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

Abstract
Figure/Table
References
Related Citation (5)

Download: PDF (3447 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

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

Received: 24 June 2022

PACS:

R734

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	HAO Yongjie
	DENG Junji
	FENG Kun
	DU Hanwang
	CHEN Wei
	WEI Yao
	CUI Chuanshui

Cite this article:

HAO Yongjie,DENG Junji,FENG Kun等. Application of magnetic resonance apparent diffusion coefficient imaging in differentiating lung cancer from benign lung lesions[J]. HuNan ShiFan DaXue XueBao(YiXueBan), 2024, 21(1): 98-102.

URL:

http://yxb.hunnu.edu.cn/EN/ OR http://yxb.hunnu.edu.cn/EN/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.