Towards automatic pulmonary nodule management in lung cancer screening with deep learning

The introduction of lung cancer screening programs will produce an unprecedented amount of chest CT scans in the near future, which radiologists will have to read in order to decide on a patient follow-up strategy. According to the current guidelines, the workup of screen-detected nodules strongly relies on nodule size and nodule type. In this paper, we present a deep learning system based on multi-stream multi-scale convolutional networks, which automatically classifies all nodule types relevant for nodule workup. The system processes raw CT data containing a nodule without the need for any additional information such as nodule segmentation or nodule size and learns a representation of 3D data by analyzing an arbitrary number of 2D views of a given nodule. The deep learning system was trained with data from the Italian MILD screening trial and validated on an independent set of data from the Danish DLCST screening trial. We analyze the advantage of processing nodules at multiple scales with a multi-stream convolutional network architecture, and we show that the proposed deep learning system achieves performance at classifying nodule type that surpasses the one of classical machine learning approaches and is within the inter-observer variability among four experienced human observers.Comment: Published on Scientific Report

Enhanced Immunogenicity, Mortality Protection, and Reduced Viral Brain Invasion by Alum Adjuvant with an H5N1 Split-Virion Vaccine in the Ferret

Pre-pandemic development of an inactivated, split-virion avian influenza vaccine is challenged by the lack of pre-existing immunity and the reduced immunogenicity of some H5 hemagglutinins compared to that of seasonal influenza vaccines. Identification of an acceptable effective adjuvant is needed to improve immunogenicity of a split-virion avian influenza vaccine.No serum antibodies were detected after vaccination with unadjuvanted vaccine, whereas alum-adjuvanted vaccination induced a robust antibody response. Survival after unadjuvanted dose regimens of 30 µg, 7.5 µg and 1.9 µg (21-day intervals) was 64%, 43%, and 43%, respectively, yet survivors experienced weight loss, fever and thrombocytopenia. Survival after unadjuvanted dose regimen of 22.5 µg (28-day intervals) was 0%, suggesting important differences in intervals in this model. In contrast to unadjuvanted survivors, either dose of alum-adjuvanted vaccine resulted in 93% survival with minimal morbidity and without fever or weight loss. The rarity of brain inflammation in alum-adjuvanted survivors, compared to high levels in unadjuvanted vaccine survivors, suggested that improved protection associated with the alum adjuvant was due to markedly reduced early viral invasion of the ferret brain.Alum adjuvant significantly improves efficacy of an H5N1 split-virion vaccine in the ferret model as measured by immunogenicity, mortality, morbidity, and brain invasion

A prospective, single-centre, randomised study evaluating the clinical, imaging and immunological depth of remission achieved by very early versus delayed Etanercept in patients with Rheumatoid Arthritis (VEDERA)

Background Rheumatoid arthritis (RA) is a chronic inflammatory arthritis, with significant impact on quality of life and functional status. Whilst biologic disease modifying anti-rheumatic drugs (bDMARD) such as tumour necrosis factor-inhibitor (TNFi) agents have revolutionised outcomes in RA, early diagnosis with immediate conventional therapy, titrated in a treat to target approach is also associated with high remission rates. The main aim of the VEDERA study (Very Early versus Delayed Etanercept in Rheumatoid Arthritis) is to assess the depth of remission, sustainability of remission and immunological normalisation induced by very early TNFi with etanercept (ETN) or standard of care +/- delayed ETN. Methods/Design VEDERA is a pragmatic, phase IV single-centre open-label randomised superiority trial of 120 patients with early, treatment-naive RA. Patients will be randomised 1:1 to first-line ETN and methotrexate (MTX) or MTX with additional synthetic disease modifying anti-rheumatic drugs (sDMARDs) according to a treat to target (TT) protocol with further step up to ETN and MTX after 24 weeks if remission is not achieved. Participants will have regular disease activity assessments and imaging evaluation including musculoskeletal ultrasound and MRI. The main objective of this study is to assess the proportion of patients with early RA that achieve clinical remission at 48 weeks, following either treatment strategy. In addition, the participants are invited to take part in a cardio-vascular sub-study (Coronary Artery Disease in RA, CADERA), which aims to identify the incidence of cardiovascular abnormalities in early RA. Discussion The hypothesis underlining this study is that very early treatment with first-line ETN increases the proportion of patients with rheumatoid arthritis achieving clinical remission, in comparison to conventional therapy. Trial registration NCT02433184, 23/04/201

Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box

In this paper, we tackle the problem of automatic classification of pulmonary peri-fissural nodules (PFNs). The classification problem is formulated as a machine learning approach, where detected nodule candidates are classified as PFNs or non-PFNs. Supervised learning is used, where a classifier is trained to label the detected nodule. The classification of the nodule in 3D is formulated as an ensemble of classifiers trained to recognize PFNs based on 2D views of the nodule. In order to describe nodule morphology in 2D views, we use the output of a pre-trained convolutional neural network known as OverFeat. We compare our approach with a recently presented descriptor of pulmonary nodule morphology, namely Bag of Frequencies, and illustrate the advantages offered by the two strategies, achieving performance of AUC = 0.868, which is close to the one of human experts

Observer variability for Lung-RADS categorisation of lung cancer screening CTs: impact on patient management

Objectives: Lung-RADS represents a categorical system published by the American College of Radiology to standardise management in lung cancer screening. The purpose of the study was to quantify how well readers agree in assigning Lung-RADS categories to screening CTs; secondary goals were to assess causes of disagreement and evaluate its impact on patient management. Methods: For the observer study, 80 baseline and 80 follow-up scans were randomly selected from the NLST trial covering all Lung-RADS categories in an equal distribution. Agreement of seven observers was analysed using Cohen’s kappa statistics. Discrepancies were correlated with patient management, test performance and diagnosis of malignancy within the scan year. Results: Pairwise interobserver agreement was substantial (mean kappa 0.67, 95% CI 0.58–0.77). Lung-RADS category disagreement was seen in approximately one-third (29%, 971) of 3360 reading pairs, resulting in different patient management in 8% (278/3360). Out of the 91 reading pairs that referred to scans with a tumour diagnosis within 1 year, discrepancies in only two would have resulted in a substantial management change. Conclusions: Assignment of lung cancer screening CT scans to Lung-RADS categories achieves substantial interobserver agreement. Impact of disagreement on categorisation of malignant nodules was low. Key Points: • Lung-RADS categorisation of low-dose lung screening CTs achieved substantial interobserver agreement. • Major cause for disagreement was assigning a different nodule as risk-dominant. • Disagreement led to a different follow-up time in 8% of reading pairs

Observer variability for Lung-RADS categorisation of lung cancer screening CTs: impact on patient management

Objectives: Lung-RADS represents a categorical system published by the American College of Radiology to standardise management in lung cancer screening. The purpose of the study was to quantify how well readers agree in assigning Lung-RADS categories to screening CTs; secondary goals were to assess causes of disagreement and evaluate its impact on patient management. Methods: For the observer study, 80 baseline and 80 follow-up scans were randomly selected from the NLST trial covering all Lung-RADS categories in an equal distribution. Agreement of seven observers was analysed using Cohen’s kappa statistics. Discrepancies were correlated with patient management, test performance and diagnosis of malignancy within the scan year. Results: Pairwise interobserver agreement was substantial (mean kappa 0.67, 95% CI 0.58–0.77). Lung-RADS category disagreement was seen in approximately one-third (29%, 971) of 3360 reading pairs, resulting in different patient management in 8% (278/3360). Out of the 91 reading pairs that referred to scans with a tumour diagnosis within 1 year, discrepancies in only two would have resulted in a substantial management change. Conclusions: Assignment of lung cancer screening CT scans to Lung-RADS categories achieves substantial interobserver agreement. Impact of disagreement on categorisation of malignant nodules was low. Key Points: • Lung-RADS categorisation of low-dose lung screening CTs achieved substantial interobserver agreement. • Major cause for disagreement was assigning a different nodule as risk-dominant. • Disagreement led to a different follow-up time in 8% of reading pairs

Assisted versus manual interpretation of low-dose CT scans for lung cancer screening: Impact on lung-RADS agreement

Purpose: To compare the inter-and intraobserver agreement and reading times achieved when assigning Lung Imaging Reporting and Data System (Lung-RADS) categories to baseline and follow-up lung cancer screening studies by using a dedicated CT lung screening viewer with integrated nodule detection and volumetric support with those achieved by using a standard picture archiving and communication system (PACS)-like viewer. Materials and Methods: Data were obtained from the National Lung Screening Trial (NLST). By using data recorded by NLST radiologists, scans were assigned to Lung-RADS categories. For each Lung-RADS category (1 or 2, 3, 4A, and 4B), 40 CT scans (20 baseline scans and 20 follow-up scans) were randomly selected for 160 participants (median age, 61 years; interquartile range, 58–66 years; 61 women) in total. Seven blinded observers independently read all CT scans twice in a randomized order with a 2-week washout period: once by using the standard PACS-like viewer and once by using the dedicated viewer. Observers were asked to assign a Lung-RADS category to each scan and indicate the risk-dominant nodule. Inter-and intraobserver agreement was analyzed by using Fleiss k values and Cohen weighted k values, respectively. Reading times were compared by using a Wilcoxon signed rank test. Results: The interobserver agreement was moderate for the standard viewer and substantial for the dedicated viewer, with Fleiss k values of 0.58 (95% CI: 0.55, 0.60) and 0.66 (95% CI: 0.64, 0.68), respectively. The intraobserver agreement was substantial, with a mean Cohen weighted k value of 0.67. The median reading time was significantly reduced from 160 seconds with the standard viewer to 86 seconds with the dedicated viewer (P, .001). Conclusion: Lung-RADS interobserver agreement increased from moderate to substantial when using the dedicated CT lung screening viewer. The median reading time was substantially reduced when scans were read by using the dedicated CT lung screening viewer

Detection and quantification of the solid component in pulmonary subsolid nodules by semiautomatic segmentation

Objective To determine whether semiautomatic volumetric software can differentiate part-solid from nonsolid pulmonary nodules and aid quantification of the solid component. Methods As per reference standard, 115 nodules were differentiated into nonsolid and part-solid by two radiologists; disagreements were adjudicated by a third radiologist. The diameters of solid components were measured manually. Semiautomatic volumetric measurements were used to identify and quantify a possible solid component, using different Hounsfield unit (HU) thresholds. The measurements were compared with the reference standard and manual measurements. Results The reference standard detected a solid component in 86 nodules. Diagnosis of a solid component by semiautomatic software depended on the threshold chosen. A threshold of -300 HU resulted in the detection of a solid component in 75 nodules with good sensitivity (90 %) and specificity (88 %). At a threshold of -130 HU, semiautomatic measurements of the diameter of the solid component (mean 2.4 mm, SD 2.7 mm) were comparable to manual measurements at the mediastinal window setting (mean 2.3 mm, SD 2.5 mm [p=0.63]). Conclusion Semiautomatic segmentation of subsolid nodules could diagnose part-solid nodules and quantify the solid component similar to human observers. Performance depends on the attenuation segmentation thresholds. This method may prove useful in managing subsolid nodules