Bronchiectasis: A retrospective study of clinical and aetiological investigation in a general respiratory department

AbstractBackgroundBronchiectasis can result from many diseases, which makes the aetiological investigation a complex process demanding special resources and experience. The aetiological diagnosis has been proven to be useful for the therapeutic approach.ObjectiveEvaluate how accurately and extensive the clinical and aetiological research was for adult bronchiectasis patients in pulmonology outpatient service which were not following a pre-existing protocol.MethodsWe retrospectively reviewed the records of 202 adult patients with bronchiectasis, including the examinations performed to explain the aetiology.ResultsThe mean age of the patients was 54±15 years, there was a predominance of female (63.9%) and non-smoker (70%) patients. Functional evaluation showed a mild airway obstruction.The sputum microbiological examination was available for 168 patients (43.1% had 3 or more sputum examinations during one year). Immunoglobulins and α1-antitrypsin were measured in around 50% of the patients. The sweat test and the CF genotyping test were performed in 18% and 17% of the patients, respectively.The most commonly identified cause was post-infectious (30.3%), mostly tuberculosis (27.2%). No definitive aetiological diagnosis was established in 57.4% of the patients. We achieved a lower aetiological diagnosis if we compare our series with studies in which a diagnostic algorithm was applied prospectively.ConclusionsThe general characteristics of our patients were similar with other series. Detailed investigation of bronchiectasis is not a standard practice in our outpatient service. These results suggest that the use of a predefined protocol, based on current guidelines, could improve the assessment of these patients and facilitate the achievement of a definitive aetiology

Machine learning and feature selection methods for egfr mutation status prediction in lung cancer

The evolution of personalized medicine has changed the therapeutic strategy from classical chemotherapy and radiotherapy to a genetic modification targeted therapy, and although biopsy is the traditional method to genetically characterize lung cancer tumor, it is an invasive and painful procedure for the patient. Nodule image features extracted from computed tomography (CT) scans have been used to create machine learning models that predict gene mutation status in a noninvasive, fast, and easy-to-use manner. However, recent studies have shown that radiomic features extracted from an extended region of interest (ROI) beyond the tumor, might be more relevant to predict the mutation status in lung cancer, and consequently may be used to significantly decrease the mortality rate of patients battling this condition. In this work, we investigated the relation between image phenotypes and the mutation status of Epidermal Growth Factor Receptor (EGFR), the most frequently mutated gene in lung cancer with several approved targeted-therapies, using radiomic features extracted from the lung containing the nodule. A variety of linear, nonlinear, and ensemble predictive classification models, along with several feature selection methods, were used to classify the binary outcome of wild-type or mutant EGFR mutation status. The results show that a comprehensive approach using a ROI that included the lung with nodule can capture relevant information and successfully predict the EGFR mutation status with increased performance compared to local nodule analyses. Linear Support Vector Machine, Elastic Net, and Logistic Regression, combined with the Principal Component Analysis feature selection method implemented with 70% of variance in the feature set, were the best-performing classifiers, reaching Area Under the Curve (AUC) values ranging from 0.725 to 0.737. This approach that exploits a holistic analysis indicates that information from more extensive regions of the lung containing the nodule allows a more complete lung cancer characterization and should be considered in future radiogenomic studies.This work is financed by the ERDF—European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation—COMPETE 2020 Programme and by National Funds through the Portuguese funding agency, FCT—Fundação para a Ciência e a Tecnologia within project POCI-01-0145-FEDER-030263

Comprehensive perspective for lung cancer characterisation based on AI solutions using CT images

Lung cancer is still the leading cause of cancer death in the world. For this reason, novel approaches for early and more accurate diagnosis are needed. Computer-aided decision (CAD) can be an interesting option for a noninvasive tumour characterisation based on thoracic computed tomography (CT) image analysis. Until now, radiomics have been focused on tumour features analysis, and have not considered the information on other lung structures that can have relevant features for tumour genotype classification, especially for epidermal growth factor receptor (EGFR), which is the mutation with the most successful targeted therapies. With this perspective paper, we aim to explore a comprehensive analysis of the need to combine the information from tumours with other lung structures for the next generation of CADs, which could create a high impact on targeted therapies and personalised medicine. The forthcoming artificial intelligence (AI)-based approaches for lung cancer assessment should be able to make a holistic analysis, capturing information from pathological processes involved in cancer development. The powerful and interpretable AI models allow us to identify novel biomarkers of cancer development, contributing to new insights about the pathological processes, and making a more accurate diagnosis to help in the treatment plan selection.This work is financed by the ERDF–European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation–COMPETE 2020 Programme and by National Funds through the Portuguese funding agency, FCT–Fundação para a Ciência e a Tecnologia within project POCI-01-0145-FEDER-030263

Impact of montelukast as add on treatment to the novel coronavirus pneumonia (COVID-19): protocol for an investigator-initiated open labeled randomized controlled pragmatic trial

Background: Montelukast, a safe drug widely use in asthmatic patients, may be an adjuvant in the treatment of Covid-19, either by improving lung injury and inflammation, or by acting as an anti-viral drug. We aim to assess the efficacy and safety of montelukast as add-on treatment in patients with Covid-19. Methods: We propose a randomized, controlled, parallel, open-label trial involving 160 hospitalized adult patients with confirmed Covid-19. Patients will be randomly assigned in a 1:1 ratio to receive either montelukast 10 mg, once a day for 14 days, in addition to standard of care (SoC), or SoC alone. SoC will follow the best practice for treating these patients, according to updated recommendations. The primary outcome is time to recovery. Participants will be assessed using diary cards to capture data on treatment-related improvements in an 8-point ordinal scale. Secondary endpoints will include changes in respiratory and inflammatory parameters, and adverse events. This phase IV clinical trial will take place at the University Hospital of São João, Porto. EudraCT number: 2020-001747-21. Results: This study intends to generate scientific evidence on efficacy and safety of montelukast as add-on treatment in Covid-19. The results will be essential to improve clinical outcomes which remains to be determined. Conclusion: Montelukast has been suggested as a potential drug with 2 main actions on Covid-19. The validation of montelukast as an adjuvant treatment may improve lung injury, inflammation, and symptoms leading to a better prognosis. The use of this drug may fulfil the existing gap on therapeutic options

EGFR Assessment in Lung Cancer CT Images: Analysis of Local and Holistic Regions of Interest Using Deep Unsupervised Transfer Learning

Statistics have demonstrated that one of the main factors responsible for the high mortality rate related to lung cancer is the late diagnosis. Precision medicine practices have shown advances in the individualized treatment according to the genetic profile of each patient, providing better control on cancer response. Medical imaging offers valuable information with an extensive perspective of the cancer, opening opportunities to explore the imaging manifestations associated with the tumor genotype in a non-invasive way. This work aims to study the relevance of physiological features captured from Computed Tomography images, using three different 2D regions of interest to assess the Epidermal growth factor receptor (EGFR) mutation status: nodule, lung containing the main nodule, and both lungs. A Convolutional Autoencoder was developed for the reconstruction of the input image. Thereafter, the encoder block was used as a feature extractor, stacking a classifier on top to assess the EGFR mutation status. Results showed that extending the analysis beyond the local nodule allowed the capture of more relevant information, suggesting the presence of useful biomarkers using the lung with nodule region of interest, which allowed to obtain the best prediction ability. This comparative study represents an innovative approach for gene mutations status assessment, contributing to the discussion on the extent of pathological phenomena associated with cancer development, and its contribution to more accurate Artificial Intelligence-based solutions, and constituting, to the best of our knowledge, the first deep learning approach that explores a comprehensive analysis for the EGFR mutation status classification.The authors acknowledge the National Cancer Institute and the Foundation for the National Institutes of Health for the free publicly available LIDC-IDRI Database used in this work. They also acknowledge The Cancer Imaging Archive (TCIA) for the open-access NSCLC-Radiogenomics dataset publicly available. This work was supported in part by the European Regional Development Fund (ERDF) through the Operational Program for Competitiveness and Internationalization—COMPETE 2020 Program, and in part by the National Funds through the Portuguese Funding Agency, Fundação para a Ciência e a Tecnologia (FCT), under Project POCI-01-0145-FEDER-030263