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胃癌的体外磁共振成像:侵袭深度诊断的准确性

MR imaging of gastric cancer in vitro: accuracy of invasion depth diagnosis.

作者信息

Sato Chiho, Naganawa Shinji, Kumada Hisashi, Miura Shunichi, Ishigaki Takeo

机构信息

Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Shouwa-ku, 466-8550, Nagoya, Japan.

出版信息

Eur Radiol. 2004 Sep;14(9):1543-9. doi: 10.1007/s00330-004-2362-9. Epub 2004 Jul 17.

Abstract

The purpose of this study was to evaluate the accuracy of grading cancerous invasion of the gastric wall in vitro using magnetic resonance (MR) imaging. Twelve specimens of gastric carcinoma were examined at 1.5-T using a small, loop surface coil. They were imaged within 2 days of fixation in formalin. The field of view was 30 mm; the matrix size was 256x256, and the section thickness was 2 mm. T1-weighted, T2-weighted and short inversion time inversion recovery (STIR) images were obtained. Two radiologists evaluated the MR images independently, and in discrepant cases, consensus was obtained through discussion. Findings on MR images were compared with histopathologic findings. All T1-weighted, T2-weighted and STIR images depicted the normal gastric wall as consisting of six layers. STIR images depicted normal six layers most clearly. Histologically, the cancerous invasion was found to extend into the mucosa in 4 of the 12 specimens, the submucosa in 3, the muscularis propria in 2, the subserosa in 2 and the serosa in 1. The grading by MR imaging matched the histopathologic findings for all 12 tumors. The overall accuracy was 100%. Thus, MR imaging in vitro was sufficiently accurate for grading cancerous invasion of the gastric wall.

摘要

本研究的目的是使用磁共振(MR)成像在体外评估胃癌侵犯胃壁的分级准确性。使用小型环形表面线圈在1.5-T场强下对12个胃癌标本进行检查。在福尔马林中固定后2天内对它们进行成像。视野为30毫米;矩阵大小为256x256,切片厚度为2毫米。获得了T1加权、T2加权和短反转时间反转恢复(STIR)图像。两名放射科医生独立评估MR图像,在存在分歧的情况下,通过讨论达成共识。将MR图像上的发现与组织病理学发现进行比较。所有T1加权、T2加权和STIR图像均显示正常胃壁由六层组成。STIR图像最清晰地显示了正常的六层结构。组织学检查发现,12个标本中有4个癌侵犯延伸至黏膜层,3个至黏膜下层,2个至固有肌层,2个至浆膜下层,1个至浆膜层。MR成像的分级与所有12个肿瘤的组织病理学发现相符。总体准确率为100%。因此,体外MR成像对于胃癌侵犯胃壁的分级足够准确。

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