Chen L, Zhang J S, Liu D G, Cui D, Meng Z L
Department of Pathology, Beijing Hospital, National Centre of Gerontology, No. 1 Da Hua Road, Dong Dan, Beijing, China.
Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Science, Dongcheng District, Beijing, China.
Cytopathology. 2018 Feb;29(1):10-21. doi: 10.1111/cyt.12449. Epub 2017 Sep 15.
There are limited studies of cytology diagnosis of haematopoietic and lymphoid tumours in serosal effusion except for occasional case reports. We would like to demonstrate an algorithmic approach for accurate diagnosis, especially in patients without previous history.
We reviewed 36 cases of lymphoma diagnosed in serosal effusion following an algorithmic approach. Suspected tumour cells were classified into small, intermediate and large sizes and two characteristic forms of plasmacytoid and Reed Sternberg-like on smears (step 1), followed by utilising panels of immunohistochemical markers and Epstein-Barr encoding region in situ hybridisation on cell blocks (step 2). A panel of CD3, CD20 and Ki-67 formed the basic workup, followed by pertinent batteries of immunostaining. Molecular tests were applied in 22 selected cases by fluorescence in situ hybridisation (step 3).
There were 15 diffuse large B-cell lymphomas; 12 plasma cell myelomas; two mantle cell lymphomas; one anaplastic large cell lymphoma ALK +; one small lymphocytic lymphoma; one plasmablastic lymphoma; one peripheral T-cell lymphoma, not otherwise specified, one extranodal NK/T-cell lymphoma, nasal type and two T-cell lymphoblastic lymphomas. 14 cases with previous history had complete concordance in immunophenotype between cytology and histology. Another 14 cases were primarily diagnosed in patients with initial symptom of effusion based on immunophenotyping and cytogenetic test in selected cases. Eight cases were diagnosed based on morphology alone.
An algorithmic approach based on morphology and immunohistochemistry is the key to making an accurate diagnosis of haematopoietic and lymphoid tumours in effusion. A molecular test is also important for confirmation and prognostic prediction. We reviewed 36 haematolymphoid neoplasms diagnosed in effusion including 14 cases primarily diagnosed in patients without previous history following an algorithmic approach by combining morphology, immunohistochemistry and molecular cytogenetics.
除了偶尔的病例报告外,关于浆膜腔积液中造血和淋巴肿瘤的细胞学诊断研究有限。我们希望展示一种用于准确诊断的算法方法,特别是对于既往无病史的患者。
我们回顾了36例采用算法方法诊断的浆膜腔积液淋巴瘤病例。在涂片上,将疑似肿瘤细胞分为小、中、大三种大小以及浆细胞样和里德·斯腾伯格样两种特征形态(步骤1),随后在细胞块上利用免疫组化标志物面板和爱泼斯坦-巴尔编码区原位杂交(步骤2)。一组CD3、CD20和Ki-67构成基本检查,随后进行相关的免疫染色组合。在22例选定病例中通过荧光原位杂交进行分子检测(步骤3)。
有15例弥漫性大B细胞淋巴瘤;12例浆细胞骨髓瘤;2例套细胞淋巴瘤;1例间变性大细胞淋巴瘤ALK阳性;1例小淋巴细胞淋巴瘤;1例浆母细胞淋巴瘤;1例未另行特指的外周T细胞淋巴瘤;1例鼻型结外NK/T细胞淋巴瘤;2例T细胞淋巴母细胞淋巴瘤。14例有既往病史的病例在细胞学和组织学的免疫表型上完全一致。另外14例主要是基于免疫表型分析和选定病例中的细胞遗传学检测,在以积液为初始症状的患者中诊断出来的。8例仅基于形态学诊断。
基于形态学和免疫组化的算法方法是准确诊断积液中造血和淋巴肿瘤的关键。分子检测对于确诊和预后预测也很重要。我们回顾了36例在积液中诊断的血液淋巴肿瘤,其中14例是通过结合形态学、免疫组化和分子细胞遗传学的算法方法,在既往无病史的患者中首次诊断出来的。