磁共振表观扩散系数成像在鉴别肺癌和肺良性病变中的应用

摘要
图/表
参考文献
相关文章 (5)

全文: PDF (3447 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要目的评估基于表观扩散系数的磁共振扩散加权成像不同模型在鉴别肺癌和肺良性病变中的应用效果。方法这项回顾性单中心研究选择2020年1月―2021年6月49例患者为研究对象,包括肺癌组（n=32）和良性病变组（n=17）。所有患者在手术或活检前均接受了扩散加权成像（DWI）检查。ADC直方图参数,包括平均值、百分位值（第10位和第90位）、峰态和偏度,由两名放射科医生独立计算。比较肺癌患者和良性病变患者的直方图参数。构建接收者操作特征曲线以评估这些参数的诊断性能。结果肺癌组中的ADC_平均值、ADC_10th 、D_平均值和D_10th显著低于良性病变组,并且ADC_偏度和D_偏度显著高于良性病变组。D_10th和ADC_10th诊断良性病变能力最好。D_10th的曲线下面积（AUC）为0.851,在0.453×10^-3 mm/s的最佳临界值下的准确率为79.6%;ADC_10th的AUC为0.814,在0.504×10^-3 mm/s最佳临界值下的准确率为83.4%。结论使用DWI单指数和双指数模型得出的ADC参数进行全病灶直方图分析可以区分肺癌与良性病灶。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	郝永杰
	邓军吉
	冯坤
	杜汉旺
	陈伟
	魏瑶
	崔传水

关键词 ：弥散加权磁共振成像, 磁共振成像, 肺癌, 肺良性病变, 表观扩散系数, ADC直方图

Abstract：Objective To evaluate the application effect of different MRI diffusion-weighted imaging models based on the apparent diffusion coefficient in distinguishing lung cancer from benign lung lesions. Methods This retrospective single-center study selected 49 patients from January 2020 to June 2021 as the study subjects, including the lung cancer group (n=32) and the benign disease group (n=17). All patients underwent diffusion-weighted imaging (DWI) before surgery or biopsy. ADC histogram parameters, including mean, percentile values (10th and 90th), kurtosis, and skewness, were calculated independently by two radiologists. The histogram parameters were compared between patients with lung cancer and benign lesions. ROC curves were constructed to evaluate the diagnostic performance. Results The ADC_mean, ADC_10th, D_mean and D_10th in the lung cancer group were significantly lower than the benign lesion group, and the ADC_skewness and D_skewness were significantly higher than the benign lesion group. D_10th and ADC_10th had the best ability to diagnose benign lesions. The area under the curve (AUC) of D_10th was 0.851, and the accuracy was 79.6% at the best critical value of 0.453×10^-3 mm/s; the AUC of ADC_10th was 0.814, and the accuracy was 83.4% at the best critical value of 0.504×10^-3 mm/s. Conclusions Whole-lesion histogram analysis of ADC parameters derived from the DWI single-index and double-index models can distinguish lung cancer from benign lesions.

Key words： diffusion-weighted magnetic resonance imaging magnetic resonance imaging lung cancer lung benign lesion apparent diffusion coefficient ADC histogram

收稿日期: 2022-06-24

中图分类号:

R734

基金资助:山东省卫生健康委员会科研项目“磁共振表观扩散系数成像在不同模型在鉴别肺癌和肺良性病变中的应用”（20200125）; 2020年度潍坊市卫健委科研项目计划“磁共振表观扩散系数成像在不同模型在鉴别肺癌和肺良性病变中的应用”（WFWSJK-2020-103）

通讯作者: 邓军吉,男,E-mail：haoyongjie163@163.com

引用本文:

郝永杰, 邓军吉, 冯坤, 杜汉旺, 陈伟, 魏瑶, 崔传水. 磁共振表观扩散系数成像在鉴别肺癌和肺良性病变中的应用[J]. 湖南师范大学学报(医学版), 2024, 21(1): 98-102. HAO Yongjie, DENG Junji, FENG Kun, DU Hanwang, CHEN Wei, WEI Yao, CUI Chuanshui. Application of magnetic resonance apparent diffusion coefficient imaging in differentiating lung cancer from benign lung lesions. HuNan ShiFan DaXue XueBao(YiXueBan), 2024, 21(1): 98-102.

链接本文:

http://yxb.hunnu.edu.cn/CN/ 或 http://yxb.hunnu.edu.cn/CN/Y2024/V21/I1/98

[1] 郭玉珠, 于钏钏, 许宁, 等. 基于贝叶斯时空模型黑龙江省肺癌死亡风险及其影响因素分析[J]. 中国公共卫生, 2021, 37(6): 965-973.
[2] 任真, 丁红梅, 钱香, 等. 肿瘤相关自身抗体在早期非小细胞肺癌中的应用价值[J]. 中华预防医学杂志, 2021, 55(12): 1426-1434.
[3] 江慎林, 龚良庚, 周战梅, 等. DWI联合CT对肺内良恶性病变的鉴别诊断价值[J]. 放射学实践, 2020, 35(9): 1117-1121.
[4] LIANG J, LI J, LI Z, et al.Differentiating the lung lesions using Intravoxel incoherent motion diffusion-weighted imaging: a meta-analysis[J]. BMC cancer, 2020, 20(1): 799.
[5] LIU H, ZHENG L, SHI G, et al.Pulmonary functional imaging for lung adenocarcinoma: combined MRI assessment based on IVIM-DWI and OE-UTE-MRI[J]. Front Oncol, 2021, 11: 677942.
[6] WAN Q, DENG Y, LEI Q, et al.Differentiating between malignant and benign solid solitary pulmonary lesions: are intravoxel incoherent motion and diffusion kurtosis imaging superior to conventional diffusion-weighted imaging?[J]. Eur Radiol, 2019, 29(3): 1607-1615.
[7] ZHENG Y, HUANG W J, HAN N, et al.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[J]. Clin Radiol, 2023, 78(3): e243-e250.
[8] KUNIMATSU N, KUNIMATSU A, MIURA K, et al.Differentiation between pleomorphic adenoma and schwannoma in the parapharyngeal space: histogram analysis of apparent diffusion coefficient[J]. Dentomaxillofac Radiol, 2023, 52(7): 20230140.
[9] DING Y, TAN Q, MAO W, et al.Differentiating between malignant and benign renal tumors: do IVIM and diffusion kurtosis imaging perform better than DWI?[J]. Eur Radiol, 2019, 29(12): 6930-6939.
[10] JIANG J, FU Y, HU X, et al.The value of diffusion-weighted imaging based on monoexponential and biexponential models for the diagnosis of benign and malignant lung nodules and masses[J]. Br J Radiol, 2020, 93(1110): 20190400.
[11] WANG Y, WAN Q, XIA X, et al.Value of radiomics model based on multi-parametric magnetic resonance imaging in predicting epidermal growth factor receptor mutation status in patients with lung adenocarcinoma[J]. J Thorac Dis, 2021, 13(6): 3497-3508.
[12] FENG H, SHI G, LIU H, et al.Free-breathing radial volumetric interpolated breath-hold examination sequence and dynamic contrast-enhanced MRI combined with diffusion-weighted imaging for assessment of solitary pulmonary nodules[J]. Magn Reson Imaging, 2021, 75: 100-106.
[13] BAO X, BIAN D, YANG X, et al.Multiparametric MRI for evaluation of pathological response to the neoadjuvant chemo-immunotherapy in resectable non-small-cell lung cancer[J]. Eur Radiol, 2023, 33(12): 9182-9193.
[14] OHKI K, IGARASHI T, ASHIDA H, et al.Usefulness of texture analysis for grading pancreatic neuroendocrine tumors on contrast-enhanced computed tomography and apparent diffusion coefficient maps[J]. Jpn J Radiol, 2021, 39(1): 66-75.
[15] MEEUS E M, NOVAK J, DEHGHANI H, et al.Rapid measurement of intravoxel incoherent motion (IVIM) derived perfusion fraction for clinical magnetic resonance imaging[J]. MAGMA, 2018, 31(2): 269-283.