Nasopharyngeal Masses in Adults

1983 ◽  
Vol 92 (2) ◽  
pp. 137-140 ◽  
Author(s):  
Steven B. Hopping ◽  
Max L. Goodman ◽  
Jeanne D. Keller ◽  
William W. Montgomery
Keyword(s):  
Computed Tomography ◽  
Conventional Radiography ◽  
Benign Lesions ◽  
Cervical Mass ◽  
Computed Tomography Scans ◽  
Benign Histology ◽  
Malignant Lesions ◽  
One Year ◽  
High Index Of Suspicion ◽  
Nasopharyngeal Masses

Nasopharyngeal masses in adults present a perplexing problem because of the concern for malignancy. A retrospective review of the records, radiographs and biopsy histology of 57 patients presenting in one year with nasopharyngeal masses at the Massachusetts Eye and Ear Infirmary suggests that persistent pain, epistaxis, trismus, or cervical mass are uncommon in patients with benign histology and should alert the clinician to the probability of malignancy. A high index of suspicion on the part of the clinician is essential for the successful recognition of early lesions. The roles of conventional radiography, polytomography, and computed tomography scans are discussed. Patients with malignant lesions are contrasted with those having benign lesions.

Use of Artificial Intelligence Deep Learning to Determine the Malignant Potential of Pancreatic Cystic Neoplasms With Preoperative Computed Tomography Imaging

2020 ◽  
pp. 000313482095377
Author(s):  
Michael D. Watson ◽  
William B. Lyman ◽  
Michael J. Passeri ◽  
Keith J. Murphy ◽  
John P. Sarantou ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Learning Model ◽  
Consensus Guidelines ◽  
Benign Lesions ◽  
Cystic Neoplasms ◽  
Advanced Neoplasia ◽  
Malignant Lesions ◽  
Pancreatic Cystic Neoplasms ◽  
Deep Learning Model

Background Society consensus guidelines are commonly used to guide management of pancreatic cystic neoplasms (PCNs). However, downsides of these guidelines include unnecessary surgery and missed malignancy. The aim of this study was to use computed tomography (CT)-guided deep learning techniques to predict malignancy of PCNs. Materials and Methods Patients with PCNs who underwent resection were retrospectively reviewed. Axial images of the mucinous cystic neoplasms were collected and based on final pathology were assigned a binary outcome of advanced neoplasia or benign. Advanced neoplasia was defined as adenocarcinoma or intraductal papillary mucinous neoplasm with high-grade dysplasia. A convolutional neural network (CNN) deep learning model was trained on 66% of images, and this trained model was used to test 33% of images. Predictions from the deep learning model were compared to Fukuoka guidelines. Results Twenty-seven patients met the inclusion criteria, with 18 used for training and 9 for model testing. The trained deep learning model correctly predicted 3 of 3 malignant lesions and 5 of 6 benign lesions. Fukuoka guidelines correctly classified 2 of 3 malignant lesions as high risk and 4 of 6 benign lesions as worrisome. Following deep learning model predictions would have avoided 1 missed malignancy and 1 unnecessary operation. Discussion In this pilot study, a deep learning model correctly classified 8 of 9 PCNs and performed better than consensus guidelines. Deep learning can be used to predict malignancy of PCNs; however, further model improvements are necessary before clinical use.

Computed Tomography of Pleural Lesions with Special Reference to the Mediastinal Pleura

1986 ◽  
Vol 27 (5) ◽  
pp. 527-531 ◽  
Author(s):  
O. Salonen ◽  
L. Kivisaari ◽  
C.-G. Standertskjöld-Nordenstam ◽  
K. Somer ◽  
K. Mattson ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Contrast Medium ◽  
Contrast Enhancement ◽  
Bolus Injection ◽  
High Contrast ◽  
Benign Lesions ◽  
Pleural Diseases ◽  
Malignant Lesions ◽  
Mediastinal Pleura

Computed tomography (CT) and conventional chest radiography were reviewed in retrospect in 84 patients in whom pleural pathology was suggested clinically. The importance of administration of contrast medium in distinguishing between malignant lesions and other pleural diseases was given special attention. CT was found to be of value in diagnosing and establishing the extent of pleural lesions, especially in the mediastinal region. All malignancies exhibited high contrast enhancement following intravenous bolus injection of contrast medium. There were no features specific for mesotheliomas distinguishing these from other pleural malignancies. Some benign infectious lesions also showed high contrast enhancement. High contrast enhancement thus indicated malignancy only if an infectious lesion could be excluded. If contrast enhancement was absent or slight, the lesion was likely to be benign. Clinical follow-up may be regarded as justified in such cases. Indirect signs were not helpful in distinguishing between malignant and benign lesions.

scholarly journals Morgagni-Larrey parasternal diaphragmatic hernia masquerading as pleural effusion: an interesting presentation with review of literature

2020 ◽  
Vol 7 (7) ◽  
pp. 2450
Author(s):  
Amit Gupta ◽  
Sudhir Kumar Singh ◽  
Anshuman Darbari ◽  
Durga Sowmya S. ◽  
Rishit Mani
Keyword(s):  
Computed Tomography ◽  
Pleural Effusion ◽  
Congenital Diaphragmatic Hernia ◽  
Diaphragmatic Hernia ◽  
Clinical History ◽  
Major Malformation ◽  
Review Of Literature ◽  
X Ray ◽  
Computed Tomography Scans ◽  
High Index Of Suspicion

Congenital diaphragmatic hernia (CDH) is a major malformation found in newborns and babies but rarely in adults. CDH is defined by the presence of a defect in the diaphragm, which permits the herniation of abdominal contents into the thorax. The diagnosis of CDH is based on clinical history, examinations and is confirmed by plain X-ray films and computed tomography scans. Here we report a case which was misdiagnosed at another centre as pleural effusion and managed by placement of intercostal tube drainage and later on diagnosed to be case of Morgnani-Larrey parasternal diaphragmatic hernia. Congenital diaphragmatic hernia (CDH) is a major malformation found in newborns and babies but rarely in adults. CDH is defined by the presence of a defect in the diaphragm, which permits the herniation of abdominal contents into the thorax. The diagnosis of CDH is based on clinical history, examinations and is confirmed by plain X-ray films and computed tomography scans. The purpose of reporting this case is to provide information on late-presenting CDH and to emphasize that a high index of suspicion is required for diagnosis and to avoid unwanted complications.

Computed Tomography Reveals Atypical Localization of Benign Mediastinal Tumors

1989 ◽  
Vol 30 (2) ◽  
pp. 175-179 ◽  
Author(s):  
J. Ikezoe ◽  
S. Sone ◽  
S. Morimoto ◽  
S. Takashima ◽  
J. Arisawa ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Bronchogenic Cyst ◽  
Malignant Lesion ◽  
Benign Lesions ◽  
Mediastinal Tumors ◽  
Computed Tomographic ◽  
Malignant Lesions ◽  
Longitudinal Extension ◽  
Esophageal Duplication ◽  
Atypical Localization

Computed tomographic appearances of 147 surgically proven mediastinal tumors (113 benign and 34 malignant lesions) were evaluated paying special attention to their localization or extension. Ten of the 113 benign lesions (9%) showed atypical or unique localization or extension, and were divided into three groups. In the first group, two lymphangiomas and one bronchogenic cyst spread over both the precardiovascular and the retrocardiovascular compartments, mimicking a malignant lesion. In the second group, the single cases of thymic hypertrophy, thymic cyst, schwannoma, and pericardial cyst were located atypically in the retrocardiovascular compartment. In the last group, two esophageal leiomyomas and one esophageal duplication showed upward and downward longitudinal extension beyond the azygos arch and the aortic arch.

Computed Tomography Angiography (CTA) Evaluation of Spontaneous Subarachnoid Hemorrhage

2020 ◽  
Vol 6 (2) ◽  
pp. 78-81
Author(s):  
Md Tauhidul Islam Chowdhury ◽  
Mohammad Shah Jahirul Hoque Chowdhury ◽  
Mohammad Sadekur Rahman Sarkar ◽  
KM Ahasan Ahmed ◽  
Md Nazmul Kabir ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Subarachnoid Hemorrhage ◽  
Computed Tomography Angiography ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Cross Sectional ◽  
Multiple Aneurysm ◽  
Traumatic Subarachnoid Hemorrhage ◽  
One Year ◽  
Spontaneous Subarachnoid Hemorrhage

Background: In evaluation of non-traumatic subarachnoid hemorrhage CT angiography (CTA) has 97-98% sensitivity and near 100% specificity. Objective: This study was conducted to evaluate the CTA findings of CT positive non traumatic subarachnoid hemorrhage. Methodology: This is an observational cross sectional study performed in Neurology department of National Institute of Neurosciences and Hospital, Dhaka over one year period (January 2019 to December 2019). Total 87 CT positive subarachnoid hemorrhage cases were purposively included in this study. All CT positive patients underwent CTA of Cerebral vessels for further evaluation. The angiography were evaluated by competent neuro-radiologists blinded about the study. Result: Among 87 patients, 40.2% were male and 59.8% were female. The average age was 53.33±11.1 years. Among the studied patient the source of bleeding was found 78.16% aneurysmal and 21.84% non-aneurysmal. 85.30% patients had single aneurysm and 14.70% had multiple aneurysm. The highest number of patient had Acom aneurysm (41.17%) followed by MCA (22.05%), ICA (13.23%), ACA (7.35%) and vertebral artery (1.14%) in order of frequency. Among the multiple aneurysm group most of the patients had combination of Acom, MCA and ICA aneurysm. Conclusion: From this study, we can conclude that CTA can be used as the primary diagnostic tool in evaluation of spontaneous SAH. Journal of National Institute of Neurosciences Bangladesh, 2020;6(2): 78-81

scholarly journals Deep Learning based Staging of Bone Lesions from Computed Tomography Scans (January 2021)

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Samira Masoudi ◽  
Sherif Mehralivand ◽  
Stephanie A. Harmon ◽  
Nathan Lay ◽  
Liza Lindenberg ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Bone Lesions ◽  
Computed Tomography Scans

scholarly journals Shared and unique metabolic features of the malignant and benign thyroid lesions determined with use of 1H HR MAS NMR spectroscopy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Agnieszka Skorupa ◽  
Mateusz Ciszek ◽  
Ewa Chmielik ◽  
Łukasz Boguszewicz ◽  
Małgorzata Oczko-Wojciechowska ◽  
...  
Keyword(s):  
Nmr Spectra ◽  
Univariate Analysis ◽  
Mas Nmr ◽  
Metabolic Reprogramming ◽  
Benign Lesions ◽  
Chronic Thyroiditis ◽  
Malignant Lesions ◽  
Metabolic Heterogeneity ◽  
Benign Thyroid ◽  
Thyroid Lesions

AbstractThe purpose of this work was to investigate the distinct and common metabolic features of the malignant and benign thyroid lesions in reference to the non-transformed tissue from the contralateral gland (chronic thyroiditis and colloid goiter). 1H HR MAS NMR spectra of 38 malignant lesions, 32 benign lesions and 112 samples from the non-tumoral tissue (32 from chronic thyroiditis and 80 samples from colloid goiter) were subjected both to multivariate and univariate analysis. The increased succinate, glutamine, glutathione, serine/cysteine, ascorbate, lactate, taurine, threonine, glycine, phosphocholine/glycerophosphocholine and decreased lipids were found in both lesion types in comparison to either colloid goiter or chronic thyroiditis. The elevated glutamate and choline, and reduced citrate and glucose were additionally evident in these lesions in reference to goiter, while the increased myo-inositol—in comparison to thyroiditis. The malignant lesions were characterized by the higher alanine and lysine levels than colloid goiter and thyroiditis, while scyllo-inositol was uniquely increased in the benign lesions (not in cancer) in comparison to both non-tumoral tissue types. Moreover, the benign lesions presented with the unique increase of choline in reference to thyroiditis (not observed in the cancerous tissue). The metabolic heterogeneity of the non-tumoral tissue should be considered in the analysis of metabolic reprogramming in the thyroid lesions.

Sign in / Sign up

Export Citation Format

Share Document