• 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

利用表观扩散系数(ADC)图区分小细胞肺癌和非小细胞肺癌脑转移瘤。

Differentiation of brain metastases from small and non-small lung cancers using apparent diffusion coefficient (ADC) maps.

机构信息

Department of Diagnostic and Interventional Neuroradiology, Georg-August-University Göttingen, Robert-Koch-Str. 40, 37075, Göttingen, Germany.

出版信息

BMC Med Imaging. 2021 Apr 15;21(1):70. doi: 10.1186/s12880-021-00602-7.

DOI:10.1186/s12880-021-00602-7
PMID:33858368
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8048287/
Abstract

BACKGROUND

Brain metastases are particularly common in patients with small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), with NSCLC showing a less  aggressive clinical course and lower chemo- and radio sensitivity compared to SCLC. Early adequate therapy is highly desirable and depends on a reliable classification of tumor type. The apparent diffusion coefficient is a noninvasive neuroimaging marker with the potential to differentiate between major histological subtypes. Here we determine the sensitivity and specificity of the apparent diffusion coefficient to distinguish between NSCLC and SCLC.

METHODS

We enrolled all NSCLC and SCLC patients diagnosed between 2008 and 2019 at the University Medical Center Göttingen. Cranial MR scans were visually inspected for brain metastases and the ratio of the apparent diffusion coefficient (ADC) was calculated by dividing the ADC measured within the solid part of a metastasis by a reference ADC extracted from an equivalent region in unaffected tissue on the contralateral hemisphere.

RESULTS

Out of 411 enrolled patients, we detected 129 patients (83 NSCLC, 46 SCLC) with sufficiently large brain metastases with histologically classified lung cancer and no hemorrhage. We analyzed 185 brain metastases, 84 of SCLC and 101 of NSCLC. SCLC brain metastases showed an ADC ratio of 0.68 ± 0.12 SD, and NSCLC brain metastases showed an ADC ratio of 1.47 ± 0.31 SD. Receiver operating curve statistics differentiated brain metastases of NSCLC from SCLC with an area under the curve of 0.99 and a 95% CI of 0.98 to 1, p < 0.001. Youden's J cut-point is 0.97 at a sensitivity of 0.989 and a specificity of 0.988.

CONCLUSIONS

In patients with lung cancer and brain metastases with solid tumor parts, ADC ratio enables an ad hoc differentiation of SCLC and NSCLC, easily achieved during routine neuroradiological examination. Non-invasive MR imaging enables an early-individualized management of brain metastases from lung cancer.

TRIAL REGISTRATION

The study was registered in the German Clinical Trials Register (DRKS00023016).

摘要

背景

脑转移在小细胞肺癌(SCLC)和非小细胞肺癌(NSCLC)患者中尤为常见,与 SCLC 相比,NSCLC 的临床病程侵袭性较低,化疗和放疗敏感性也较低。早期充分的治疗是非常理想的,这取决于对肿瘤类型的可靠分类。表观扩散系数是一种无创神经影像学标志物,具有区分主要组织学亚型的潜力。在这里,我们确定表观扩散系数区分 NSCLC 和 SCLC 的敏感性和特异性。

方法

我们招募了 2008 年至 2019 年期间在哥廷根大学医学中心诊断为 NSCLC 和 SCLC 的所有患者。对颅磁共振扫描进行视觉检查,以检测脑转移,并通过将转移灶实性部分的表观扩散系数(ADC)除以对侧半球无病变组织中提取的参考 ADC 来计算 ADC 比值。

结果

在纳入的 411 名患者中,我们检测到 129 名(83 名 NSCLC,46 名 SCLC)患有具有组织学分类肺癌且无出血的足够大的脑转移。我们分析了 185 个脑转移,84 个 SCLC 和 101 个 NSCLC。SCLC 脑转移的 ADC 比值为 0.68±0.12 SD,NSCLC 脑转移的 ADC 比值为 1.47±0.31 SD。接收者操作曲线统计区分 NSCLC 和 SCLC 的脑转移,曲线下面积为 0.99,95%可信区间为 0.98 至 1,p<0.001。Youden 的 J 切点为 0.97,灵敏度为 0.989,特异性为 0.988。

结论

在具有实性肿瘤部分的肺癌和脑转移患者中,ADC 比值可实现 SCLC 和 NSCLC 的特定区分,在常规神经放射学检查中很容易实现。非侵入性磁共振成像可实现对肺癌脑转移的早期个体化管理。

试验注册

该研究在德国临床试验注册处(DRKS00023016)注册。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8723/8048287/e7e122e04bb5/12880_2021_602_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8723/8048287/9e7a02724d45/12880_2021_602_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8723/8048287/56c03703cec5/12880_2021_602_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8723/8048287/e7e122e04bb5/12880_2021_602_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8723/8048287/9e7a02724d45/12880_2021_602_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8723/8048287/56c03703cec5/12880_2021_602_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8723/8048287/e7e122e04bb5/12880_2021_602_Fig3_HTML.jpg

相似文献

1
Differentiation of brain metastases from small and non-small lung cancers using apparent diffusion coefficient (ADC) maps.利用表观扩散系数(ADC)图区分小细胞肺癌和非小细胞肺癌脑转移瘤。
BMC Med Imaging. 2021 Apr 15;21(1):70. doi: 10.1186/s12880-021-00602-7.
2
Histogram Analysis of ADC Maps for Differentiating Brain Metastases From Different Histological Types of Lung Cancers.ADC 图直方图分析用于鉴别脑转移瘤与不同组织学类型肺癌。
Can Assoc Radiol J. 2021 May;72(2):271-278. doi: 10.1177/0846537120933837. Epub 2020 Jun 30.
3
Differentiation of brain metastases originating from lung and breast cancers using apparent diffusion coefficient histogram analysis and the relation of histogram parameters with Ki-67.利用表观扩散系数直方图分析鉴别肺和乳腺癌脑转移瘤及直方图参数与 Ki-67 的关系
Neuroradiol J. 2022 Jun;35(3):370-377. doi: 10.1177/19714009211049082. Epub 2021 Oct 5.
4
Diffusion Tensor Imaging Can Discriminate the Primary Cell Type of Intracranial Metastases for Patients with Lung Cancer.弥散张量成像可区分肺癌颅内转移的原发细胞类型。
Magn Reson Med Sci. 2022 Jul 1;21(3):425-431. doi: 10.2463/mrms.mp.2020-0183. Epub 2021 Mar 4.
5
Differentiating Small-Cell Lung Cancer From Non-Small-Cell Lung Cancer Brain Metastases Based on MRI Using Efficientnet and Transfer Learning Approach.基于 Efficientnet 和迁移学习方法的 MRI 鉴别小细胞肺癌与非小细胞肺癌脑转移。
Technol Cancer Res Treat. 2021 Jan-Dec;20:15330338211004919. doi: 10.1177/15330338211004919.
6
Whole-tumor histogram analysis of multi-parametric MRI for differentiating brain metastases histological subtypes in lung cancers: relationship with the Ki-67 proliferation index.多参数 MRI 全肿瘤直方图分析鉴别肺癌脑转移组织学亚型:与 Ki-67 增殖指数的关系
Neurosurg Rev. 2023 Sep 2;46(1):218. doi: 10.1007/s10143-023-02129-7.
7
MRI features and whole-lesion apparent diffusion coefficient histogram analysis of brain metastasis from non-small cell lung cancer for differentiating epidermal growth factor receptor mutation status.非小细胞肺癌脑转移瘤 MRI 特征及全病灶表观弥散系数直方图分析鉴别表皮生长因子受体突变状态
Clin Radiol. 2023 Mar;78(3):e243-e250. doi: 10.1016/j.crad.2022.11.010. Epub 2022 Dec 26.
8
Diffusion-weighted imaging vs STIR turbo SE imaging: capability for quantitative differentiation of small-cell lung cancer from non-small-cell lung cancer.弥散加权成像与短 TI 反转恢复快速自旋回波序列成像:定量鉴别小细胞肺癌与非小细胞肺癌的能力。
Br J Radiol. 2014 Jun;87(1038):20130307. doi: 10.1259/bjr.20130307. Epub 2014 Apr 30.
9
Quantitative apparent diffusion coefficients in the characterization of brain tumors and associated peritumoral edema.定量表观扩散系数在脑肿瘤及相关瘤周水肿特征描述中的应用
Acta Radiol. 2009 Jul;50(6):682-9. doi: 10.1080/02841850902933123.
10
Accuracy of diffusion-weighted (DW) MRI with background signal suppression (MR-DWIBS) in diagnosis of mediastinal lymph node metastasis of nonsmall-cell lung cancer (NSCLC).背景信号抑制扩散加权磁共振成像(MR-DWIBS)在非小细胞肺癌(NSCLC)纵隔淋巴结转移诊断中的准确性。
J Magn Reson Imaging. 2014 Jul;40(1):200-5. doi: 10.1002/jmri.24343. Epub 2013 Oct 29.

引用本文的文献

1
Late enhancement and wash-out maps for differentiation of glioblastoma and metastases.用于鉴别胶质母细胞瘤和转移瘤的延迟强化及廓清图。
BMC Med Imaging. 2025 Aug 27;25(1):353. doi: 10.1186/s12880-025-01889-6.
2
Classifying brain metastases originating from different pathological subtypes of lung cancer via a multimodal magnetic resonance imaging-based deep learning approach.通过基于多模态磁共振成像的深度学习方法对源自肺癌不同病理亚型的脑转移瘤进行分类。
J Thorac Dis. 2025 Jul 31;17(7):5250-5259. doi: 10.21037/jtd-2025-1285. Epub 2025 Jul 29.
3
Magnetic resonance imaging characteristics of small cell and non-small cell lung cancer brain metastases: a retrospective study.

本文引用的文献

1
Basement membrane stiffness determines metastases formation.基底膜硬度决定转移灶形成。
Nat Mater. 2021 Jun;20(6):892-903. doi: 10.1038/s41563-020-00894-0. Epub 2021 Jan 25.
2
The Effect of Comorbidities on Wound Healing.合并症对伤口愈合的影响。
Surg Clin North Am. 2020 Aug;100(4):695-705. doi: 10.1016/j.suc.2020.05.002. Epub 2020 Jun 17.
3
Impact of neuroimaging in the pretreatment evaluation of early stage non-small cell lung cancer.神经影像学在早期非小细胞肺癌预处理评估中的作用
小细胞肺癌和非小细胞肺癌脑转移瘤的磁共振成像特征:一项回顾性研究。
J Med Life. 2025 Jun;18(6):563-574. doi: 10.25122/jml-2024-0411.
4
Developing a predictive model and uncovering immune influences on prognosis for brain metastasis from lung carcinomas.建立预测模型并揭示免疫因素对肺癌脑转移预后的影响。
Front Oncol. 2025 Mar 3;15:1554242. doi: 10.3389/fonc.2025.1554242. eCollection 2025.
5
De novo GTP synthesis is a metabolic vulnerability for the interception of brain metastases.从头合成 GTP 是阻断脑转移的代谢脆弱性。
Cell Rep Med. 2024 Oct 15;5(10):101755. doi: 10.1016/j.xcrm.2024.101755. Epub 2024 Oct 4.
6
Differentiation between multifocal CNS lymphoma and glioblastoma based on MRI criteria.基于MRI标准鉴别多灶性中枢神经系统淋巴瘤和胶质母细胞瘤。
Discov Oncol. 2024 Sep 1;15(1):397. doi: 10.1007/s12672-024-01266-9.
7
Differentiation of multiple brain metastases and glioblastoma with multiple foci using MRI criteria.使用 MRI 标准区分多发性脑转移瘤和多灶性脑胶质母细胞瘤。
BMC Med Imaging. 2024 Jan 2;24(1):3. doi: 10.1186/s12880-023-01183-3.
8
Magnetic resonance imaging characteristics of brain metastases in small cell lung cancer.小细胞肺癌脑转移的磁共振成像特征。
Cancer Med. 2023 Jul;12(14):15199-15206. doi: 10.1002/cam4.6206. Epub 2023 Jun 8.
9
Towards updated understanding of brain metastasis.迈向对脑转移的更新理解。
Am J Cancer Res. 2022 Sep 15;12(9):4290-4311. eCollection 2022.
Heliyon. 2020 Jun 29;6(6):e04319. doi: 10.1016/j.heliyon.2020.e04319. eCollection 2020 Jun.
4
Histogram Analysis of ADC Maps for Differentiating Brain Metastases From Different Histological Types of Lung Cancers.ADC 图直方图分析用于鉴别脑转移瘤与不同组织学类型肺癌。
Can Assoc Radiol J. 2021 May;72(2):271-278. doi: 10.1177/0846537120933837. Epub 2020 Jun 30.
5
Evaluation of First-line Radiosurgery vs Whole-Brain Radiotherapy for Small Cell Lung Cancer Brain Metastases: The FIRE-SCLC Cohort Study.小细胞肺癌脑转移一线立体定向放疗与全脑放疗的评价:FIRE-SCLC 队列研究。
JAMA Oncol. 2020 Jul 1;6(7):1028-1037. doi: 10.1001/jamaoncol.2020.1271.
6
Real-world treatment patterns and outcomes of patients with extensive disease small cell lung cancer.广泛期小细胞肺癌患者的真实世界治疗模式和结局。
Eur J Cancer Care (Engl). 2020 Sep;29(5):e13250. doi: 10.1111/ecc.13250. Epub 2020 May 14.
7
Rates of Overall Survival and Intracranial Control in the Magnetic Resonance Imaging Era for Patients With Limited-Stage Small Cell Lung Cancer With and Without Prophylactic Cranial Irradiation.限局性小细胞肺癌患者接受和未接受预防性颅脑照射的磁共振成像时代的总生存和颅内控制率。
JAMA Netw Open. 2020 Apr 1;3(4):e201929. doi: 10.1001/jamanetworkopen.2020.1929.
8
Metastases to the central nervous system: Molecular basis and clinical considerations.中枢神经系统转移:分子基础与临床考量
J Neurol Sci. 2020 May 15;412:116755. doi: 10.1016/j.jns.2020.116755. Epub 2020 Feb 21.
9
Risk stratification of symptomatic brain metastases by clinical and FDG PET parameters for selective use of prophylactic cranial irradiation in patients with extensive disease of small cell lung cancer.通过临床和 FDG PET 参数对有症状脑转移进行风险分层,以便在广泛期小细胞肺癌患者中选择性使用预防性颅脑照射。
Radiother Oncol. 2020 Feb;143:81-87. doi: 10.1016/j.radonc.2020.01.009. Epub 2020 Feb 7.
10
Distribution Of Brain Metastasis From Lung Cancer.肺癌脑转移的分布
Cancer Manag Res. 2019 Nov 1;11:9331-9338. doi: 10.2147/CMAR.S222920. eCollection 2019.