分享:
分享到微信朋友圈
X
临床研究
基于全病灶的动态增强MRI强度直方图鉴别肺部炎性结节与肺癌
高叶祺 陆杰 徐海 袁梅 俞同福

Cite this article as: GAO Y Q, LU J, XU H, et al. Differentiating pulmonary inflammatory nodules from lung cancer based on whole-focus dynamic enhanced MRI intensity histogram[J]. Chin J Magn Reson Imaging, 2023, 14(7): 42-48.本文引用格式:高叶祺, 陆杰, 徐海, 等. 基于全病灶的动态增强MRI强度直方图鉴别肺部炎性结节与肺癌[J]. 磁共振成像, 2023, 14(7): 42-48. DOI:10.12015/issn.1674-8034.2023.07.008.


[摘要] 目的 应用动态增强磁共振成像(dynamic contrast-enhancement magnetic resonance imaging, DCE-MRI)强度直方图分析,对肺部直径0.8~3.0 cm的炎性结节与肺癌进行全病灶分析并评估其对二者的鉴别诊断价值。材料与方法 回顾性分析2019年7月至2022年6月共123例经手术或穿刺活检病理以及临床影像随访证实的肺部炎性结节及肺癌患者的DCE-MRI资料,其中肺部炎性结节63例、肺癌60例。使用FireVoxel软件在病灶峰值增强期图像及其剪影图像上逐层手动勾画全病灶感兴趣区(region of interest, ROI),得到3D ROI的信号强度直方图参数,包括最小值、最大值、平均值、中位数、标准差、偏度、峰度、熵值、变异系数(coefficient of variation, CoV)、第10百分位数(P10)、第25百分位数(P25)、第50百分位数(P50)、第75百分位数(P75)、第90百分位数(P90)等,进行组间比较,利用受试者工作特征(receiver operating characteristic, ROC)曲线确定强度直方图参数对于二者的诊断效能。应用logistic回归分析模型得到联合变量(joint variable, JV),利用ROC曲线来确定其诊断效能。结果 峰值增强期图像强度直方图参数中,肺癌的最小值、平均值、中位数、P10及P25均高于炎性结节,而CoV、偏度低于炎性结节,且差异均具有统计学意义(P<0.05)。其中以最小值155.5为阈值的AUC [0.668,95%置信区间(confidence interval, CI):0.573~0.764]最大,诊断效能最佳,敏感度与特异度分别为35.0%和93.7%。峰值增强期剪影图像强度直方图参数中,肺癌的最小值、P10及P25均高于炎性结节,而CoV、熵值低于炎性结节,且差异均具有统计学意义(P<0.05)。其中以CoV 0.275为阈值的AUC(0.775,95% CI:0.692~0.858)最大,诊断效能最佳,敏感度与特异度分别为88.9%和58.3%。分析时间-信号强度曲线(time-signal intensity curve, TIC)所衍生的半定量参数包括达峰时间(time to peak, TTP)、对比增强比及曲线斜率,肺癌的TTP比炎性结节短,而曲线斜率大于炎性结节,且差异均具有统计学意义(P<0.05)。TTP以204.2 s为阈值的AUC(0.737,95% CI:0.647~0.828)最大,敏感度与特异度分别为58.7%和85.0%;曲线斜率以1.76为阈值的AUC(0.732,95% CI:0.641~0.822)最大,敏感度与特异度分别为61.7%和82.5%。联合剪影图像的直方图参数与半定量参数得到JV以0.43为阈值的AUC(0.885,95% CI:0.823~0.947)最大,敏感度与特异度分别为88.3%和79.4%。结论 基于全病灶的DCE-MRI强度直方图可以为肺部炎性结节与肺癌的鉴别诊断提供信息,且剪影图像的诊断效能更高,联合应用直方图参数与TIC衍生的半定量参数可进一步提高对二者的鉴别能力,为鉴别诊断提供可靠的客观依据。
[Abstract] Objective Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) intensity histogram analysis was used to analyze the whole focus of pulmonary inflammatory nodules and lung cancer which diameter 0.8-3.0 cm and to evaluate their value in differential diagnosis.Materials and Methods The DCE-MRI data of 123 patients with pulmonary inflammatory nodules and lung cancer confirmed by operation or puncture biopsy and clinical imaging follow-up from July 2019 to June 2022 were analyzed retrospectively, including 63 cases of pulmonary inflammatory nodules and 60 cases of lung cancer. Using FireVoxel software, the region of interest (ROI) of the whole lesion was manually delineated layer by layer on the peak enhancement image and its silhouette image, and the signal intensity histogram parameters of 3D ROI were obtained. Including minimum, maximum, average, median, standard deviation, skewness, kurtosis, entropy, coefficient of variation (CoV), 10th percentile (P10), 25th percentile (P25), 50th percentile (P50), 75th percentile (P75), 90th percentile (P90), etc. The diagnostic ability of intensity histogram parameters for both groups was determined by using thereceiver operating characteristic (ROC) curve. The joint variable (JV) was obtained by logistic regression analysis model, and the diagnostic ability was determined by ROC curve.Results In the histogram parameters of peak enhancement, the minimum, mean, median, P10 and P25 of lung cancer were higher than those of inflammatory nodules, while the CoV and skewness of lung cancer were lower than those of inflammatory nodules, and the difference was statistically significant. Among them, the AUC [0.668, 95% confidence interval (CI): 0.573-0.764] with the minimum value of 155.5 as the threshold is the largest, the diagnostic efficiency is the best, and the sensitivity and specificity are 35.0% and 93.7%. In the histogram parameters of peak enhancement, the minimum, P10 and P25 of lung cancer were higher than those of inflammatory nodules, while the values of CoV and entropy were lower than those of inflammatory nodules, and the difference was statistically significant. Among them, AUC (0.775, 95% CI: 0.692-0.858) with CoV 0.275 as the threshold was the largest, and the diagnostic efficiency was the best, with a sensitivity and specificity of 88.9% and 58.3%. The semi-quantitative parameters derived from time-signal intensity curve (TIC) included time to peak (TTP), contrast enhancement ratio and curve slope. The TTP of lung cancer was shorter than that of inflammatory nodules, but the slope of curve was larger than that of inflammatory nodules, and the difference was statistically significant. The AUC (0.737, 95% CI: 0.647-0.828) of TTP with a threshold of 204.2 seconds was the largest, with a sensitivity and specificity of 58.7% and 85.0%, and AUC (0.732, 95% CI: 0.641-0.822) with a slope of 1.76 was the largest, with a sensitivity and specificity of 61.7% and 82.5%. The combination of histogram parameters and semi-quantitative parameters of the silhouette showed that AUC (0.885, 95% CI: 0.823-0.947) with a threshold of JV of 0.43 was the highest, with a sensitivity and specificity of 88.3% and 79.4%.Conclusions DCE-MRI intensity histogram based on whole focus can provide information for differential diagnosis of lung cancer and inflammatory nodules, and the diagnostic efficiency of silhouette images is higher. The combined application of histogram parameters and semi-quantitative parameters derived from TIC can further improve the ability to distinguish between malignant nodules and inflammatory nodules, and provide reliable objective basis for differential diagnosis.
[关键词] 肺癌;肺部炎性结节;动态增强;强度直方图;剪影图像;半定量参数;磁共振成像
[Keywords] lung cancer;pulmonary inflammatory nodules;dynamic enhanced magnetic resonance imaging;intensity histogram;silhouette images;semi-quantitative parameters;magnetic resonance imaging

高叶祺    陆杰    徐海    袁梅    俞同福 *  

南京医科大学第一附属医院放射科,南京 210029

通信作者:俞同福,E-mail:yu.tongfu@163.com

作者贡献声明:俞同福设计本研究的方案,参与资料的分析与解释,并对稿件重要的学术内容进行了修改;高叶祺起草和撰写稿件,获取、分析或解释本研究的数据;陆杰、徐海、袁梅获取、分析或解释本研究的数据,对稿件的重要学术内容进行了修改。全体作者均同意发表最后的修改稿,同意对本研究的所有方面负责,确保本研究的准确性和诚信。


收稿日期:2022-09-19
接受日期:2023-06-25
中图分类号:R445.2  R734.2  R563.1 
文献标识码:A
DOI: 10.12015/issn.1674-8034.2023.07.008
本文引用格式:高叶祺, 陆杰, 徐海, 等. 基于全病灶的动态增强MRI强度直方图鉴别肺部炎性结节与肺癌[J]. 磁共振成像, 2023, 14(7): 42-48. DOI:10.12015/issn.1674-8034.2023.07.008.

0 前言

       根据2020全球癌症统计数据,肺癌位居癌症死亡率的首位,占总体癌症死亡的18.0%[1],早期发现和早期治疗能明显降低肺癌的病死率。目前肺癌筛查主要采用低剂量螺旋CT[2],多项随访研究表明恶性实性结节较纯磨玻璃结节及部分实性结节进展明显迅速[3, 4],说明实性结节恶性程度较高,需要引起高度重视。对CT发现的可疑8 mm以上的实性结节可以通过正电子发射断层扫描/CT(positron emission tomography/CT, PET/CT)进行诊断。但是在一项大样本量的Meta分析中,PET/CT对可疑病变的假阳性率平均为25%[5],活动性感染比如分枝杆菌感染、真菌感染等会在肺内代谢葡萄糖并积聚18F脱氧葡萄糖,从而导致假阳性[6]。且PET/CT辐射剂量较高,不适用于长期随访。在临床实际工作中,相当一部分患者肺部实性结节的定性诊断仍难以判读,术前鉴别肺癌与其他良性病变对于选择治疗方案具有重要意义。

       随着MRI硬件及软件技术的飞速发展,高场强MRI开始应用于临床,通过呼吸门控、心电门控技术减少因呼吸及心脏搏动产生的运动伪影;磁共振平面回波成像技术与并行采集技术的应用使成像速度明显提高,图像信噪比也随之提高,从而得到清晰的解剖图像,可以与胸部CT图像相媲美。既往研究表明,MRI检出直径>8 mm以上肺结节的敏感度高达100%,同时,各种呼吸门控在临床中的应用减少了运动和呼吸伪影,且自由呼吸状态下的Star VIBE序列对肺结节的形态特征显示明显优于屏气3D-VIBE序列[7]。利用MRI对肺部结节进行评估作为一种新兴的诊断技术得到广泛重视,其较传统CT检查具有显著的优点,首先由于MRI原理为利用磁共振现象从人体中获得电磁信号重建出人体信息,用于成像的磁共振信号直接来自于物体本身,避免了CT电离辐射带来的危害。其次,在传统MRI技术的基础上,随着弥散加权成像及动态对比增强(dynamic contrast-enhancement, DCE)的研发[8, 9],赋予了肺部结节功能成像的能力,对于鉴别肺部实性结节的良恶性起到关键作用。

       DCE-MRI可以通过病灶的血供特点及强化方式对病灶进行定性诊断[10]。对于良恶性肺结节的鉴别,DCE-MRI的药代动力学分析可以获得与PET/CT相同的鉴别诊断效能,并且DCE-MRI具有无电离辐射和成本效益高的优点[11]。直方图分析是一种基于图像像素灰度分布的分析方法,可以获得反映肿瘤异质性的多个直方图参数,观察肿瘤微环境的变化[12]。目前全病灶强度直方图分析已被应用于乳腺、口咽部、中枢等部位的疾病诊断、分级与疗效评估中[13, 14, 15, 16],而没有在肺部病变诊断中的相关研究。既往肺部MRI的研究中大多选择直径>3 cm的肺部实性肿块[17, 18],对于直径0.8~3.0 cm的实性肺结节研究较少,且部分研究未将实性结节与肿块进行区分[19, 20],因此本研究旨在通过全病灶强度直方图分析对直径0.8~3.0 cm的肺癌与炎性结节进行鉴别,并探讨其联合DCE-MRI半定量参数是否能提高对于二者的鉴别诊断效能,旨在通过DCE-MRI无创地鉴别肺部炎性结节和肺癌,为临床治疗方法的选择提供影像学依据,减少手术和穿刺活检给患者带来的创伤与经济负担。

1 材料与方法

1.1 研究对象

       本研究为回顾性研究,收集2019年7月至2022年6月南京医科大学第一附属医院经手术或穿刺活检病理以及临床影像随访证实的肺炎性结节及肺癌的患者资料,纳入标准:(1)经手术或穿刺活检病理以及临床影像随访证实的肺部炎性结节及肺癌;(2)具有临床可接受图像质量的标准前处理肺DCE-MRI图像;(3)病灶最大直径为0.8~3.0 cm。排除标准:(1)DCE-MRI成像资料不完整;(2)转移性肺癌。本研究遵守《赫尔辛基宣言》,该方案经过南京医科大学第一附属医院伦理委员会的批准,免除受试者知情同意(批准文号:2022-NT-11)。

1.2 DCE-MRI检查

       所有MRI检查均使用带有16通道线圈的3 T MRI系统(Magnetom Skyra,Siemens Healthcare,德国Erlangen)进行。所有患者均先扫描常规T1WI、T2WI序列,检查范围从胸骨上切迹到膈肌。轴位DCE-MRI采用可以实现患者在自由呼吸状态下的Star VIBE序列。T1WI序列:TR 3.11 ms,TE 1.54 ms,层厚3 mm,FOV 340 mm×340 mm,扫描时间65 s;T2WI序列:TR 4100 ms,TE 98 ms,层厚3 mm,FOV 340 mm×340 mm,扫描时间90 s;Star VIBE序列:TR 5.70 ms,TE 1.26 ms,层厚3 mm,FOV 360 mm×360 mm,扫描时间322.7 s。使用高压注射器经肘静脉注射Gd-二乙烯三胺五乙酸(Magnevist;拜耳先灵制药股份公司,德国柏林),流速为3.0 mL/s,剂量为0.1 mmol/kg,然后以相同的注射速率注射生理盐水20 mL。

1.3 图像处理与分析

       使用西门子工作站中的MEAN-CURVE软件(Erlangen VE31B)对DCE-MRI数据进行图像后处理,绘制病灶的时间-信号强度曲线(time-signal intensity curve, TIC),选取峰值增强期图像及其与平扫期的剪影图像,将图像以DICOM格式导入FireVoxel软件(FireVoxel,v383),逐层手动勾画病灶的感兴趣区(region of interest, ROI),其代表整个病灶的体积,尽量避开病灶内部的坏死、囊变、出血区,同时消除病变周围或内部的大血管。软件将所有层面的ROI累加为一个3D ROI,并自动计算出强度直方图信息,记录直方图参数,包括最小值、最大值、平均值、中位数、标准差、偏度、峰度、熵值、变异系数(coefficient of variation, CoV)、第10百分位数、第25百分位数、第50百分位数、第75百分位数、第90百分位数等(图12),ROI的勾画是由一位有2年经验的胸部放射科住院医师进行的,同时由1名具有5年以上肺结节诊断经验的放射科副主任医师对肺结节进行形态学特征评估和数值测量。提取TIC所衍生的半定量参数,包括达峰时间(time to peak, TTP)、最大增强比及曲线斜率,TTP被定义为注射对比剂后,病灶信号强度达到峰值的时间。

图1  男,59岁,病灶位于右肺上叶,边缘欠光整,最大径约2.6 cm,经穿刺活检病理证实为炎症。1A:病灶增强后呈轻度不均匀强化;1B:剪影图像中结节内血流信号明显增多且分布不均匀;1C:病灶体素强度直方图分布分散且广泛,图1C中横坐标为灰度级,纵坐标为图像中该灰度级出现的次数(频率)。
图2  女,31岁,病灶位于右肺上叶,最大径约2.6 cm,经穿刺活检病理证实为浸润性腺癌。2A:病灶增强后呈轻度强化;2B:剪影图像中结节内血流信号均匀增多;2C:病灶体素强度直方图分布集中,图2C中横坐标为灰度级,纵坐标为图像中该灰度级出现的次数(频率)。
Fig. 1  Male, 59 years old, the lesion is located in the upper lobe of the right lung, the edge is not smooth, and the maximum diameter is about 2.6 cm. Inflammation was confirmed by puncture biopsy. 1A: The lesions showed mild inhomogeneous enhancement; 1B: The blood flow signals in the nodules were significantly increased and unevenly distributed in the silhouette images; 1C: The voxel intensity histograms of the lesions were scattered and widely distributed. In 1C, the horizontal coordinate is the gray level, and the vertical coordinate is the number (frequency) of occurrence of the gray level in the image.
Fig. 2  Female, 31 years old, the lesion is located in the upper lobe of the right lung with a maximum diameter of about 2.6 cm. It is pathologically confirmed as invasive adenocarcinoma by puncture biopsy. 2A: The lesions showed mild enhancement after enhancement; 2B: The blood flow signals in the nodules increased uniformly in the silhouette images; 2C: The distribution of voxel intensity histograms is concentrated. In 2C, the horizontal coordinate is the gray level, and the vertical coordinate is the number (frequency) of occurrence of the gray level in the image.

1.4 统计学方法

       应用社会科学统计软件包SPSS 26.0版进行数据分析。当数据符合正态分布且方差齐性时,以“均数±标准差”表示,采用两独立样本t检验;不符合正态分布者以“中位数±四分位间距”表示,采用Mann-Whitney U检验。对于有统计学意义的直方图参数,利用受试者工作特征(receiver operating characteristic, ROC)曲线来确定各参数对于鉴别诊断肺部炎性结节与肺癌的效能。应用二元logistic回归分析模型得到联合变量(joint variable, JV),利用ROC曲线来确定其诊断能力。所有统计学分析均以P<0.05为差异有统计学意义。

2 结果

2.1 临床特征

       最终入组123例,肺部炎性结节组63例,肺癌组60例。肺部炎性结节组包括炎症28例;结核性肉芽肿18例;肺脓肿9例;隐球菌6例;曲霉菌2例。其中2例经手术病理证实,35例经穿刺活检病理证实,26例治疗后经临床影像随访,病灶明显缩小或消失,证实为肺部炎性结节。结节最大径8~30 mm,平均(18.2±6.3)mm。肺癌组包括腺癌52例;鳞癌6例;大细胞癌1例;小细胞癌1例,其中52例经手术病理证实,8例经穿刺活检病理证实。结节最大径8~30 mm,平均(18.0±5.9)mm。两组间的年龄差异具有统计学意义(P=0.007),而结节最大径与结节位置差异没有统计学意义(P>0.05),详见表1

表1  入组患者临床特征
Tab. 1  Clinical characteristics of the enrolled patients

2.2 肺部炎性结节与肺癌的强度直方图参数分析

       峰值增强期图像强度直方图参数中,肺癌的最小值、平均值、中位数、P10及P25均高于炎性结节,而CoV、偏度低于炎性结节,且差异均具有统计学意义(P<0.05),详见表2

       峰值增强期剪影图像直方图参数中,肺癌的最小值、P10及P25均高于肺部炎性结节,而CoV、熵值低于炎性病变,且差异有统计学意义(P<0.05)。详见表3

表2  峰值增强期图像强度直方图参数
Tab. 2  Intensity histogram parameters in peak enhancement period
表3  峰值增强期剪影图像强度直方图参数
Tab. 3  Intensity histogram parameters of silhouette image during peak enhancement period

2.3 肺部炎性结节与肺癌的半定量参数分析

       肺癌的TTP比肺部炎性结节短,而曲线斜率大于炎性病变,且差异均具有统计学意义(P<0.05),而两组之间的最大增强比差异无统计学意义(P>0.05),详见表4

表4  炎性结节与肺癌半定量参数
Tab. 4  Semi-quantitative parameters of inflammatory nodules and lung cancer

2.4 有统计学意义的参数的诊断效能

       峰值增强期图像强度直方图参数中,以最小值155.5为阈值的AUC [0.668,95% 置信区间(confidence interval, CI):0.573~0.764]最大,诊断效能最佳,敏感度与特异度分别为35.0%和93.7%。峰值增强期剪影图像强度直方图参数中,以CoV 0.275为阈值的AUC(0.775,95% CI:0.692~0.858)最大,诊断效能最佳,敏感度与特异度分别为88.9%和58.3%。半定量参数中,TTP以204.2 s为阈值的AUC(0.737,95% CI:0.647~0.828)最大,敏感度与特异度分别为58.7%和85.0%;曲线斜率以1.76为阈值的AUC(0.732,95% CI:0.641~0.822)最大,敏感度与特异度分别为61.7%和82.5%。联合剪影图的直方图参数与半定量参数得到JV,建立方程如公式(3)所示:

       其中,X1为最小值,X2为CoV,X3为熵值,X4为P10,X5为P25,X6为TTP,X7为曲线斜率。JV以0.43为阈值的AUC(0.885,95% CI:0.823~0.947)最大,敏感度与特异度分别为88.3%和79.4%(图3)。

图3  不同参数条件下ROC曲线。ROC:受试者工作特征;JV:联合变量;AUC:ROC曲线下面积。
Fig. 3  ROC curves under different parameter conditions. ROC: receiver operating characteristic; JV: joint variable;AUC: area under the ROC curve.

3 讨论

       本研究是国内首个应用增强剪影图像强度直方图分析对肺癌与炎性结节进行鉴别的研究,首先在峰值增强期剪影图像中绘制病灶的感兴趣体积(volume of interest, VOI)代表病灶的强化方式与特点,再应用强度直方图逐个分析病变内的体素,建立VOI内的强度分布,并提供有关肿瘤特征的定量信息,如对称性、异质性和一致性等。最后,将剪影图像的直方图参数包括最小值、CoV、熵值、P10、P25与半定量参数包括TTP和曲线斜率进行联合得到JV,JV以0.43为阈值的AUC最大,敏感度与特异度分别为88.3%和79.4%,具有一定的诊断价值。本研究通过DCE-MRI对直径0.8~3.0 cm的实性肺结节进行鉴别诊断,为患者治疗方式的选择提供影像学依据,同时DCE-MRI可以避免CT检查的电离辐射危害和穿刺活检带来的创伤。

3.1 肺结节的DCE-MRI定性分析

       既往研究已经发现DCE-MRI可以通过病灶的强化方式及其内部血供情况对病灶进行定性及定量诊断,JUERGEN等[21]在定性分析孤立性肺结节的动态增强序列后,根据病灶的强化方式与强化程度将TIC曲线分为A~D型:A型为上升-下降型,B型为上升-平台型,C型为持续上升型,D型为持续低平型。A型曲线反映病灶的血供丰富,主要见于恶性肿瘤;B型曲线可见于血供较丰富的良、恶性肿瘤与炎性病变,在良、恶性病变的鉴别中存在明显的交叉;C型呈渐进性或延迟强化,主要见于良性病变;D型曲线仅见于几乎无血管成分的良性病变[10]。但是部分曲线间仍然存在交叉,例如B型曲线在鉴别血供丰富的恶性肿瘤和炎性病变时仍具有局限性,肺癌与炎性结节的鉴别仍存在困难[22]。因此,本研究旨在应用DCE-MRI增强剪影技术及全病灶强度直方图分析为肺部炎性结节与肺癌的鉴别诊断提供新的方法,通过增强剪影图像强度直方图分析对病灶进行分析,避免了原始图像中病灶的信号对分析的影响,更能代表病灶的强化特点与强化方式,为临床中术前鉴别诊断肺部炎性结节和肺癌提供了新的且无创的方法,可以避免误诊导致炎性结节的手术切除以及穿刺活检给患者带来的负担[23, 24, 25]

3.2 肺结节的DCE-MRI定量分析

       随着MRI技术在肺部成像中的发展,有研究使用DCE-MRI半定量参数对实性肺结节进行良恶性鉴别,发现肺癌的TTP较良性结节显著降低,而曲线斜率明显增加[25],这与本研究中的半定量参数部分的结果一致,且相对于定量参数,半定量参数不需要复杂的扫描设置和后处理程序,TIC曲线比定量参数更直观,更易获取,提示半定量DCE-MRI可以取代定量DCE-MRI在临床肺部病变鉴别诊断中的价值,并且在肺癌化疗早期疗效的评估中也有一定价值[25, 26]。但目前没有研究应用增强剪影技术对肺结节进行良恶性鉴别,且既往的研究大多在病灶最大层面选取ROI[27, 28, 29, 30],易导致抽样偏差,而本研究的测量区域包括了全病灶,同时通过增强剪影技术去除了病灶本身的信号,提供了更多关于肿瘤强化特点及异质性的信息,也可以潜在地消除数据处理过程中的采样偏倚。

       本研究logistic回归分析显示,CoV是诊断肺癌的独立预测因子,且与肺癌呈负相关,这可能是因为肺癌与炎性结节相比,病灶内的高灌注区和血供受限的坏死区相对较少,导致体素对比增强的动态范围较小(即较低的CoV),而炎性结节内部由于存在高强化的活动性炎症区与低强化的纤维组织区,导致体素对比增强的动态范围较大(即较高的CoV)[31]。增强剪影图像强度直方图分析的AUC较峰值增强期图像的强度直方图分析略有提升,并通过增强剪影图像强度直方图分析与半定量DCE-MRI进行联合,得到的AUC进一步提升,敏感度与特异度分别为88.3%和79.4%,DCE-MRI对于肺癌和炎性结节的鉴别诊断具有一定价值,有待进一步研究。

3.3 局限性

       本研究的局限性在于:(1)样本量相对较少,后期可增大样本量进行更系统的分析;(2)病灶的VOI由人工逐层勾画,容易导致误差,在未来的研究中应考虑使用自动分割算法;(3)MRI扫描时间较长,可能带来呼吸运动伪影,影响图像质量;(4)属于回顾性研究,研究结果需要进一步进行前瞻性研究进行验证。

4 结论

       DCE-MRI具有无创、高分辨率、无电离辐射等优势,适用范围更广,同时减少了患者多次随访所带来的辐射累积,随着技术的发展,肺部MRI在肺部疾病的诊断中具有广阔的应用前景。基于全病灶的DCE-MRI强度直方图可以用于肺癌与炎性结节的鉴别诊断,且剪影图像的诊断效能比增强图像更高,联合应用剪影图像直方图参数与DCE-MRI半定量参数可进一步提高对两者的鉴别诊断效能,为术前诊断与治疗方法的选择提供可靠的客观依据。

[1]
曹毛毛, 陈万青. GLOBOCAN 2020全球癌症统计数据解读[J/OL]. 中国医学前沿杂志(电子版), 2021, 13(3): 63-69 [2022-07-20]. https://kns.cnki.net/kcms/detail/detail.aspx?FileName=YXQY202103010&DbName=CJFQ2021. DOI: 10.12037/YXQY.2021.03-10.
CAO M M, CHEN W Q. Interpretation on the global cancer statistics of GLOBOCAN 2020[J/OL]. Chin J Front Med Sci (Electronic Version), 2021, 13(3): 63-69 [2022-09-18]. https://kns.cnki.net/kcms/detail/detail.aspx?FileName=YXQY202103010&DbName=CJFQ2021. DOI: 10.12037/YXQY.2021.03-10.
[2]
王利平, 段颖佳, 候箭, 等. 低剂量胸部CT筛查早期肺癌的临床应用研究[J]. 现代肿瘤医学, 2021, 29(3): 407-409. DOI: 10.3969/j.issn.1672-4992.2021.03.009.
WANG L P, DUAN Y J, HOU J, et al. Clinical study of low-dose chest CT plain scan in screening early lung cancer[J]. J Mod Oncol, 2021, 29(3): 407-409. DOI: 10.3969/j.issn.1672-4992.2021.03.009.
[3]
HEUVELMANS M A, OUDKERK M, DE BOCK G H, et al. Optimisation of volume-doubling time cutoff for fast-growing lung nodules in CT lung cancer screening reduces false-positive referrals[J]. Eur Radiol, 2013, 23(7): 1836-1845. DOI: 10.1007/s00330-013-2799-9.
[4]
MACMAHON H, AUSTIN J H M, GAMSU G, et al. Guidelines for management of small pulmonary nodules detected on CT scans: a statement from the Fleischner Society[J]. Radiology, 2005, 237(2): 395-400. DOI: 10.1148/radiol.2372041887.
[5]
DEPPEN S A, BLUME J D, KENSINGER C D, et al. Accuracy of FDG-PET to diagnose lung cancer in areas with infectious lung disease: a meta-analysis[J]. JAMA, 2014, 312(12): 1227-1236. DOI: 10.1001/jama.2014.11488.
[6]
MAIGA A W, DEPPEN S A, MERCALDO S F, et al. Assessment of fluorodeoxyglucose F18-labeled positron emission tomography for diagnosis of high-risk lung nodules[J]. JAMA Surg, 2018, 153(4): 329-334. DOI: 10.1001/jamasurg.2017.4495.
[7]
任占丽, 贺太平, 杨创勃, 等. 磁共振3D-VIBE序列和STAR-VIBE序列对肺结节显示能力的比较研究[J]. 磁共振成像, 2019, 10(1)14-17. DOI: 10.12015/issn.1674-8034.2019.01.003
REN Z L, HE T P, YANG C B, et al. Comparative study of magnetic resonance imaging with 3D-VIBE sequence and STAR-VIBE sequence on pulmonary nodule[J]. Chin J Magn Reson Imaging, 2019, 10(1)14-17. DOI: 10.12015/issn.1674-8034.2019.01.003
[8]
KOYAMA H, OHNO Y, AOYAMA N, et al. Comparison of STIR turbo SE imaging and diffusion-weighted imaging of the lung: capability for detection and subtype classification of pulmonary adenocarcinomas[J]. Eur Radiol, 2010, 20(4): 790-800. DOI: 10.1007/s00330-009-1615-z.
[9]
REGIER M, SCHWARZ D, HENES F O, et al. Diffusion-weighted MR-imaging for the detection of pulmonary nodules at 1.5 Tesla: Intraindividual comparison with multidetector computed tomography[J]. J Med Imaging Radiat Oncol, 2011, 55(3): 266-274. DOI: 10.1111/j.1754-9485.2011.02263.x.
[10]
丁茜琳, 金观桥, 赵阳. 肺孤立性病灶的功能MRI诊断研究进展[J]. 实用放射学杂志, 2020, 36(1): 151-153, 170. DOI: 10.3969/j.issn.1002-1671.2020.01.038
DING Q L, JIN G Q, ZHAO Y. Progress in fMRI diagnosis of solitary pulmonary lesions[J]. J Pract Radiol, 2020, 36(1): 151-153, 170. DOI: 10.3969/j.issn.1002-1671.2020.01.038
[11]
FENG F, QIANG F L, SHEN A J, et al. Dynamic contrast-enhanced MRI versus 18F-FDG PET/CT: which is better in differentiation between malignant and benign solitary pulmonary nodules?[J]. Chin J Cancer Res, 2018, 30(1): 21-30. DOI: 10.21147/j.issn.1000-9604.2018.01.03.
[12]
JUST N. Improving tumour heterogeneity MRI assessment with histograms[J]. Br J Cancer, 2014, 111(12): 2205-2213. DOI: 10.1038/bjc.2014.512.
[13]
黄裕存, 黄胜福, 陆少范, 等. 基于MRI增强后T1WI直方图分析鉴别原发鼻咽淋巴瘤和鼻咽癌[J]. 中国医学影像学杂志, 2020, 28(3)194-196. DOI: 10.3969/j.issn.1005-5185.2020.03.009
HUANG Y C, HUANG S F, LU S F, et al. Differentiation between primary nasopharyngeal lymphoma and nasopharyngeal carcinoma based on T1WI histogram analysis after MRI enhancement[J]. Chin J Med Imaging, 2020, 28(3)194-196. DOI: 10.3969/j.issn.1005-5185.2020.03.009
[14]
韩亮, 苗延巍, 董俊伊, 等. 基于肿瘤全域的常规MRI参数直方图分析对弥漫大B淋巴瘤与胶质母细胞瘤的鉴别[J]. 中国临床医学影像杂志, 2018, 29(12): 837-843. DOI: 10.12117/jccmi.2018.12.001.
HAN L, MIAO Y W, DONG J Y, et al. Histogram analysis of conventional MRI parameters for differentiating glioblastoma from large B cell lymphoma based on whole tumor measurement[J]. J China Clin Med Imaging, 2018, 29(12): 837-843. DOI: 10.12117/jccmi.2018.12.001.
[15]
李晓欣, 苗延巍, 郭妍, 等. 基于肿瘤全域MRI信号强度的直方图分析分级诊断脑膜瘤[J]. 中国医学影像技术, 2018, 34(8): 1143-1147. DOI: 10.13929/j.1003-3289.201712114.
LI X X, MIAO Y W, GUO Y, et al. Grading diagnosis of meningiomas with histogram analysis of MRI based on whole tumor volume measurement[J]. Chin J Med Imaging Technol, 2018, 34(8): 1143-1147. DOI: 10.13929/j.1003-3289.201712114.
[16]
黄婧潇, 吴朋, 孙静宜, 等. DCE-MRI定量参数全域直方图分析法在乳腺肿瘤鉴别诊断中的应用价值[J]. 山西医科大学学报, 2019, 50(3): 347-353. DOI: 10.13753/j.issn.1007-6611.2019.03.019.
HUANG J X, WU P, SUN J Y, et al. Value of global histogram analysis of quantitative parameters of DCE-MRI in differential diagnosis of breast tumors[J]. J Shanxi Med Univ, 2019, 50(3): 347-353. DOI: 10.13753/j.issn.1007-6611.2019.03.019.
[17]
耿广, 李臻, 吴欣娟, 等. 全病变表观扩散系数直方图对肺部孤立性实性病变的鉴别诊断[J]. 中国医学影像学杂志, 2020, 28(7): 513-516, 519. DOI: 10.3969/j.issn.1005-5185.2020.07.007.
GENG G, LI Z, WU X J, et al. Histogram of whole-lesion apparent diffusion coefficient for the differential diagnosis of solitary solid lesions in the lung[J]. Chin J Med Imaging, 2020, 28(7): 513-516, 519. DOI: 10.3969/j.issn.1005-5185.2020.07.007.
[18]
WEBER J P D, SPIRO J E, SCHEFFLER M, et al. Reproducibility of dynamic contrast enhanced MRI derived transfer coefficient Ktrans in lung cancer[J/OL]. PLoS One, 2022, 17(3): e0265056 [2022-09-18]. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0265056. DOI: 10.1371/journal.pone.0265056.
[19]
阎辉, 杨永雁, 宋琨, 等. 磁共弥散加权成像与动态增强扫描在肺癌临床分期中联合诊断价值分析[J]. 中国CT和MRI杂志, 2020, 18(4): 39-42. DOI: 10.3969/j.issn.1672-5131.2020.04.013.
YAN H, YANG Y Y, SONG K, et al. Analysis of combined diagnostic value of magnetic diffusion-weighted imaging and dynamic enhanced scanning in clinical staging of lung cancer[J]. Chin J CT MRI, 2020, 18(4): 39-42. DOI: 10.3969/j.issn.1672-5131.2020.04.013.
[20]
杨安妮, 黎泳娥, 洪荣臻. DCE-MRI联合CA125、CA153对肺部良恶性肿块/结节鉴别效能及意义[J]. 中国CT和MRI杂志, 2022, 20(5): 57-60. DOI: 10.3969/j.issn.1672-5131.2022.05.020.
YANG A N, LI Y E, HONG R Z. The efficacy and significance of DCEMRI combined with CA125 and CA153 in distinguishing benign and malignant masses/nodules in the lungs[J]. Chin J CT MRI, 2022, 20(5): 57-60. DOI: 10.3969/j.issn.1672-5131.2022.05.020.
[21]
SCHAEFER J F, SCHNEIDER V, VOLLMAR J, et al. Solitary pulmonary nodules: association between signal characteristics in dynamic contrast enhanced MRI and tumor angiogenesis[J]. Lung Cancer, 2006, 53(1): 39-49. DOI: 10.1016/j.lungcan.2006.03.010.
[22]
ZHOU S C, WANG Y J, AI T, et al. Diagnosis of solitary pulmonary lesions with intravoxel incoherent motion diffusion-weighted MRI and semi-quantitative dynamic contrast-enhanced MRI[J/OL]. Clin Radiol, 2019, 74(5): 409.e7-409409.e16 [2022-07-20]. https://pubmed.ncbi.nlm.nih.gov/30795843/. DOI: 10.1016/j.crad.2018.12.022.
[23]
傅奕铖, 余烨, 陈杏彪, 等. 双层探测器光谱CT鉴别诊断肺癌与炎性结节的价值[J]. 中华放射学杂志, 2021, 55(12): 1264-1269. DOI: 10.3760/cma.j.cn112149-20210125-00061
FU Y C, YU Y, CHEN X B, et al. Value of dual-layer spectral detector CT in differentiating the diagnosis of lung cancer and inflammatory nodules[J]. Chin J Radiol, 2021, 55(12): 1264-1269. DOI: 10.3760/cma.j.cn112149-20210125-00061
[24]
MACMAHON H, AUSTIN J H M, GAMSU G, et al. Guidelines for management of small pulmonary nodules detected on CT scans: a statement from the fleischner society[J]. Radiology, 2005, 237(2): 395-400. DOI: 10.1148/radiol.2372041887.
[25]
MACMAHON H, NAIDICH D P, GOO J M, et al. Guidelines for management of incidental pulmonary nodules detected on CT images: from the fleischner society 2017[J]. Radiology, 2017, 284(1): 228-243. DOI: 10.1148/radiol.2017161659.
[26]
ZHU J, YUN J, WANG K X, et al. Assessment of early response to lung cancer chemotherapy by semiquantitative analysis of dynamic contrast-enhanced MRI[J]. Dis Markers, 2022, 2022: 1-7. DOI: 10.1155/2022/2669281.
[27]
YUAN M, ZHONG Y, ZHANG Y D, et al. Volumetric analysis of intravoxel incoherent motion imaging for assessment of solitary pulmonary lesions[J]. Acta Radiol, 2017, 58: 1448-1456. DOI: 10.1177/0284185117698863.
[28]
SUO S T, CHEN X X, FAN Y, et al. Histogram analysis of apparent diffusion coefficient at 3.0 T in urinary bladder lesions[J]. Acad Radiol, 2014, 21(8): 1027-1034. DOI: 10.1016/j.acra.2014.03.004.
[29]
BOZDAĞ M, ER A, ÇINKOOĞLU A. Histogram analysis of ADC maps for differentiating brain metastases from different histological types of lung cancers[J]. Can Assoc Radiol J, 2020, 72: 271-278. DOI: 10.1177/0846537120933837.
[30]
邓颖诗, 魏新华. 肺孤立性病变的ADC直方图的比较研究[J]. 广州医药, 2021, 52(4): 45-50, 64. DOI: 10.3969/j.issn.1000-8535.2021.04.010.
DENG Y S, WEI X H. Diffusion-weighted MR imaging of solid solitary pulmonary lesions: a comparison of ADC histogram[J]. Guangzhou Med J, 2021, 52(4): 45-50, 64. DOI: 10.3969/j.issn.1000-8535.2021.04.010.
[31]
KONO R, FUJIMOTO K, TERASAKI H, et al. Dynamic MRI of solitary pulmonary nodules: comparison of enhancement patterns of malignant and benign small peripheral lung lesions[J]. Am J Roentgenol, 2007, 188(1): 26-36. DOI: 10.2214/ajr.05.1446.

上一篇 基于术前MR影像组学模型预测颅咽管瘤术后5年内复发的研究
下一篇 磁共振体素内不相干运动成像鉴别肝细胞癌与肝内胆管细胞癌的价值
  
诚聘英才 | 广告合作 | 免责声明 | 版权声明
联系电话:010-67113815
京ICP备19028836号-2