The value of cell-free circulating tumour DNA profiling in advanced non-small cell lung cancer (NSCLC) management

Liquid biopsy (LB) has boosted a remarkable change in the management of cancer patients by contributing to tumour genomic profiling. Plasma circulating cell-free tumour DNA (ctDNA) is the most widely searched tumour-related element for clinical application. Specifically, for patients with lung cancer, LB has revealed valuable to detect the diversity of targetable genomic alterations and to detect and monitor the emergence of resistance mechanisms. Furthermore, its non-invasive nature helps to overcome the difficulty in obtaining tissue samples, offering a comprehensive view about tumour diversity. However, the use of the LB to support diagnostic and therapeutic decisions still needs further clarification. In this sense, this review aims to provide a critical view of the clinical importance of plasma ctDNA analysis, the most widely applied LB, and its limitations while anticipating concepts that will intersect the present and future of LB in non-small cell lung cancer patients

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

Management of Coronavirus Disease 2019 Patients With Lung Cancer: Experience From a Thoracic Oncology Center

Background: Cancer patients appear to be at a higher risk of complications from coronavirus disease 2019 (COVID-19). Specific data related to lung cancer (LC) patient management, active treatment, and/or recent diagnosis are still very limited. Here, we aimed to investigate the clinical presentation, baseline features, and clinical outcomes of LC patients with COVID-19. Methods: A retrospective case study was performed at Centro Hospitalar Universitário de São Joao, a tertiary hospital in the North of Portugal. Data from LC patients diagnosed with COVID-19 were collected during the first 10 months of the COVID-19 pandemic (March 2020–January 2021). Results: Twenty-eight patients with active LC were diagnosed with COVID-19, being adenocarcinoma the most common histological type present (n = 13, 46.4%). Sixteen patients had metastatic stage IV LC (61.5%). Twenty-five patients (89.3%) had relevant comorbidities including hypertension (39.3%) and chronic obstructive pulmonary disease (32.1%). For patients undergoing antineoplastic treatment, the median time from the last chemotherapy administration to COVID-19 diagnosis was of 16 days (interquartile range = 13–41 days). Half of patients were previously on corticosteroid therapy. Twenty patients (71.4%) needed hospitalization, 18 received oxygen therapy (64.3%), 3 (10.7%) of them received high-flow nasal cannula with good tolerability, and 1 (3.6%) needed non-invasive ventilation. Hydroxychloroquine and antibiotics were given to 4 (14.3%) and 12 (42.9%) patients, respectively. Seven patients (25%) died at a median time of 5 days following COVID-19 diagnosis. Conclusion: This is one of the first studies reporting the adverse outcomes associated with COVID-19 in LC patients at same time that adds evidence regarding the need to create protocols and guidelines to reduce the infection risk in such patients.NC-M acknowledges the Portuguese Foundation for Science and Technology under the Horizon 2020 Program (PTDC/PSI-GER/28076/2017)

Pre-training autoencoder for lung nodule malignancy assessment using CT images

Lung cancer late diagnosis has a large impact on the mortality rate numbers, leading to a very low five-year survival rate of 5%. This issue emphasises the importance of developing systems to support a diagnostic at earlier stages. Clinicians use Computed Tomography (CT) scans to assess the nodules and the likelihood of malignancy. Automatic solutions can help to make a faster and more accurate diagnosis, which is crucial for the early detection of lung cancer. Convolutional neural networks (CNN) based approaches have shown to provide a reliable feature extraction ability to detect the malignancy risk associated with pulmonary nodules. This type of approach requires a massive amount of data to model training, which usually represents a limitation in the biomedical field due to medical data privacy and security issues. Transfer learning (TL) methods have been widely explored in medical imaging applications, offering a solution to overcome problems related to the lack of training data publicly available. For the clinical annotations experts with a deep understanding of the complex physiological phenomena represented in the data are required, which represents a huge investment. In this direction, this work explored a TL method based on unsupervised learning achieved when training a Convolutional Autoencoder (CAE) using images in the same domain. For this, lung nodules from the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) were extracted and used to train a CAE. Then, the encoder part was transferred, and the malignancy risk was assessed in a binary classification—benign and malignant lung nodules, achieving an Area Under the Curve (AUC) value of 0.936. To evaluate the reliability of this TL approach, the same architecture was trained from scratch and achieved an AUC value of 0.928. The results reported in this comparison suggested that the feature learning achieved when reconstructing the input with an encoder-decoder based architecture can be considered an useful knowledge that might allow overcoming labelling constraints.This work is financed by National Funds through the Portuguese funding agency, FCT—Fundação para a Ciência e a Tecnologia within project UIDB/50014/2020

The Role of Liquid Biopsy in Early Diagnosis of Lung Cancer

Liquid biopsy is an emerging technology with a potential role in the screening and early detection of lung cancer. Several liquid biopsy-derived biomarkers have been identified and are currently under ongoing investigation. In this article, we review the available data on the use of circulating biomarkers for the early detection of lung cancer, focusing on the circulating tumor cells, circulating cell-free DNA, circulating micro-RNAs, tumor-derived exosomes, and tumor-educated platelets, providing an overview of future potential applicability in the clinical practice. While several biomarkers have shown exciting results, diagnostic performance and clinical applicability is still limited. The combination of different biomarkers, as well as their combination with other diagnostic tools show great promise, although further research is still required to define and validate the role of liquid biopsies in clinical practice.This work is financed by the ERDF—European Regional Development Fund through the Operational Programme for Competitiveness and Internationalization—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. Authors thank Abílio Cunha and Francisco Correia for the illustration work. NC-M acknowledges the Portuguese Foundation for Science and Technology under Horizon 2020 Program (PTDC/PSI-GER/28076/2017)

Assessment of the proportionality of skin folds between professional footballers and alternate, Peruvian

El objetivo del estudio fue proporcionar información descriptiva sobre las diferencias de proporcionalidad de la masa corporal y pliegues cutáneos entre jugadores titulares y reservas en función de la posición de juego. Fueron seleccionados de forma no probabilística 4 clubes profesionales de Primera División del Fútbol Profesional Peruano, considerándose 44 titulares y 28 reservas. Se evaluó la masa corporal, estatura, seis pliegues cutáneos, determinándose la proporcionalidad corporal por medio de la estrategia del Phantom. Los resultados muestran que la proporcionalidad de la masa corporal y los pliegues cutáneos es similar, tanto de forma general y por ubicación de juego, no existiendo diferencias significativas entre las posiciones de juego y entre titulares y suplentes. En conclusión, todos los jugadores, independientemente de la posición de juego y la titularidad y/o suplencia muestran similares valores de proporcionalidad corporal y de pliegues cutáneos, mostrando en general altos valores de masa corporal y bajos niveles de tejido adiposo.The aim of the study was to provide descriptive information about the differences in proportionality of body mass and skinfold thickness between starters and reserves as playing position. They were selected in a non-probabilistic 04 professional clubs in the First Division of Professional Football Peruvian, considering 44 starters and 28 reserves. We assessed body mass, height, six skinfolds, body proportionality determined by the strategy of the Phantom. The results show that the proportionality of body mass and skinfold is similar, both generally and by playing position, with no significant differences between playing positions and titular or alternate players. In conclusion, all players regardless of playing position and title and/or substitution proportionality show similar values and skinfold body, showing generally high body mass values and low levels of fat.Peer Reviewe

The MOVECLIM – AZORES project: Bryophytes from Pico Island along an elevation gradient

Background In September 2012, a comprehensive survey of Pico Island was conducted along an elevational transect, starting at Manhenha (10 m a.s.l.) and culminating at the Pico Mountain caldera (2200 m a.s.l.). The primary objective was to systematically inventory the bryophytes inhabiting the best-preserved areas of native vegetation environments. Twelve sites were selected, each spaced at 200 m elevation intervals. Within each site, two 10 m x 10 m plots were established in close proximity (10-15 m apart). Within these plots, three 2 m x 2 m quadrats were randomly selected and sampled for bryophytes using microplots measuring 10 cm x 5 cm, which were then collected into paper bags. Six substrates were surveyed in each quadrat: rock, soil, humus, organic matter, tree bark and leaves/fronds. Three replicates were obtained from all substrates available and colonised by bryophytes, resulting in a maximum of 18 microplots per quadrat, 54 microplots per plot, 108 microplots per site, and a total of 1296 microplots across the 12 sites on Pico Island. New information Two-thirds of the maximum expected number of microplots (n = 878; 67.75%) were successfully collected, yielding a total of 4896 specimens. The vast majority (n = 4869) were identified at the species/subspecies level. The study identified a total of 70 moss and 71 liverwort species or subspecies. Elevation levels between 600-1000 m a.s.l., particularly in the native forest plots, exhibited both a higher number of microplots and greater species richness. This research significantly enhanced our understanding of Azorean bryophyte diversity and distribution, contributing valuable insights at both local and regional scales. Notably, two new taxa for the Azores were documented during the MOVECLIM study, namely the pleurocarpous mosses Antitrichia curtipendula and Isothecium interludens.info:eu-repo/semantics/publishedVersio

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

Loci identificados através de estudo de associação genômica ampla e risco de câncer do pulmão: existe algo mais?

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