Objectives: The objective of this study was to evaluate head and neck masses with real-time elastography to differentiate malignant masses.
Materials And Methods: Between January 2011 and December 2012, a total of 51 patients with a palpable mass in the neck region were included in this study. Excisional histopathologic data were compared with elastographic results and Doppler ultrasonography.
Results: The study group comprised 27 males (52.9%) and 24 females (47.1%) (mean [SD] age, 41.47 [19.59] y; range, 4-80 y). Fourteen masses were malignant (27.5%) and 37 were benign (72.5%). Comparing the elastographic results of benign and malignant masses, elastographic scores of the malignant masses were significantly higher than those of the benign masses (P < 0.005).The elastographic scores were divided into 2 groups: 34 (91.9%) patients with the diagnosis of benign mass had the score of 1 to 2, whereas 9 (64.3%) patients with the diagnosis of malignant cases had the score of 3 to 4. There was a significant difference between the groups (P < 0.005).However, 2 squamous cell carcinomas (28.6%) and 1 lymphoma (8.1%) were diagnosed with an elastographic score of 1 (Table 2). This showed that even an elastographic score of 1 was not enough to issue a diagnosis of benign masses.
Conclusions: With improvements in the device and increased experience, this modality can become a useful tool for the routine use. However, this modality cannot be used for screening to merely provide additional information about the nature of the masses.
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http://dx.doi.org/10.1097/SCS.0000000000001009 | DOI Listing |
Kardiochir Torakochirurgia Pol
December 2024
Endoscopic and Minimally Invasive Surgery Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
Introduction: Chest wall tumors, though rare, represent a significant subset of thoracic neoplasms, accounting for approximately 5% of thoracic and 2% of overall body neoplasms. Their management has historically posed challenges for surgeons, often leading to misdiagnosis, incomplete resection, and high complication rates. An individualized surgical approach, tailored to the specific characteristics of the disease, is crucial for optimizing outcomes.
View Article and Find Full Text PDFBiomed Eng Lett
January 2025
Electronics and Communication Engineering, IFET College of Engineering, Villupuram, Tamilnadu India.
Unlabelled: Breast cancer (BC) remains a significant global health issue, necessitating innovative methodologies to improve early detection and diagnosis. Despite the existence of intelligent deep learning models, their efficacy is often limited due to the oversight of small-sized masses, leading to false positive and false negative outcomes. This research introduces a novel segmentation-guided classification model developed to increase BC detection accuracy.
View Article and Find Full Text PDFInt Forum Allergy Rhinol
January 2025
Department of Otolaryngology - Head and Neck Surgery, Stanford University School of Medicine, Stanford, California, USA.
Background: We developed and assessed the performance of a machine learning model (MLM) to identify, classify, and segment sinonasal masses based on endoscopic appearance.
Methods: A convolutional neural network-based model was constructed from nasal endoscopy images from patients evaluated at an otolaryngology center between 2013 and 2024. Images were classified into four groups: normal endoscopy, nasal polyps, benign, and malignant tumors.
Zhongguo Yi Xue Ke Xue Yuan Xue Bao
December 2024
Department of Radiology,The Central Hospital of Wuhan,Tongji Medical College, Huazhong University of Science and Technology,Wuhan 430014,China.
Objective To assess the value of the MRI-based ovarian-adnexal reporting and data system (O-RADS MRI) for the diagnosis of adnexal masses. Methods A total of 407 patients who underwent dynamic contrast enhancement (DCE)-MRI and pathological examination (gold standard) at the Department of Radiology,Central Hospital of Wuhan between May 2017 and December 2022 were enrolled in this study.Two radiologists performed the O-RADS MRI scoring of adnexal masses according to MRI features and calculated the malignancy rate of adnexal masses by O-RADS MRI score,enhancement type,and mass type.
View Article and Find Full Text PDFAJR Am J Roentgenol
January 2025
Associate Professor, University of Ottawa Department of Radiology. Clinical Epidemiology Program, Ottawa Hospital Research Institute. Room c159 Ottawa Hospital Civic Campus, 1053 Carling Ave. Ottawa, ON, K1Y 4E9.
Bosniak classification version 2019 (v2019) was a major revision to version 2005 (v2005) that defined cystic renal mass subclasses based on wall or septa features. To determine the proportion of malignancy within cystic renal masses stratified by Bosniak classification v2019 class and feature-based subclass. MEDLINE and EMBASE databases were searched on July 24, 2023 for studies published in 2019 or later that reported cystic renal masses that underwent renal-mass CT or MRI, were assessed using Bosniak v2019, and had a reference standard (histopathology indicating benignity or malignancy or ≥5-year imaging follow-up indicating benignity).
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