Integrative and comparative genomic analyses identify clinically relevant pulmonary carcinoid groups and unveil the supra-carcinoids

International audienceThe worldwide incidence of pulmonary carcinoids is increasing, but little is known about their molecular characteristics. Through machine learning and multi-omics factor analysis, we compare and contrast the genomic profiles of 116 pulmonary carcinoids (including 35 atypical), 75 large-cell neuroendocrine carcinomas (LCNEC), and 66 small-cell lung cancers. Here we report that the integrative analyses on 257 lung neuroendocrine neoplasms stratify atypical carcinoids into two prognostic groups with a 10-year overall survival of 88% and 27%, respectively. We identify therapeutically relevant molecular groups of pulmonary car-cinoids, suggesting DLL3 and the immune system as candidate therapeutic targets; we confirm the value of OTP expression levels for the prognosis and diagnosis of these diseases, and we unveil the group of supra-carcinoids. This group comprises samples with carcinoid-like morphology yet the molecular and clinical features of the deadly LCNEC, further supporting the previously proposed molecular link between the low-and high-grade lung neuroendocrine neoplasms

Extracorporeal Membrane Oxygenation for Severe Acute Respiratory Distress Syndrome associated with COVID-19: An Emulated Target Trial Analysis.

RATIONALE: Whether COVID patients may benefit from extracorporeal membrane oxygenation (ECMO) compared with conventional invasive mechanical ventilation (IMV) remains unknown. OBJECTIVES: To estimate the effect of ECMO on 90-Day mortality vs IMV only Methods: Among 4,244 critically ill adult patients with COVID-19 included in a multicenter cohort study, we emulated a target trial comparing the treatment strategies of initiating ECMO vs. no ECMO within 7 days of IMV in patients with severe acute respiratory distress syndrome (PaO2/FiO2 <80 or PaCO2 ≥60 mmHg). We controlled for confounding using a multivariable Cox model based on predefined variables. MAIN RESULTS: 1,235 patients met the full eligibility criteria for the emulated trial, among whom 164 patients initiated ECMO. The ECMO strategy had a higher survival probability at Day-7 from the onset of eligibility criteria (87% vs 83%, risk difference: 4%, 95% CI 0;9%) which decreased during follow-up (survival at Day-90: 63% vs 65%, risk difference: -2%, 95% CI -10;5%). However, ECMO was associated with higher survival when performed in high-volume ECMO centers or in regions where a specific ECMO network organization was set up to handle high demand, and when initiated within the first 4 days of MV and in profoundly hypoxemic patients. CONCLUSIONS: In an emulated trial based on a nationwide COVID-19 cohort, we found differential survival over time of an ECMO compared with a no-ECMO strategy. However, ECMO was consistently associated with better outcomes when performed in high-volume centers and in regions with ECMO capacities specifically organized to handle high demand. This article is open access and distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Réseaux de neurones convolutifs en médecine nucléaire : applications à la segmentation automatique des tumeurs gliales et à la correction d’atténuation en TEP/IRM

Convolutional neural networks group together a set of architectures whose elementary units are inspired by biological neurons. They are able to estimate and extract internal representations (filters) by learning from annotated data. They are then convoluted with our images to classify or predict. Our objective through two examples is to try to better understand the potential of the latter in nuclear medicine and to understand their limits. We will first study a segmentation procedure that can be assimilated to a classification problematic where we try to predict for each voxel a category. For this we will rely on a cohort of 37 patients with glial tumors explored within initial staging by 18-F-Fluoro-ethyl-thyrosine PET. In a second time, we will focus on estimating CT scans from ZTE MRI sequences and to evaluate its impact on attenuation correction during tomographic reconstruction in 47 patients referred for neurodegenerative diseases.Les réseaux de neurones convolutifs regroupent un ensemble d’architectures dont les unités élémentaires sont inspirées des neurones biologiques. Ils permettent d’estimer et d’extraire par un ensemble de techniques d’apprentissage, des représentations internes (filtres) qui sont ensuite convoluées avec nos images pour classer, prédire... Notre objectif au travers de deux exemples est de tenter de mieux comprendre les potentialités de ces derniers en médecine nucléaire et d’en appréhender les limites. Nous étudierons donc dans un premier temps une procédure de segmentation qui peut être assimilée à un problème de classification où l’on cherche à prédire pour chaque voxel une catégorie. Pour cela nous nous appuierons sur une cohorte de 37 patients avec des tumeurs gliales explorées dans leur bilan initial en TEP à la 18-F-Fluoro-ethyl-thyrosine. Dans un second temps nous nous intéresserons à estimer des TDM à partir de la séquence IRM ZTE et d’évaluer son impact sur la correction d’atténuation lors de la reconstruction tomographique chez 47 patients adressés dans le cadre de pathologies neuro-dégénératives

Learning curves with DSC over epochs (upper panel) and loss over epochs (lower panel).

<p>Learning curves with DSC over epochs (upper panel) and loss over epochs (lower panel).</p

Architecture of the fully connected CNN with a 3 layer “U-Net” architecture.

<p>Architecture of the fully connected CNN with a 3 layer “U-Net” architecture.</p

Confusion matrix at the voxel level in the validation set of 11 patients.

<p>Confusion matrix at the voxel level in the validation set of 11 patients.</p

Segmentation examples with for each example: Original resized slices ¹⁸F-FET (top), original mask (middle up), predicted mask + threshold (middle down) and difference between the predicted and original mask (bottom, with false negative in red and false positive in blue).

<p>Segmentation examples with for each example: Original resized slices ¹⁸F-FET (top), original mask (middle up), predicted mask + threshold (middle down) and difference between the predicted and original mask (bottom, with false negative in red and false positive in blue).</p

‘Just Because You Teach, Doesn’t Mean It’s Over’:Bunheads and the Pedagogy of Live Performance

International audienc