1,721,244 research outputs found
Valutazione con ecografia del carcinoma del canale anale prima e dopo trattamento conservativo: Un caso
No full textA novel methodology for head and neck carcinoma treatment stage detection by means of model checking
No full textContext: Head and neck cancers are diagnosed at an annual rate of 3% to 7% with respect to the total number of cancers, and 50% to 75% of such new tumours occur in the upper aerodigestive tract. Purpose: In this paper we propose formal methods based approach aimed to identify the head and neck tumour treatment stage by means of model checking. We exploit a set of radiomic features to model medical imaging as a labelled transition system to verify treatment stage properties.Main findings: We experiment the proposed method using a public dataset related to computed tomography images obtained in different treatment stages, reaching an accuracy ranging from 0.924 to 0.978 in treatment stage detection.Principal conclusions: The study confirms the effectiveness of the adoption of formal methods in the head and neck carcinoma treatment stage detection to support radiologists and pathologists
A Neural Network-Based Method for Respiratory Sound Analysis and Lung Disease Detection
No full textBackground: Respiratory sound analysis represents a research topic of growing interest in recent times. In fact, in this area, there is the potential to automatically infer the abnormalities in the preliminary stages of a lung dysfunction. Methods: In this paper, we propose a method to analyse respiratory sounds in an automatic way. The aim is to show the effectiveness of machine learning techniques in respiratory sound analysis. A feature vector is gathered directly from breath audio and, thus, by exploiting supervised machine learning techniques, we detect if the feature vector is related to a patient affected by a lung disease. Moreover, the proposed method is able to characterise the lung disease in asthma, bronchiectasis, bronchiolitis, chronic obstructive pulmonary disease, pneumonia, and lower or upper respiratory tract infection. Results: A retrospective experimental analysis on 126 patients with 920 recording sessions showed the effectiveness of the proposed method. Conclusion: The experimental analysis demonstrated that it is possible to detect lung disease by exploiting machine learning techniques. We considered several supervised machine learning algorithms, obtaining the most interesting performance with the neural network model, with an F-Measure of 0.983 in lung disease detection and equal to 0.923 in lung disease characterisation, increasing the state-of-the-art performance
Lung Cancer Detection and Characterisation through Genomic and Radiomic Biomarkers
No full textMedical image bio-markers of cancer are expected to improve patient care through advances in precision medicine. Compared to genomic bio-markers, bio-markers obtained directly from medical images provide the advantages of being a non-invasive procedure, and characterizing a heterogeneous tumor in its entirety, as opposed to limited tissue available for biopsy. In this paper, with the aim to demonstrate that non-invasive features can obtain better performances if compared to invasive ones in lung cancer detection and characterisation, we propose a method to discriminate between different lung cancers (i.e., Adenocarcinoma and Squamous Cell Carcinoma) by adopting both invasive (genomic) and non-invasive (radiomic) bio-markers, by building supervised machine learning models exploiting both invasive and non-invasive features. Experiments on a data-set of 130 patients show that radiomic bio-markers obtain better performances (with an f-measure equal to 0.993) if compared to the ones obtained by considering genomic ones (reaching an f-measure equal to 0.929) in lung cancer detection and characterisation
Coinfections with influenza virus and atypical bacteria: Implications for severe outcomes?
No full textTossicità e malattie metaboliche acquisite
No full textLe patologie neurologiche acute da danno tossicometabolico rappresentano una considerevole percentuale della complessa attività dei reparti di terapia intensiva. Tale attività tende al raggiungimento di alcuni obiettivi: caratterizzare l’estensione e la natura della disfunzione cerebrale; determinare le modalità del monitoraggio; istituire appropriate terapie di recupero cerebrale. Le malattie tossico-metaboliche acquisite del Sistema Nervoso Centrale (SNC) sono solitamente patologie dell’età adulta; esse sono dovute all’esposizione, esogena o endogena, a prodotti metabolici tossici in concentrazioni elevate nel sangue, con accumulo nel SNC [1,2]. Tali malattie possono determinare danno principale a carico della sostanza grigia, con coinvolgimento dei nuclei della base e del tronco encefalico, senza o con coinvolgimento corticale; oppure a carico della sostanza bianca, di solito meno evidente rispetto alle malattie metaboliche ereditarie [1,3]. Lo studio neuroradiologico rappresenta un indispensabile complemento ai dati clinici e fisiologici nella valutazione diagnostica e prognostica dei pazienti con disfunzione neurologica acuta. Esso consente una valutazione diretta della struttura cerebrale, fornendo inoltre importanti informazioni sulla fisiopatologia e sulla storia naturale della disfunzione cerebrale acuta. Grazie a tali possibilità, lo studio neuroradiologico può consentire una diagnosi precoce e una conseguente tempestiva strategia terapeutica, al fine di evitare danni a livello cerebrale irreversibili o letali; può fornire inoltre informazioni predittive sugli esiti a lungo termine; consente, infine, di monitorare nel tempo l’efficacia della terapia e l’evolutività del quadro neuroradiologico
CT and MR imaging of the thoracic aorta
Full text linkAt present time, both CT and MRI are valuable techniques in the study of the thoracic aorta. Nowadays, CT represents the most widely employed technique for the study of the thoracic aorta. The new generation CTs show sensitivities up to 100% and specificities of 98-99%. Sixteen and wider row detectors provide isotropic pixels, mandatory for the ineludible longitudinal reconstruction. The main limits are related to the X-ray dose expoure and the use of iodinated contrast media. MRI has great potential in the study of the thoracic aorta. Nevertheless, if compared to CT, acquisition times remain longer and movement artifact susceptibility higher. The main MRI disadvantages are claustrophobia, presence of ferromagnetic implants, pacemakers, longer acquisition times with respect to CT, inability to use contrast media in cases of renal insufficiency, lower spatial resolution and less availability than CT. CT is preferred in the acute aortic disease. Nevertheless, since it requires iodinated contrast media and X-ray exposure, it may be adequately replaced by MRI in the follow up of aortic diseases. The main limitation of MRI, however, is related to the scarce visibility of stents and calcifications
Diagnostic imaging and intervention of the musculoskeletal system: state of the art
No full textThe study of the musculoskeletal system has always been one of the most important application for diagnostic imaging in radiology (1-4)
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