1,721,176 research outputs found
Editorial: Colorectal cancer awareness month 2023: diagnosis, clinical course, and surgical management of metastatic colorectal cancer
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Computed Tomographic Detection of Toothpick Perforation of the Jejunum: Case Report and Review of the Literature
No full textAbstractForeign body ingestion is commonly encountered in the emergency department. Although in most cases, the ingested object will pass uneventfully in the feces [1], ingestion of sharp foreign bodies such as dental plates, sewing needles, toothpicks, fish bones and chicken bones carries increased risk of gastrointestinal perforation [2–4].The use of toothpicks as both tooth-clearing implements and eating utensils increase the likelihood of toothpick unintentional ingestion [5].Toothpicks account for 9% of reported foreign bodies ingested [6]. These pointed wooden bodies when accidentally swallowed are associated with higher risk of complications, such as gastric, small bowel or colonic perforation, obstruction, colonic impaction, gastrointestinal bleeding, subphrenic abscess, fistula formation, sepsis and/or death due to the damaged caused by the sharp pointed ends [7–9].Unfortunately, many patients who ingested such objects fail to remember the mis-swallowing event when symptoms of perforation develop, making diagnosis problematic.We present a case of jejunal perforation secondary to an ingested wooden toothpick correctly diagnosed with Computed Tomography (CT)
Pneumoretroperitoneum Associated with “Dirty Mass”: An Unusual Case of Rectal Perforation
No full textAbstractPerforation of the rectum requires early recognition and treatment. The diagnosis of rectal perforation is sometimes difficult owing to non specific clinical presentation, especially in elderly patients, in whom, in case of acute abdomen, Computed Tomography (CT) is increasing used as first diagnostic procedure [1]. Several CT signs of gastrointestinal perforation have been described [2, 3]. Recently another CT finding related to colonic perforation called “ dirty mass” has been reported [4]. We present a case of extraperitoneal rectal perforation secondary to colonoscopy in which CT demonstrated the presence of a focal collection of extraluminal fecal matter (“dirty mass”) associated with pneumoretroperitoneum
A 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
Prevalence of asymptomatic deep vein thrombosis in patients with inflammatory bowel diseases in the ambulatory surgery setting
No full textIntroduction: Patients suffering from inflammatory bowel disease (IBD) are reported at higher risk of venous thromboembolism (VTE). This is relevant in IBD patients scheduled for surgery. We aimed to seek for differences in the prevalence of asymptomatic lower extremity deep venous thrombosis (DVT) in IBD patients observed in outpatient surgery setting compared with controls. Methods: All consecutive patients diagnosed with IBD observed in outpatient setting between December 2013 and June 2014 were prospectively included. A sex, age, and gender matched cohort of non- IBD patients served as control group. All patients underwent clinical examination and ultrasound (US) assessment of their lower extremity venous vascular system performed by a clinician blind to patient diagnosis. Results: A total of 40 IBD patients and 40 controls agreed to participate. One IBD patient and one control were found with nonocclusive chronic DVT. No differences were observed in valvular incompetence between the two groups. Neither acute DVT nor severe venous incompetence were observed. Surgery was only performed in one control. Conclusion: Our data show that patients with IBD in remission are not at higher risk of either asymptomatic DVT or venous insufficiency compared with general population, suggesting that the higher risk of VTE events may rely on complex inflammatory mechanisms related with immune response. Screening asymptomatic IBD patients for DVT showed no advantages, suggesting that routine control in ambulatory surgery units is not warranted
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
Correction: Thymic hyperplasia is accurate to detect new-onset Graves' hyperthyroidism and resolves after restoring euthyroidism
No full textIn the original version of this article, the author’s name Reginelli Alfonso was incorrectly written as Regginelli Alfonso
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