多排探测器高分辨率计算机断层扫描上的超小肺混浊：一项前瞻性放射学-病理学检查

Ultrasmall pulmonary opacities on multidetector-row high-resolution computed tomography: a prospective radiologic-pathologic examination.

作者信息

Ishikawa Hiroyuki, Koizumi Naoya, Morita Tetsuro, Tani Yoshiko, Tsuchida Masanori, Umezu Hajime, Naito Makoto, Sasai Keisuke

机构信息

Department of Radiology, Niigata University Hospital, Niigata, Japan.

出版信息

J Comput Assist Tomogr. 2005 Sep-Oct;29(5):621-5. doi: 10.1097/01.rct.0000172672.09564.8c.

DOI:10.1097/01.rct.0000172672.09564.8c

PMID:16163031

Abstract

OBJECTIVE

To clarify the pathologic findings of ultrasmall pulmonary opacities (5 mm or smaller in diameter) found on multidetector-row high-resolution computed tomography (MD-HRCT).

METHODS

Ten lobes in 10 patients were included in this study. Each lobe had a primary lung tumor and was removed surgically. Two thoracic radiologists noted any tiny nonlinear opacity on preoperative MD-HRCT films (1.25-mm thickness) covering the whole lobe. Pathologic findings of detected opacities were evaluated macroscopically and microscopically.

RESULTS

Among 139 ultrasmall opacities 5 mm or smaller in diameter, 94 corresponded to normal anatomic structures (partial volume averaging or motion artifact), 36 corresponded to pathologic abnormalities, and 9 were unidentified. Histologic diagnoses of 36 pathologic abnormalities were inflammatory lesions (n = 16), intrapulmonary lymph nodes (IPLN; n = 7), atypical adenomatous hyperplasia (AAH; n = 7), bronchioloalveolar carcinoma (BAC; n = 5), and another neoplastic lesion (n = 1).

CONCLUSION

Tiny pulmonary lesions, such as AAHs, BACs, and IPLNs, were identified among ultrasmall opacities found on MD-HRCT.

摘要

目的

明确在多排螺旋高分辨率计算机断层扫描（MD-HRCT）上发现的超小肺结节（直径5mm或更小）的病理表现。

方法

本研究纳入10例患者的10个肺叶。每个肺叶均有原发性肺肿瘤并接受了手术切除。两名胸部放射科医生在术前覆盖整个肺叶的MD-HRCT片（厚度1.25mm）上记录任何微小的非线性结节。对检测到的结节的病理表现进行宏观和微观评估。

结果

在139个直径5mm或更小的超小结节中，94个对应正常解剖结构（部分容积平均或运动伪影），36个对应病理异常，9个无法明确。36个病理异常的组织学诊断为炎症性病变（n = 16）、肺内淋巴结（IPLN；n = 7）、非典型腺瘤样增生（AAH；n = 7）、细支气管肺泡癌（BAC；n = 5）和另一种肿瘤性病变（n = 1）。