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核旁蓝色包涵体:对原发性和转移性肺小细胞癌的细胞病理学诊断有帮助。

Paranuclear blue inclusions: an aid in the cytopathologic diagnosis of primary and metastatic pulmonary small-cell carcinoma.

作者信息

Mullins R K, Thompson S K, Coogan P S, Shurbaji M S

机构信息

Pathology and Laboratory Medicine Service, Veterans Affairs Medical Center, Johnson City, TN.

出版信息

Diagn Cytopathol. 1994;10(4):332-5. doi: 10.1002/dc.2840100408.

Abstract

Accurate diagnosis of small-cell carcinoma of the lung (SCLC) is clinically important because of the therapeutic implications. SCLC must be distinguished from non-small-cell carcinoma (NSCLC) and lymphoma. Paranuclear blue inclusions (PBIs) were recently described as a feature of metastatic SCLC on air-dried Wright-stained bone marrow aspirate smears. To determine the utility of PBIs in distinguishing SCLC from NSCLC and lymphoma, we evaluated air-dried Diff-Quik-stained smears from 103 fine-needle aspiration (FNA) specimens and 14 touch imprint specimens. PBIs were identified in 24 (89%) of 27 cases of SCLC, in 6 (9%) of 64 non-small-cell carcinomas (P < 0.00001), and in two (8%) of the 26 lymphoma cases (P < 0.00001). No PBIs were seen on any of the alcohol-fixed Papanicolaou or hematoxylin-eosin (H&E) stained smears examined. In conclusion, PBIs appear to be a feature of SCLC on air-dried cytologic material stained with Romanowsky type stains. In the presence of cytologic features of SCLC, the identification of PBIs provides a useful diagnostic feature for differentiating between SCLC and NSCLC carcinomas, and between SCLC and lymphomas in FNA specimens and touch imprints from surgical specimens.

摘要

由于治疗意义,准确诊断肺小细胞癌(SCLC)在临床上很重要。SCLC必须与非小细胞癌(NSCLC)和淋巴瘤相鉴别。核旁蓝色包涵体(PBIs)最近被描述为转移性SCLC在空气干燥的瑞氏染色骨髓穿刺涂片上的一个特征。为了确定PBIs在区分SCLC与NSCLC和淋巴瘤方面的效用,我们评估了来自103份细针穿刺(FNA)标本和14份触摸印片标本的空气干燥Diff-Quik染色涂片。在27例SCLC中的24例(89%)、64例非小细胞癌中的6例(9%)(P < 0.00001)以及26例淋巴瘤中的2例(8%)(P < 0.00001)中发现了PBIs。在所检查的任何酒精固定的巴氏染色或苏木精-伊红(H&E)染色涂片上均未见到PBIs。总之,PBIs似乎是空气干燥的经罗曼诺夫斯基型染色的细胞学材料上SCLC的一个特征。在存在SCLC细胞学特征的情况下,PBIs的识别为在FNA标本和手术标本的触摸印片中区分SCLC与NSCLC以及SCLC与淋巴瘤提供了一个有用的诊断特征。

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