Tavakol Sherwin, Catalino Michael P, Cote David J, Boles Xian, Laws Edward R, Bi Wenya Linda
Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.
Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States.
Front Oncol. 2021 Dec 10;11:778824. doi: 10.3389/fonc.2021.778824. eCollection 2021.
A 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.
We retrospectively reviewed 205 patients' medical records (2008-2020) who underwent primary surgery for a cystic sellar lesion. Cysts were classified 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.
The 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%.
When 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.
目前尚无针对鞍区囊性病变的分类系统。我们基于囊壁/内容物的异质性以及影像学上实性成分的存在,提出了一种针对这些病变的新型分类方案。
我们回顾性分析了2008年至2020年间接受鞍区囊性病变初次手术的205例患者的病历。根据囊壁/内容物的异质性以及实性成分影像学表现,将囊肿分为4种囊肿类型之一。评分者间信度较高。使用单变量和多变量模型来评估囊肿类型预测两种最常见诊断的能力:拉克氏裂囊肿(RCC)和囊性垂体腺瘤。
我们队列中RCC和囊性垂体腺瘤的发生率分别为45.4%和36.4%。非肿瘤性病变(如蛛网膜囊肿和RCC)更可能为1型或2型,而囊性肿瘤(如垂体腺瘤和颅咽管瘤)更可能为3型或4型(p<0.0001)。与1型相比,较高囊肿类型的病变为囊性垂体腺瘤而非RCC的几率更高(2型和4型的比值比分别为:23.7,p=0.033;342.6,p<0.0001)。术前MRI显示有液-液平面的病变为垂体腺瘤的几率也更高(比值比:12.7;p=0.023)。囊性垂体腺瘤在肥胖患者(比值比:5.0,p=0.003)或有症状性高催乳素血症患者中更常见(比值比分别为:11.5;p<0.001)。多变量模型的阳性预测值为82.2%,阴性预测值为86.4%。
当应用于RCC与囊性垂体腺瘤的诊断时,较高的囊性病变类型(2型和4型)、液-液平面的存在、症状性高催乳素血症和肥胖是囊性垂体腺瘤的预测因素。需要进一步验证,但这种分类方案可能被证明是管理常见鞍区病变患者的有用工具。