axis, however the cephalocaudal distribution of lung infection remains unknown. We used an experimental model to evaluate the distribution of lung infection, considering its anteroposterior and cephalocaudal gradient, and its impact on lung aeration. Ten healthy domestic piglets were anesthetized, paralyzed and mechanically ventilated for 59 hours in the prone position. At the end of the experiment they were sacrificed and their lungs were fixed. Six segments were analyzed: a non-dependant (ND) and a dependant (D) segment of the upper (UL), middle (ML) and lower (LL) lobes. The presence of healthy lung or of histological infectious lesions was analyzed with a semi-quantitative method. The regional distribution of lung infection was compared between upper, middle and lower lobes, as well as between dependant and non-dependant regions. The presence of infectious lesions was correlated with measurements of lung aeration. Nine of the ten piglets developed VAP. Infectious lesions were distributed along a sterno-vertebral and a cephalocaudal gradient; the lower and middle lobes were more frequently infected than the upper lobes. There was an inverse correlation (R= - 0.902) between the development of lung lesions and lung aeration. In conclusion, VAP was a frequent complication in healthy mechanically ventilated piglets, showing an anteroposterior as well as a cephalocaudal gradient. As expected, development of lung infection was accompanied by a corresponding loss of aeration