The importance of endobag use with incision-protective devices in VATS lung resection: a preliminary study.

Objective: The advent of new techniques such as video-assisted thoracoscopic surgery (VATS) for the removal of lung segments leads to compression of the surgical specimen, with the possible dissemination of neoplastic cells. The sheer volume of surgeries performed using these techniques has caused many institutions to stop removing the surgical specimen using an endobag, even when retractors/protectors are used in the instrumentalization incision. This study aimed to collect data from patients undergoing lung resection by VATS and analyze the cytopathological results of the collected material.

Automatic method for classifying COVID-19 patients based on chest X-ray images, using deep features and PSO-optimized XGBoost.

The COVID-19 pandemic, which originated in December 2019 in the city of Wuhan, China, continues to have a devastating effect on the health and well-being of the global population. Currently, approximately 8.8 million people have already been infected and more than 465,740 people have died worldwide.

An emergency system for monitoring pulse oximetry, peak expiratory flow, and body temperature of patients with COVID-19 at home: Development and preliminary application.

Background: COVID-19 is characterized by a rapid change in the patient's condition, with major changes occurring over a few days. We aimed to develop and evaluate an emergency system for monitoring patients with COVID-19, which may be useful in hospitals where more severe patients stay in their homes.

Methodology/principal Findings: The system consists of the home-based patient unit, which is set up around the patient and the hospital unit, which enables the medical staff to telemonitor the patient's condition and help to send medical recommendations.

Solid Indeterminate Nodules with a Radiological Stability Suggesting Benignity: A Texture Analysis of Computed Tomography Images Based on the Kurtosis and Skewness of the Nodule Volume Density Histogram.

Background: The number of incidental findings of pulmonary nodules using imaging methods to diagnose other thoracic or extrathoracic conditions has increased, suggesting the need for in-depth radiological image analyses to identify nodule type and avoid unnecessary invasive procedures.

Objectives: The present study evaluated solid indeterminate nodules with a radiological stability suggesting benignity (SINRSBs) through a texture analysis of computed tomography (CT) images.

Methods: A total of 100 chest CT scans were evaluated, including 50 cases of SINRSBs and 50 cases of malignant nodules.

Computer-Aided Diagnosis of Lung Nodules in Computed Tomography by Using Phylogenetic Diversity, Genetic Algorithm, and SVM.

Lung cancer is pointed as the major cause of death among patients with cancer throughout the world. This work is intended to develop a methodology for diagnosis of lung nodules using images from the Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI). The proposed methodology uses image processing and pattern recognition techniques.

Statistical tools for the temporal analysis and classification of lung lesions.

Background And Objective: Lung cancer remains one of the most common cancers globally. Temporal evaluation is an important tool for analyzing the malignant behavior of lesions during treatment, or of indeterminate lesions that may be benign. This work proposes a methodology for the analysis, quantification, and visualization of small (local) and large (global) changes in lung lesions.

3D shape analysis to reduce false positives for lung nodule detection systems.

Using images from the Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), we developed a methodology for classifying lung nodules. The proposed methodology uses image processing and pattern recognition techniques. To classify volumes of interest into nodules and non-nodules, we used shape measurements only, analyzing their shape using shape diagrams, proportion measurements, and a cylinder-based analysis.

Computer-aided diagnosis system for lung nodules based on computed tomography using shape analysis, a genetic algorithm, and SVM.

Lung cancer is the major cause of death among patients with cancer worldwide. This work is intended to develop a methodology for the diagnosis of lung nodules using images from the Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI). The proposed methodology uses image processing and pattern recognition techniques.

Voxel-based comparative analysis of lung lesions in CT for therapeutic purposes.

Lung cancer remains as one of the most incident types of cancer throughout the world. Temporal evaluation has become a very useful tool when one wishes to analyze some malignancy-indicating behavior. The objective of the present work is to detect changes in the local densities of lung lesions over time (follow-up analysis).

The assessment of telemedicine to support outpatient palliative care in advanced cancer.

Objective: We aimed to examine telemedicine as a form of home and additional support for traditional outpatient care as a way to remotely monitor and manage the symptoms of patients with advanced cancer.

Method: In total, 12 patients were monitored through monthly consultations with a multidisciplinary healthcare team and weekly web conferences. To evaluate and treat pain and other symptoms, the Edmonton Symptom Assessment System (ESAS) was applied during all remote or in-person interviews.

Lung cancer with chest wall invasion: retrospective analysis comparing en-bloc resection and 'resection in bird cage'.

Background: Invasion of the chest wall per se is not a contraindication for tumor resection in non-small cell lung cancer (NSCLC), provided there is no mediastinal lymph node or vital structure involvement. Although widely known to Brazilian surgeons, the 'resection in bird cage' technique has never been widely studied in terms of patient survival. Thus, the objective of this study was to evaluate the postoperative consequences and overall survival of extra-musculoperiosteal resection compared with en-bloc resection in NSCLC patients with invasion of the endothoracic fascia.

Automatic detection of solitary lung nodules using quality threshold clustering, genetic algorithm and diversity index.

Objective: The present work has the objective of developing an automatic methodology for the detection of lung nodules.

Methodology: The proposed methodology is based on image processing and pattern recognition techniques and can be summarized in three stages. In the first stage, the extraction and reconstruction of the pulmonary parenchyma is carried out and then enhanced to highlight its structures.

Royal sun medicinal mushroom Agaricus brasiliensis (higher Basidiomycetes) and the attenuation of pulmonary inflammation induced by 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK).

Agaricus brasiliensis currently is one of the most studied fungi because of its nutritional and therapeutic properties as an anti-inflammatory agent and an adjuvant in cancer chemotherapy. The effects of orally administered aqueous A. brasiliensis extract (14.

The surprising outcome of a giant primary mediastinal synovial sarcoma treated with neoadjuvant chemotherapy.

There are only a few cases of primary mediastinal synovial sarcoma in the literature. Normally, they do not respond well to chemotherapy. In our case, a 30-year-old patient was admitted due to thoracic pain, dyspnea, orthopnea, cough, hoarseness and weight loss over a 3-month period as well as a dramatic worsening a week before the admission.

Relationship between the phase angle and volume of tumours in patients with lung cancer.

Background/aims: The phase angle (PA) obtained by bioelectrical impedance has been used as a predictor of nutritional status in cancer. This study aimed to verify the association between the PA and tumour volume in non-small cell lung cancer (NSCLC) patients.

Methods: Volumetric determination of the tumour mass was performed using a computerised image analysis system incorporated in helical tomography.

Automatic segmentation of lung nodules with growing neural gas and support vector machine.

Lung cancer is distinguished by presenting one of the highest incidences and one of the highest rates of mortality among all other types of cancer. Unfortunately, this disease is often diagnosed late, affecting the treatment outcome. In order to help specialists in the search and identification of lung nodules in tomographic images, many research centers have developed computer-aided detection systems (CAD systems) to automate procedures.

Results and complications of CT-guided transthoracic fine-needle aspiration biopsy of pulmonary lesions.

Objective: To analyze the cytological findings of CT-guided percutaneous fine-needle aspiration biopsies of the lung, to demonstrate the diagnostic feasibility of the method in the investigation of pulmonary lesions, and to determine the complications of the procedure, evaluating its safety.

Methods: A retrospective analysis of 89 patients with various types of pulmonary lesions who underwent 97 procedures over a period of five years. The patients were divided into groups regarding the indication for the procedure: suspicion of primary lung cancer (stages IIIB or IV); suspicion of lung cancer (stages I, II, or IIIA) and clinical contraindications for surgery; suspicion of pulmonary metastasis from other organs; and pulmonary lesions with benign radiological aspect.

Methodology for automatic detection of lung nodules in computerized tomography images.

Lung cancer is a disease with significant prevalence in several countries around the world. Its difficult treatment and rapid progression make the mortality rates among people affected by this illness to be very high. Aiming to offer a computational alternative for helping in detection of nodules, serving as a second opinion to the specialists, this work proposes a totally automatic methodology based on successive detection refining stages.

Diagnosis of solitary lung nodules using the local form of Ripley's K function applied to three-dimensional CT data.

This paper analyzes the application of Ripley's K function to characterize lung nodules as malignant or benign in computerized tomography images. The proposed characterization method is based on a selection of measures from Ripley's K function to discriminate between benign and malignant nodules, using stepwise discriminant analysis. Based on the selected measures, a linear discriminant analysis procedure is performed once again in order to predict the classification of each nodule.