• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

扩散加权磁共振成像 ADC 值测量在鉴别鼻窦肿瘤组织学类型中的应用。

Application of diffusion-weighted MR imaging with ADC measurement for distinguishing between the histopathological types of sinonasal neoplasms.

机构信息

Department of Radiology, University of Minnesota Medical Center, Minneapolis, MN, USA.

Department of Otolaryngology-Head and Neck Surgery, University of Minnesota Medical Center, Minneapolis, MN, USA.

出版信息

Clin Imaging. 2019 May-Jun;55:76-82. doi: 10.1016/j.clinimag.2019.02.004. Epub 2019 Feb 7.

DOI:10.1016/j.clinimag.2019.02.004
PMID:30769222
Abstract

PURPOSE

To evaluate the potential contribution of quantitative DWI parameters including ADC and ADC values to help in distinguishing the histopathological types of sinonasal neoplasms.

METHODS

This retrospective study included 83 patients (50 males, 33 females; mean age 61 years) with pathologically proven untreated sinonasal neoplasms who have undergone diffusion-weighted MRI imaging from February 2010 to August 2017. Diffusion-weighted MRI was performed on a 3 T unit with b factors of 0 and 1000 s/mm, and ADC maps were generated. Mean ADC values of sinonasal tumors and ADC ratios (ADC of the tumor to ADC of pterygoid muscles) were compared with the histopathological diagnosis by utilizing the Kruskal-Wallis non-parametric test.

RESULTS

Mean ADC and ADC were 0.8 (SD, ±0.4) × (10 mm/s) and 1.2 (SD, ±0.5), respectively, and each parameter was significantly different between histopathological types (p < 0.05). Mean ADC and ADC were higher in adenoid cystic carcinoma (ACC) than in SCC, lymphoma, neuroendocrine carcinoma and sinonasal undifferentiated carcinoma (SNUC) (p < 0.05). Optimized ADC thresholds of 0.79, 0.81, 0.74 and 0.78 (10 mm/s) achieved maximal discriminatory accuracies of 100%, 79%, 100% and 89% for ACC/SNUC, ACC/SCC, ACC/neuroendocrine carcinoma, and ACC/lymphoma, respectively.

CONCLUSIONS

The optimized ADC threshold of 0.80 (10 mm/s) could be used to differentiate ACC from non-ACC sinonasal neoplasms with maximal discriminatory accuracy (82%) and sensitivity of 100%. However, there is considerable overlapping of the ADC and ADC values among non-ACC sinonasal neoplasms hence surgical biopsy is still needed.

摘要

目的

评估定量 DWI 参数(包括 ADC 值和 ADC 值)在帮助鉴别鼻窦肿瘤组织学类型方面的潜在作用。

方法

本回顾性研究纳入了 2010 年 2 月至 2017 年 8 月期间在我院接受扩散加权 MRI 成像检查且经病理证实未治疗的鼻窦肿瘤患者 83 例(男 50 例,女 33 例;平均年龄 61 岁)。DWI 检查在 3T 磁共振仪上进行,b 值分别为 0 和 1000s/mm2,生成 ADC 图。利用 Kruskal-Wallis 非参数检验比较鼻窦肿瘤的平均 ADC 值和 ADC 比值(肿瘤的 ADC 值与翼内肌的 ADC 值之比)与组织病理学诊断之间的关系。

结果

平均 ADC 值和 ADC 比值分别为 0.8(标准差±0.4)×(10mm/s)和 1.2(标准差±0.5),各参数在不同组织学类型之间差异均有统计学意义(p<0.05)。腺样囊性癌(ACC)的平均 ADC 值和 ADC 比值均高于鳞状细胞癌(SCC)、淋巴瘤、神经内分泌癌和未分化癌(SNUC)(p<0.05)。优化后的 ADC 阈值为 0.79、0.81、0.74 和 0.78(10mm/s),用于鉴别 ACC/SNUC、ACC/SCC、ACC/神经内分泌癌和 ACC/淋巴瘤的最大鉴别准确率分别为 100%、79%、100%和 89%。

结论

优化后的 ADC 阈值为 0.80(10mm/s),可以用于鉴别 ACC 和非 ACC 鼻窦肿瘤,最大鉴别准确率(82%)和敏感性均为 100%。然而,非 ACC 鼻窦肿瘤的 ADC 值和 ADC 比值有较大重叠,因此仍需要进行手术活检。

相似文献

1
Application of diffusion-weighted MR imaging with ADC measurement for distinguishing between the histopathological types of sinonasal neoplasms.扩散加权磁共振成像 ADC 值测量在鉴别鼻窦肿瘤组织学类型中的应用。
Clin Imaging. 2019 May-Jun;55:76-82. doi: 10.1016/j.clinimag.2019.02.004. Epub 2019 Feb 7.
2
Cervical lymphadenopathy: can the histogram analysis of apparent diffusion coefficient help to differentiate between lymphoma and squamous cell carcinoma in patients with unknown clinical primary tumor?颈部淋巴结病:表观扩散系数直方图分析能否帮助区分不明临床原发性肿瘤患者的淋巴瘤和鳞状细胞癌?
Radiol Med. 2019 Jan;124(1):19-26. doi: 10.1007/s11547-018-0940-1. Epub 2018 Sep 8.
3
Preoperative grading of supratentorial gliomas using high or standard b-value diffusion-weighted MR imaging at 3T.使用3T高或标准b值扩散加权磁共振成像对幕上胶质瘤进行术前分级
Diagn Interv Imaging. 2017 Mar;98(3):261-268. doi: 10.1016/j.diii.2016.11.005. Epub 2016 Dec 28.
4
Diagnostic accuracy of diffusion-weighted MR imaging for nasopharyngeal carcinoma, head and neck lymphoma and squamous cell carcinoma at the primary site.弥散加权磁共振成像对鼻咽部原发灶的鼻咽癌、头颈部淋巴瘤和鳞状细胞癌的诊断准确性。
Oral Oncol. 2010 Aug;46(8):603-6. doi: 10.1016/j.oraloncology.2010.05.004. Epub 2010 Jul 8.
5
Diffusion-Weighted Imaging (DWI) derived from PET/MRI for lymph node assessment in patients with Head and Neck Squamous Cell Carcinoma (HNSCC).正电子发射断层扫描/磁共振成像(PET/MRI)弥散加权成像(DWI)用于头颈部鳞状细胞癌(HNSCC)患者的淋巴结评估。
Cancer Imaging. 2020 Aug 8;20(1):56. doi: 10.1186/s40644-020-00334-x.
6
Malignant cervical lymphadenopathy: diagnostic accuracy of diffusion-weighted MR imaging.恶性宫颈淋巴结病:扩散加权磁共振成像的诊断准确性
Radiology. 2007 Dec;245(3):806-13. doi: 10.1148/radiol.2451061804. Epub 2007 Oct 2.
7
Positron emission computed tomography and magnetic resonance imaging features of sinonasal small round blue cell tumors.鼻窦小圆形蓝细胞肿瘤的正电子发射计算机断层扫描和磁共振成像特征
Neuroradiol J. 2020 Feb;33(1):48-56. doi: 10.1177/1971400919873895. Epub 2019 Aug 28.
8
High-Resolution Diffusion-Weighted Imaging Improves the Diagnostic Accuracy of Dynamic Contrast-Enhanced Sinonasal Magnetic Resonance Imaging.高分辨率扩散加权成像提高了动态对比增强鼻窦磁共振成像的诊断准确性。
J Comput Assist Tomogr. 2017 Mar/Apr;41(2):199-205. doi: 10.1097/RCT.0000000000000502.
9
Improving lymph node characterization in staging malignant lymphoma using first-order ADC texture analysis from whole-body diffusion-weighted MRI.利用全身弥散加权 MRI 的一阶 ADC 纹理分析提高恶性淋巴瘤分期中淋巴结的特征描述。
J Magn Reson Imaging. 2018 Oct;48(4):897-906. doi: 10.1002/jmri.26034. Epub 2018 Apr 14.
10
Texture analysis of conventional magnetic resonance imaging and diffusion-weighted imaging for distinguishing sinonasal non-Hodgkin's lymphoma from squamous cell carcinoma.基于常规磁共振成像和弥散加权成像的纹理分析对鉴别鼻腔鼻窦非霍奇金淋巴瘤与鳞状细胞癌的价值。
Eur Arch Otorhinolaryngol. 2022 Dec;279(12):5715-5720. doi: 10.1007/s00405-022-07493-6. Epub 2022 Jun 22.

引用本文的文献

1
Chinese expert consensus on imaging examination and diagnosis of nasal cavity and paranasal sinus tumors.鼻腔及鼻窦肿瘤影像学检查与诊断中国专家共识
Front Oncol. 2025 Aug 11;15:1626584. doi: 10.3389/fonc.2025.1626584. eCollection 2025.
2
Deep transfer learning radiomics for distinguishing sinonasal malignancies: a preliminary MRI study.用于鉴别鼻窦恶性肿瘤的深度迁移学习放射组学:一项初步的MRI研究
Future Oncol. 2025 Apr;21(8):975-982. doi: 10.1080/14796694.2025.2469486. Epub 2025 Feb 24.
3
Deep learning models for differentiating three sinonasal malignancies using multi-sequence MRI.
使用多序列磁共振成像鉴别三种鼻窦恶性肿瘤的深度学习模型
BMC Med Imaging. 2025 Feb 21;25(1):56. doi: 10.1186/s12880-024-01517-9.
4
Sinonasal adenoid cystic carcinoma: preoperative apparent diffusion coefficient histogram analysis in prediction of prognosis and Ki-67 proliferation status.鼻窦腺样囊性癌:术前表观扩散系数直方图分析对预后及Ki-67增殖状态的预测
Jpn J Radiol. 2025 Mar;43(3):389-401. doi: 10.1007/s11604-024-01676-3. Epub 2024 Oct 9.
5
Accuracy of magnetic resonance imaging in the assessment of depth of invasion in tongue carcinoma: A systematic review and meta-analysis.磁共振成像评估舌癌浸润深度的准确性:一项系统评价和荟萃分析。
Natl J Maxillofac Surg. 2023 Sep-Dec;14(3):341-353. doi: 10.4103/njms.njms_174_22. Epub 2023 Nov 10.
6
PET/CT Imaging in Treatment Planning and Surveillance of Sinonasal Neoplasms.PET/CT成像在鼻窦肿瘤治疗计划与监测中的应用
Cancers (Basel). 2023 Jul 25;15(15):3759. doi: 10.3390/cancers15153759.
7
Whole-tumor histogram analysis of apparent diffusion coefficient maps with machine learning algorithms for predicting histologic grade of sinonasal squamous cell carcinoma: a preliminary study.基于机器学习算法的表观扩散系数图全瘤直方图分析预测鼻窦鳞状细胞癌组织学分级:一项初步研究。
Eur Arch Otorhinolaryngol. 2023 Sep;280(9):4131-4140. doi: 10.1007/s00405-023-07989-9. Epub 2023 May 9.
8
Development and validation of apparent diffusion coefficient histogram-based nomogram for predicting malignant transformation of sinonasal inverted papilloma.基于表观扩散系数直方图的nomogram 模型预测鼻窦内翻性乳头状瘤恶变的建立与验证。
Dentomaxillofac Radiol. 2023 Sep;52(6):20220301. doi: 10.1259/dmfr.20220301. Epub 2023 Feb 17.
9
A Clinical-Radiomics Nomogram Based on the Apparent Diffusion Coefficient (ADC) for Individualized Prediction of the Risk of Early Relapse in Advanced Sinonasal Squamous Cell Carcinoma: A 2-Year Follow-Up Study.基于表观扩散系数(ADC)的临床-影像组学列线图用于个体化预测晚期鼻窦鳞状细胞癌早期复发风险:一项2年随访研究
Front Oncol. 2022 May 16;12:870935. doi: 10.3389/fonc.2022.870935. eCollection 2022.
10
Texture Analysis of Fat-Suppressed T2-Weighted Magnetic Resonance Imaging and Use of Machine Learning to Discriminate Nasal and Paranasal Sinus Small Round Malignant Cell Tumors.脂肪抑制T2加权磁共振成像的纹理分析及利用机器学习鉴别鼻腔和鼻窦小圆形恶性细胞肿瘤
Front Oncol. 2021 Dec 13;11:701289. doi: 10.3389/fonc.2021.701289. eCollection 2021.