4 research outputs found
Percutaneous lung needle biopsies : utility and complications in various chest lesions : a single-institution experience
Purpose: It is crucial to obtain a specific diagnosis before treatment of chest pathology is initiated. The purpose of the study is to present the utility of percutaneous biopsies, core and fine-needle aspiration, in various thoracic lesions, and related complications. Material and methods: A total of 593 transthoracic biopsies were performed in the Department of Radiology between 2013 and 2016. Fine-needle aspiration biopsy (FNAB) and core biopsy (CB) were implemented. The procedures were divided into four groups according to the location of the pathology: lung lesions (LL - 540), mediastinal masses (MM - 25), chest wall tumours (CWT - 13), and pleural lesions (PL - 15). The lung lesion group was divided into two subgroups: lung nodules and lung infiltrations. All groups were analysed in respect of diagnostic accuracy, pathological findings, and complication rate. Results: Pathological diagnosis was confirmed in 447 cases after all 593 procedures. The sensitivity of malignancy diagnosis in the group of lung tumours was 75% for FNAB and 89% for CB. The sensitivity in other groups, where CB was a preferable technique, was counted for lung infiltration, mediastinal masses, chest wall tumours, and pleural lesions and amounted to 83.3%, 90.9%, 100%, and 85.7%, respectively. In the group of lung tumours malignancy was confirmed most commonly (79%), while in the lung infiltration group benign processes dominated (83%). There was no statistical difference between the pneumothorax rate after CB and FNAB. Haemoptysis appeared more often after CB. Conclusions: FNAB and CB are useful, safe, and sensitive tools in the diagnostic work-up. They can both be used to diagnose almost all chest pathologies
Combining low-dose CT-based radiomics and metabolomics for early lung cancer screening support
Due to its predominantly asymptomatic or mildly symptomatic progression, lung
cancer is often diagnosed in advanced stages, resulting in poorer survival
rates for patients. As with other cancers, early detection significantly
improves the chances of successful treatment. Early diagnosis can be
facilitated through screening programs designed to detect lung tissue tumors
when they are still small, typically around 3mm in size. However, the analysis
of extensive screening program data is hampered by limited access to medical
experts. In this study, we developed a procedure for identifying potential
malignant neoplastic lesions within lung parenchyma. The system leverages
machine learning (ML) techniques applied to two types of measurements: low-dose
Computed Tomography-based radiomics and metabolomics. Using data from two
Polish screening programs, two ML algorithms were tested, along with various
integration methods, to create a final model that combines both modalities to
support lung cancer screening
BRONCO: Automated modelling of the bronchovascular bundle using the Computed Tomography Images
Segmentation of the bronchovascular bundle within the lung parenchyma is a
key step for the proper analysis and planning of many pulmonary diseases. It
might also be considered the preprocessing step when the goal is to segment the
nodules from the lung parenchyma. We propose a segmentation pipeline for the
bronchovascular bundle based on the Computed Tomography images, returning
either binary or labelled masks of vessels and bronchi situated in the lung
parenchyma. The method consists of two modules, modeling of the bronchial tree
and vessels. The core revolves around a similar pipeline, the determination of
the initial perimeter by the GMM method, skeletonization, and hierarchical
analysis of the created graph. We tested our method on both low-dose CT and
standard-dose CT, with various pathologies, reconstructed with various slice
thicknesses, and acquired from various machines. We conclude that the method is
invariant with respect to the origin and parameters of the CT series. Our
pipeline is best suited for studies with healthy patients, patients with lung
nodules, and patients with emphysema