cyst type
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2021 ◽  
Vol 11 ◽  
Author(s):  
Sherwin Tavakol ◽  
Michael P. Catalino ◽  
David J. Cote ◽  
Xian Boles ◽  
Edward R. Laws ◽  
...  

PurposeA classification system for cystic sellar lesions does not exist. We propose a novel classification scheme for these lesions based on the heterogeneity of the cyst wall/contents and the presence of a solid component on imaging.MethodsWe retrospectively reviewed 205 patients’ medical records (2008–2020) who underwent primary surgery for a cystic sellar lesion. Cysts were classified a priori into 1 of 4 cyst types based on the heterogeneity of the cyst wall/contents and the presence of a solid component imaging. There was high interrater reliability. Univariable and multivariable models were used to estimate the ability of cyst type to predict the two most common diagnoses: Rathke cleft cyst (RCC) and cystic pituitary adenoma.ResultsThe frequencies of RCC and cystic pituitary adenoma in our cohort were 45.4% and 36.4%, respectively. Non-neoplastic lesions (e.g., arachnoid cysts and RCC) were more likely to be Type 1 or 2, whereas cystic neoplasms (e.g., pituitary adenomas and craniopharyngiomas) were more likely to be Type 3 or 4 (p<0.0001). Higher cyst types, compared to Type 1, had higher odds of being cystic pituitary adenomas compared to RCCs (OR: 23.7, p=0.033, and 342.6, p <0.0001, for Types 2 and 4, respectively). Lesions with a fluid-fluid level on preoperative MRI also had higher odds of being pituitary adenomas (OR: 12.7; p=0.023). Cystic pituitary adenomas were more common in patients with obesity (OR: 5.0, p=0.003) or symptomatic hyperprolactinemia (OR: 11.5; p<0.001, respectively). The multivariable model had a positive predictive value of 82.2% and negative predictive value of 86.4%.ConclusionWhen applied to the diagnosis of RCC versus cystic pituitary adenoma, higher cystic lesion types (Type 2 & 4), presence of fluid-fluid level, symptomatic hyperprolactinemia, and obesity were predictors of cystic pituitary adenoma. Further validation is needed, but this classification scheme may prove to be a useful tool for the management of patients with common sellar pathology.


2021 ◽  
Author(s):  
Dr jaitalkar
Keyword(s):  

2021 ◽  
Vol 233 (5) ◽  
pp. S156
Author(s):  
Sophia Hernandez ◽  
Andre Luiz Lourenco ◽  
Ezgi Kirimli ◽  
Tyler J. York ◽  
Alexa Glencer ◽  
...  
Keyword(s):  

2021 ◽  
Author(s):  
Henry Knipe ◽  
Dijendra Biswas
Keyword(s):  

Cureus ◽  
2021 ◽  
Author(s):  
Sugumaran K ◽  
Hirdaya H Nag

2021 ◽  
Vol 25 (1) ◽  
pp. S135-S135
Author(s):  
Jae Do YANG ◽  
Mi Rin LEE ◽  
Sung Woo AHN ◽  
Hee Chul YU

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Tatsuo Hata ◽  
Masamichi Mizuma ◽  
Fuyuhiko Motoi ◽  
Yuko Omori ◽  
Masaharu Ishida ◽  
...  

Abstract Pancreatic cystic neoplasms (PCNs) are a heterogeneous group with varying risks of malignancy. To explore the clinical utility of liquid biopsy in cyst type classification, we analyzed the GNAS/KRAS mutations in circulating cell-free DNA (cfDNA) obtained from 57 patients with histologically diagnosed PCNs, including 34 with intraductal papillary mucinous neoplasms (IPMNs) and compared the mutant allele prevalence and variant patterns with the paired resected specimens using next-generation sequencing. The positive prevalence of GNAS mutations in cfDNA of patients with IPMN (n = 11, 32%) was significantly higher than that in those with other PCNs (0%, P = 0.002). Conversely, KRAS mutations were detected in cfDNA of only 2 (6%) IPMN patients. The paired-sample comparison revealed highly concordance between the GNAS mutation status of cfDNA and resected IPMN specimens. Similar distributions of GNAS mutation positivity in cfDNA were observed across the different histological grades, whereas IPMNs with intestinal subtype showed a significantly higher prevalence of GNAS mutations than other subtypes (P = 0.030). GNAS mutation positivity in cfDNA was significantly associated with the acellular mucin pool of histological findings in primary IPMN lesions (P = 0.017). Detection of GNAS mutation in cfDNA can serve as a novel biomarker for cyst type classification and differentiation of intestinal subtype IPMN from the other PCNs.


2020 ◽  
Vol 08 (05) ◽  
pp. E656-E667 ◽  
Author(s):  
Donevan R. Westerveld ◽  
Sandeep A. Ponniah ◽  
Peter V. Draganov ◽  
Dennis Yang

Abstract Background and study aims Accurate diagnosis and risk stratification of pancreatic cysts (PCs) is challenging. The aim of this study was to perform a systematic review and meta-analysis to assess the feasibility, safety, and diagnostic yield of endoscopic ultrasound-guided through-the-needle biopsy (TTNB) versus fine-needle aspiration (FNA) in PCs. Methods Comprehensive search of databases (PubMed, EMBASE, Cochrane, Web of Science) for relevant studies on TTNB of PCs (from inception to June 2019). The primary outcome was to compare the pooled diagnostic yield and concordance rate with surgical pathology of TTNB histology and FNA cytology of PCs. The secondary outcome was to estimate the safety profile of TTNB. Results: Eight studies (426 patients) were included. The diagnostic yield was significantly higher with TTNB over FNA for a specific cyst type (OR: 9.4; 95 % CI: [5.7–15.4]; I2 = 48) or a mucinous cyst (MC) (OR: 3.9; 95 % CI: [2.0–7.4], I2 = 72 %). The concordance rate with surgical pathology was significantly higher with TTNB over FNA for a specific cyst type (OR: 13.5; 95 % CI: [3.5–52.3]; I2 = 48), for a MC (OR: 8.9; 95 % [CI: 1.9–40.8]; I2 = 29), and for MC histologic severity (OR: 10.4; 95 % CI: [2.9–36.9]; I2 = 0). The pooled sensitivity and specificity of TTNB for MCs were 90.1 % (95 % CI: [78.4–97.6]; I2 = 36.5 %) and 94 % (95 % CI: [81.5–99.7]; I2 = 0), respectively. The pooled adverse event rate was 7.0 % (95 % CI: [2.3–14.1]; I2 = 82.9). Conclusions TTNB is safe, has a high sensitivity and specificity for MCs and may be superior to FNA cytology in risk-stratifying MCs and providing a specific cyst diagnosis.


Author(s):  
Arkaitz Perfecto Valero ◽  
Mikel Prieto Calvo ◽  
Mikel Gastaca Mateo

2019 ◽  
Vol 376 (3) ◽  
pp. 457-470 ◽  
Author(s):  
Sergei Iu. Demin ◽  
Vera N. Stefanova ◽  
Andrey I. Granovitch ◽  
Natalia A. Mikhailova

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