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临床研究
基于18F-FDG PET/MR成像和扩散加权成像分数微积分模型对肺腺癌增殖状态评估价值的研究
罗与 孟楠 黄准 魏巍 李自强 付芳芳 袁健闵 王哲 王梅云

Cite this article as: Luo Y, Meng N, Huang Z, et al. The value of monoexponentia, fractional order calculus models and 18F-FDG PET imaging in evaluating the proliferation status of lung adenocarcinoma[J]. Chin J Magn Reson Imaging, 2022, 13(10): 121-126.本文引用格式:罗与, 孟楠, 黄准, 等. 基于18F-FDG PET/MR成像和扩散加权成像分数微积分模型对肺腺癌增殖状态评估价值的研究[J]. 磁共振成像, 2022, 13(10): 121-126. DOI:10.12015/issn.1674-8034.2022.10.018.


[摘要] 目的 探讨基于扩散加权成像(diffusion weighted imaging, DWI)的单指数、分数微积分(fractional order calculus, FROC)模型和氟代脱氧葡萄糖正电子发射断层扫描(18F-fluorodeoxyglucose-positron emission tomography, 18F-FDG PET)在评估肺腺癌增殖状态中的价值。材料与方法 选取经我院病理证实的64例肺腺癌患者,以Ki-67表达25%为界,>25%为高Ki-67组,≤25%为低Ki-67组。所有患者在治疗前均行肺部18F-FDG PET/MR检查,其中DWI采取10个b值(0~1000 s/mm2)扫描。比较两组间表观扩散系数(apparent diffusion coefficient, ADC)、空间变量(a microstructural quantity, μ)、扩散系数(diffusion coefficient, D)、分数空间导数(fractional order parameter, β)、最大标准摄取值(maximum standardized uptake value, SUVmax)有无显著差异。通过多因素logistic回归分析Ki-67增殖状态的独立预测因素,采用受试者工作特征曲线(receiver operating characteristic curve, ROC)评估鉴别效能,并分析各参数与Ki-67之间的相关性。结果 低Ki-67组的ADC、D、β显著大于高Ki-67组(P<0.05),高Ki-67组的μ、SUVmax显著大于低Ki-67组(P<0.05)。参数D和SUVmax曲线下面积分别为0.873和0.727,且多因素logistic回归显示D值(OR:0.421,95% CI:0.245~0.723,P=0.002)和SUVmax值(OR:1.022,95% CI:1.002~1.042,P=0.031)是Ki-67高表达的独立危险因素。ADC值和D值与Ki-67呈负相关(r=-0.361,r=-0.420),μ和SUVmax值与Ki-67呈正相关(r=0.369,r=0.527)。结论 单指数、FROC模型和18F-FDG PET均是评估肺腺癌增殖状态的有效手段,其中FROC模型的D值具有最高的诊断效能。FROC模型为探索肿瘤组织微环境信息提供了新的视角,在无创评估肺腺癌增殖状态方面具有很大潜力,其临床应用前景广阔。
[Abstract] Objective To explore the value of monoexponential, fractional order calculus (FROC) models based on diffusion weighted imaging (DWI) and 18F-fluorodeoxyglucose-positron emission tomography (18F-FDG PET) in assessing the proliferation status of lung adenocarcinoma.Materials and Methods A total of 64 patients with lung adenocarcinoma confirmed by pathology in our hospital were included. The expression of Ki-67 in lung cancer tissues was detected by immunohistochemistry and divided into the high Ki-67 group (>25%) and the low Ki-67 group (≤25%). Before treatment, all patients underwent a dedicated thoracic 18F-FDG PET/MR examination. The DWI was scanned with 10 b-values (0-1000 s/mm2). The apparent diffusion coefficient (ADC), a microstructural quantity (μ), diffusion coefficient (D), fractional order parameter (β) and maximum standardized uptake value (SUVmax) were compared between the two groups. The independent predictors of Ki-67 proliferative status were analyzed by multivariate logistic regression, receiver operating characteristic (ROC) curve was used to evaluate the discriminant performance, and the correlation between each parameter and Ki-67 was analyzed.Results The ADC, D, and β values in the low Ki-67 group were significantly higher than in the high Ki-67 group (P<0.05), and the μ and SUVmax values in the high Ki-67 group were significantly higher than in the low Ki-67 group (P<0.05). The area under the curve (AUC) of parameters D and SUVmax were 0.873 and 0.727, respectively, and multivariate logistic regression showed that parameters D (OR: 0.421, 95% CI: 0.245-0.723, P=0.002) and SUVmax (OR: 1.022, 95% CI: 1.002-1.042, P=0.031) were independent risk factors for high Ki-67 expression. ADC and D values were negatively correlated with Ki-67 (r=-0.361, r=-0.420), and μ and SUVmax values were positively correlated with Ki-67 (r=0.369, r=0.527).Conclusions Monoexponential, FROC models and 18F-FDG PET are effective methods to evaluate the proliferation status of lung adenocarcinoma, and the D value of FROC model shows the highest diagnostic performance. FROC model provides a new perspective for exploring the information of tumor tissue microenvironment, and has great potential in non-invasive evaluation of lung adenocarcinoma proliferation, and its clinical application has broad prospects.
[关键词] 肺腺癌;Ki-67;单指数;分数微积分模型;氟代脱氧葡萄糖正电子发射断层扫描;磁共振成像;鉴别诊断
[Keywords] lung adenocarcinoma;Ki-67;monoexponential;fractional order calculus model;18F-fluorodeoxyglucose-positron emission tomography;magnetic resonance imaging;differential diagnosis

罗与 1, 2   孟楠 1, 2   黄准 3   魏巍 2   李自强 4   付芳芳 2   袁健闵 5   王哲 5   王梅云 2*  

1 郑州大学人民医院医学影像科,郑州 450000

2 河南省人民医院医学影像科,郑州 450000

3 河南大学人民医院医学影像科,郑州 450000

4 新乡医学院人民医院影像科,郑州 450000

5 上海联影医疗科技股份有限公司中央研究院,上海 201807

王梅云,E-mail:mywang@zzu.edu.cn

作者利益冲突声明:全部作者均声明无利益冲突。


基金项目: 河南省科技攻关项目 212102310689 河南省医学科技攻关计划联合共建项目 LHGJ20210001
收稿日期:2022-04-11
接受日期:2022-10-09
中图分类号:R445.2  R734.2 
文献标识码:A
DOI: 10.12015/issn.1674-8034.2022.10.018
本文引用格式:罗与, 孟楠, 黄准, 等. 基于18F-FDG PET/MR成像和扩散加权成像分数微积分模型对肺腺癌增殖状态评估价值的研究[J]. 磁共振成像, 2022, 13(10): 121-126. DOI:10.12015/issn.1674-8034.2022.10.018

       肺癌是全球最常见的恶性肿瘤,也是癌症相关死亡的主要原因,其中腺癌是最常见的肺癌病理类型,表现出显著的病理异质性和多变的预后[1, 2]。Ki-67是反映细胞增殖状态的重要指标,已被用作预测肺癌患者预后的独立生物学标志物[3, 4],有研究表明Ki-67高表达与肺癌患者的不良预后和疾病进展相关[5]。对Ki-67的准确评估在临床决策和预后监测方面具有重要意义,临床上往往通过手术或活检等有创的方式来评估肺癌患者的增殖状态,但具有患者依从性差、不能获取病灶整体情况、并发症发生率高等缺点,因此对Ki-67的无创评估非常重要。扩散加权成像(diffusion weighted imaging, DWI)利用了生物组织中水分子的布朗运动,其表观扩散系数(apparent diffusion coefficient, ADC)是水分子在生物组织中扩散的定量参数[6]。近年来,有多项研究表明ADC值与各种肿瘤中Ki-67表达有关[7, 8, 9, 10]。分数微积分(fractional order calculus, FROC)模型在2010年被提出,能对水分子在不同组织内扩散速度、扩散距离以及组织结构均匀性进行量化分析,揭示肿瘤组织内部微观结构的复杂性[11]。FROC模型目前已在儿童脑肿瘤、前列腺癌、唾液腺肿瘤、肝纤维化以及肺肿瘤的鉴别诊断、分期、预测侵袭性以及预后等方面成为研究热点[12, 13, 14, 15, 16, 17]。正电子发射计算机断层扫描(positron emission tomography, PET)/MR可以同时获取代谢信息和多参数MRI信息,在肿瘤学研究中有巨大的应用潜力[2],多模态医学影像融合技术为临床疾病的精准诊断和治疗提供强有力的技术支持。然而,目前鲜有研究对比不同扩散模型以及氟代脱氧葡萄糖正电子发射断层扫描(18F-fluorodeoxyglucose-PET, 18F-FDG PET)在识别肺腺癌Ki-67指数中的诊断效用。本研究旨在比较单指数、FROC模型和PET成像对肺腺癌Ki-67表达水平进行无创评估的效能,以提高治疗前诊断准确性,从而指导治疗方案决策。

1 材料与方法

1.1 研究对象

       本前瞻性研究遵守《赫尔辛基宣言》,获得了河南省人民医院伦理委员会的批准(批准文号:2020116),每位患者均签署书面知情同意书。前瞻性纳入2020年7月至2022年1月于河南省人民医院就诊疑似肺肿瘤并接受了PET/MR扫描检查的患者。纳入标准:(1)胸部CT检查发现肺部肿瘤最大直径≥1.0 cm;(2)所有患者均未接受任何相关治疗;(3)经病理证实的肺腺癌患者;(4)经免疫组化检测Ki-67的表达。排除标准:(1)检查时间较长患者无法耐受导致MRI图像质量差;(2)临床资料不完整。最终纳入64例肺腺癌患者。

1.2 仪器与方法

       所有检查均在3.0 T PET/MR(uPMR790,上海联影医疗科技股份有限公司,上海)扫描仪上进行,采用12通道相控阵体线圈。所用的示踪剂是18F-FDG。嘱患者空腹6 h以上,控制血糖水平<8.0 mmol/L。静脉注射显像剂18F-FDG 4.07 mBq/kg,1 h后开始采集全肺图像,扫描范围为从肺尖到膈角。使用腹部束带以减少呼吸运动伪影。图像经衰减校正后采用迭代法进行图像重建。在扫描PET的同时,进行以下MRI序列扫描:MR-base衰减校正、T2加权成像(重复时间、回波时间、层厚、层间距、视野、矩阵、扫描时间等扫描参数分别为3315 ms、90.2 ms、5 mm、20 mm、400 mm×300 mm、320×70、2.26 min)、T1加权成像(重复时间、回波时间、层厚、层间距、视野、矩阵、扫描时间等扫描参数分别为4.24 ms、1.13 ms、6 mm、0 mm、400 mm×300 mm、320×70、14 min)、和多b值 DWI(b=0、25、50、100、150、200、400、600、800和1000 s/mm2;重复时间、回波时间、层厚、层间距、视野、矩阵、扫描时间等扫描参数分别为1620 ms、69.6 ms、5 mm、20 mm、400 mm×300 mm、128×100、5.15 min)。

1.3 图像分析与测量

       所有图像被导入联影成像工作站(uWS-MR,UIH)进行后处理。两位放射科医生(分别有5年和10年的工作经验)独立测量了SUVmax,并在轴位T2加权像中测量病灶最大径,两位医生对临床资料和病理结果均不知情。后处理软件自动勾画出病变的感兴趣体积(volume of interest, VOI),并计算出40%相对阈值的SUVmax。参考T1和T2加权像,避免囊性成分、坏死区、出血和钙化,在DWI图像上手工绘制感兴趣区域(regione of interest, ROI),分别计算出对应区的ADC、空间变量μ、扩散系数D、分数空间导数值β。单指数模型的信号强度公式为Sb/S0=exp(-b×ADC);FROC模型的信号强度公式为Sb/S0=exp-Dμ2(β-1)(γGdδ)2β(Δ-2β-12β+1δ),其中Gd为扩散梯度振幅,δ为扩散梯度脉冲宽度,Δ为梯度分隔,γ为自旋比,Δ=23.96 ms,δ=17.36 ms。单指数和FROC模型均在Matlab软件上分析处理,DWI、FROC和18F-FDG PET各参数伪彩图见图12

图1  男,60岁,左肺上叶腺癌。1A:T2WI表现为左肺上叶高信号肿块(箭);1B:免疫组织化学(HC ×200)示CK7(+),CK5/6(-),TTF-1(+),Napsin A(+),P40(-),Ki-67(约5%+);1C:μ伪彩图,μ=5.98 μm;1D:D伪彩图,D=1.49×10-3 mm2/s;1E:β伪彩图,β=0.63;1F:ADC伪彩图,ADC=1.25×10-3 mm2/s;1G:PET/MR融合图,SUVmax=7.17。μ为空间变量;D为扩散系数;β为分数空间导数;ADC为表观扩散系数;SUVmax为最大标准摄取值。
Fig. 1  Male, 60 years old, lung adenocarcinoma, located in the upper lobe of the left lung. 1A: T2WI image shows a high-signal mass in the upper lobe of the left lung (arrow); 1B: Immunohistochemistry(HC ×200)shows CK7(+),CK5/6(-),TTF-1(+),Napsin A(+),P40(-),Ki-67(about 5%+); 1C: μ pseudo-color map, μ=5.98 μm; 1D: D pseudo-color map, D=1.49×10-3 mm2/s; 1E: β pseudo-color map, β=0.63; 1F: ADC pseudo-color map, ADC=1.25×10-3 mm2/s; 1G: PET/MR fusion map, SUVmax =7.17. μ: microstructural quantity; D: diffusion coefficient; β: fractional order parameter; ADC: apparent diffusion coefficient; SUVmax: maximum standard uptake value.
图2  男,67岁,右肺下叶腺癌。2A:T2WI表现为右肺下叶高信号肿块(箭);2B:免疫组织化学(HC ×200)示CK7(+),CK5/6(-),TTF-1(+),Napsin A(+),P40(-),Ki-67(40%+);2C:μ伪彩图,μ=7.53 μm;2D:D伪彩图,D=1.40×10-3 mm2/s;2E:β伪彩图,β=0.82;2F:ADC伪彩图,ADC=1.37×10-3 mm2/s;2G:PET/MR融合图,SUVmax=10.97。μ为空间变量;D为扩散系数;β为分数空间导数;ADC为表观扩散系数;SUVmax为最大标准摄取值。
Fig. 2  Male, 67 years old, lung adenocarcinoma, located in the lower lobe of the right lung. 2A: T2WI image shows a high-signal mass in the lower lobe of the right lung (arrow); 2B: Immunohistochemistry(HC ×200)shows CK7(+),CK5/6(-),TTF-1(+),Napsin A(+),P40(-),Ki-67(40%+); 2C: μ pseudo-color map, μ=7.53 μm; 2D: D pseudo-color map, D=1.40×10-3 mm2/s; 2E: β pseudo-color map, β=0.82; 2F: ADC pseudo-color map, ADC=1.37×10-3 mm2/s; 2G: PET/MR fusion map, SUVmax =10.97. μ: microstructural quantity; D: diffusion coefficient; β: fractional order parameter; ADC: apparent diffusion coefficient; SUVmax: maximum standard uptake value.

1.4 免疫组织化学染色

       所有标本经石蜡包埋、切片、常规HE染色以及采用鼠抗人Ki-67单克隆抗体(MIB-1, DAKO,Denmark)进行免疫组化分析Ki-67表达。光学显微镜下随机选取5个高倍视野(×400),每个视野计数100个细胞。Ki-67阳性表达在胞核,呈棕黄色颗粒,计数并记录染色阳性的肿瘤细胞百分数(%),根据之前的研究,Ki-67>25%被定义为高表达,≤25%为低表达[2,18]

1.5 统计学分析

       使用SPSS 26.0、MedCalc 15.2.2软件进行统计学分析,P<0.05认为差异具有统计学意义。使用组内相关系数(intraclass correlation coefficient, ICC)评价两位放射科医生对两组患者各参数测量结果的一致性,ICC>0.75为一致性良好。对所有计量资料进行正态分布检验,符合正态分布的数据以均数±标准差(x¯±s)表示,两组间比较采用独立样本t检验;不符合正态分布的数据以中位数(四分位数间距)[MIQR)]表示,两组间比较采用秩和检验。将单因素回归分析有统计学差异的参数纳入多因素logistic回归分析中,采用基于最大似然估计的向前逐步回归法(Forward:LR)探讨Ki-67高表达的独立影响因素,受试者工作特征(receiver operating characteristic, ROC)曲线被用来评估鉴别效能,根据最大约登指数确定其诊断阈值、敏感度和特异度。采用Pearson相关分析探讨各参数与Ki-67指数的相关性。

2 结果

2.1 临床一般资料

       共64例肺腺癌患者纳入研究,其中男30例,女34例,年龄33~79(62.0±10.1)岁,低Ki-67组与高Ki-67组在性别、年龄、淋巴结是否转移、是否吸烟以及癌胚抗原方面比较,差异均无统计学意义(P>0.05);在肿瘤最大径方面比较,差异有统计学意义(P<0.05)。详见表1

表1  肺腺癌患者基本信息
Tab. 1  Basic information of patients with lung adenocarcinoma

2.2 一致性分析

       两位放射科医生所测参数ADC、μ、D、β、SUVmax值以及肿瘤最大径均具有较高的一致性,ICC=0.887、0.895、0.855、0.838、0.843、0.868,取两者的平均值作为最终结果纳入研究。

2.3 低Ki-67与高Ki-67组间各参数比较

       低Ki-67组的ADC、D、β值显著大于高Ki-67组(P<0.05),高Ki-67组的μ、SUVmax值显著大于低Ki-67组(P<0.05)。详见表2

表2  低Ki-67与高Ki-67组间各参数比较
Tab. 2  Comparison of parameters between low Ki-67 and high Ki-67 groups

2.4 单因素和多因素logistic回归

       单因素分析显示,两组间参数ADC、μ、D、β、SUVmax(P均<0.05)的差异均有统计学意义。进一步调整混杂因素后,多因素logistic回归分析发现,参数D(OR=0.421,95% CI:0.245~0.723,P=0.002)和SUVmax(OR=1.022,95% CI:1.002~1.042,P=0.031)是Ki-67高表达相关的独立危险因素。见表3

表3  单因素和多因素logistic回归分析结果
Tab. 3  Univariate and multivariate logistic regression analysis results

2.5 各参数诊断效能比较

       ROC曲线(图3)分析结果显示ADC、μ、D、β、SUVmax鉴别低Ki-67与高Ki-67组的曲线下面积分别为0.829、0.844、0.873、0.655、0.727。各参数诊断阈值、约登指数、敏感度和特异度见表4

图3  3A:表观扩散系数(ADC)、空间变量(μ)、扩散系数(D)、分数空间导数(β)、最大标准摄取值(SUVmax)预测低Ki-67与高Ki-67组的受试者工作特征(ROC)曲线;3B:Ki-67与ADC值间的相关性;3C:Ki-67与μ值间的相关性;3D:Ki-67与D值间的相关性;3E:Ki-67与SUVmax值间的相关性。
Fig. 3  3A: Apparent diffusion coefficient (ADC), microstructural quantity (μ), diffusion coefficient (D), fractional order parameter (β), maximum standard uptake value (SUVmax) values predict the receiver operating characteristic (ROC) curve of the low Ki-67 and the high Ki-67 groups; 3B: Correlation between Ki-67 and ADC values; 3C: Correlation between Ki-67 and μ values; 3D: Correlation between Ki-67 and D values; 3E: Correlation between Ki-67 and SUVmax values.
表4  ADC、μ、D、β、SUVmax的鉴别效能比较
Tab. 4  Comparison of the discrimination efficacy of parameters ADC, μ, D, β, and SUVmax

2.6 相关性分析

       Pearson相关性分析显示Ki-67与ADC值和D值呈负相关(r=-0.361、-0.420,P=0.003、0.001),与μ值和SUVmax值呈正相关(r=0.369、0.527,P=0.003、<0.001)。β值与Ki-67无相关性(P>0.05)(图3)。

3 讨论

       Ki-67与细胞有丝分裂和细胞周期有关,能反映肿瘤细胞的增殖能力[19, 20]。本研究结果显示,由单指数DWI模型衍生的参数ADC、FROC模型衍生的参数μ、D、β以及PET成像衍生的参数SUVmax均能评估肺腺癌Ki-67的表达水平。ROC曲线显示相比于ADC和SUVmax,参数μ和D的鉴别诊断效能更高。

3.1 DWI在肺腺癌增殖状态中的研究价值

       DWI检查可以无创地用于体内水分子扩散的测量和成像,在微观水平上间接反映组织结构和细胞密度等信息[21]。本研究结果显示低Ki-67组的ADC值显著高于高Ki-67组(P<0.001),这与既往多项肿瘤研究表明ADC与Ki-67表达呈负相关[22, 23, 24, 25]的结果一致。其原因是Ki-67高表达的肿瘤组织更具侵袭性,细胞增殖速度更快,细胞密度更高,细胞外空间减少。因此,水分子的活动受到限制,ADC值降低。

3.2 FROC模型在肺腺癌增殖状态中的研究价值

       单指数模型理论基础是假设水分子在均匀介质中的位移为高斯分布,然而生物组织中复杂的微观结构导致水分子的扩散受到限制,水分子运动早已偏离高斯分布[26, 27]。认识到单指数模型这一局限性,近年来双指数模型、拉伸指数模型、扩散峰度成像模型被先后提出,以提供关于水分子扩散更准确的信息来反映组织微结构的复杂性[28]。2010年,FROC模型被提出,该模型的扩散系数D与ADC相似,在我们的研究中参数D的结果与ADC一致。尽管越来越多的研究证实ADC值在探索肿瘤组织特性、监测治疗反应和预测预后等方面具有一定的潜在价值[29, 30, 31],但其过度简化了复杂生物组织中的异常扩散过程,可能无法充分识别体素内结构异质性。ADC值是由两个b值拟合而成,而FROC模型是将多个b值拟合获得的更精准的D值,ROC曲线分析显示与传统单指数模型得出的ADC相比,D值提高了鉴别诊断效能。参数β反映组织内部结构均一性,与肿瘤微观结构的异质性和复杂性呈负相关。β值的范围在0和1之间,越靠近1表示组织成分越均匀[32]。我们的研究结果显示低Ki-67组的β值高于高Ki-67组(P=0.041),说明Ki-67表达水平越高,组织异质性程度越高。然而ROC曲线结果表明β值的鉴别诊断效能偏低,其原因可能是纳入的样本量比较少或者忽略了肿瘤分化程度的影响。μ值反映水分子的扩散间距,与扩散自由长度呈负相关[17, 33],在我们的研究中高Ki-67组的μ值显著高于低Ki-67组,其原因可能是Ki-67表达水平高的肿瘤组织增殖活性强,细胞生长旺盛,排列更紧密,细胞外间隙减小。

3.3 SUVmax在肺腺癌增殖状态中的研究价值

       SUVmax是18F-FDG PET图像分析评估代谢活动使用最广泛的定量参数[34]。以往有研究表明FDG的摄取可以反映肿瘤组织的代谢活性,而SUVmax被证明与肺腺癌患者的肿瘤有丝分裂和预后相关[35]。细胞过度增殖引起耗氧量增加,从而对葡萄糖的利用需求增加,葡萄糖转运蛋白1(Glut-1)和己糖激酶被激活,导致18F-FDG的摄取增加[2, 36]。但本研究中SUVmax的曲线下面积与特异度较低,其原因可能是SUVmax增高不仅见于恶性肿瘤组织,还见于组织纤维化和炎症反应等良性病理反应。

3.4 Ki-67与各参数值的相关性

       Ki-67是一种与细胞增殖相关的核抗原,与癌细胞的恶性增殖、侵袭、疾病进展以及预后有关[37, 38]。本研究相关性分析显示Ki-67与ADC和D值呈负相关,与μ和SUVmax值呈正相关。分析其原因,随着肿瘤细胞增殖活性的增强,导致细胞密度增高,水分子扩散受限,平均自由扩散长度减少,细胞需氧量和葡萄糖消耗增加,因此ADC和D值降低,μ和SUVmax值增高。

3.5 本研究的局限性

       本研究的不足之处:(1)样本量较小,未比较不同分化程度的肺腺癌各参数之间的差异,需扩大样本量进一步验证;(2)FROC模型中使用b值与在单指数模型中不同,导致信噪比不一样;(3)尽管采用腹部束带,但检查过程中难以避免呼吸伪影。

       综上所述,单指数、FROC模型和PET成像均能评估肺腺癌Ki-67的表达水平,但μ与D值的鉴别效能更高,此外FROC模型还能对水分子扩散速度、组织结构均匀度以及扩散平均自由长度进行量化分析,有助于提高对肿瘤异质性的理解,以及更深入地探讨肿瘤生物学特性,将有潜力成为帮助临床制订患者治疗方案和预测预后的一种新型、无创的影像学标记物。

[1]
Succony L, Rassl DM, Barker AP, et al. Adenocarcinoma spectrum lesions of the lung: Detection, pathology and treatment strategies[J/OL]. Cancer Treat Rev, 2021, 99 [2022-04-10]. https://pubmed.ncbi.nlm.nih.gov/34182217/. DOI: 10.1016/j.ctrv.2021.102237.
[2]
Huang Z, Li X, Wang Z, et al. Application of Simultaneous (18) F-FDG PET With Monoexponential, Biexponential, and Stretched Exponential Model-Based Diffusion-Weighted MR Imaging in Assessing the Proliferation Status of Lung Adenocarcinoma[J/OL]. J Magn Reson Imaging, 2021, 56(1) [2022-04-10]. https://pubmed.ncbi.nlm.nih.gov/34888990/. DOI: 10.1002/jmri.28010.
[3]
Lin L, Cheng J, Tang D, et al. The associations among quantitative spectral CT parameters, Ki-67 expression levels and EGFR mutation status in NSCLC[J/OL]. Sci Rep, 2020, 10(1) [2022-04-10]. https://pubmed.ncbi.nlm.nih.gov/32103127/. DOI: 10.1038/s41598-020-60445-0.
[4]
Liu P, Li H, Liao C, Tang Y, et al. Identification of key genes and biological pathways in Chinese lung cancer population using bioinformatics analysis[J/OL]. PeerJ, 2022, 10 [2022-04-10]. https://pubmed.ncbi.nlm.nih.gov/35178291/. DOI: 10.7717/peerj.12731.
[5]
Wei DM, Chen WJ, Meng RM, et al. Augmented expression of Ki-67 is correlated with clinicopathological characteristics and prognosis for lung cancer patients: an up-dated systematic review and meta-analysis with 108 studies and 14,732 patients[J/OL]. Respir Res, 2018, 19(1) [2022-04-10]. https://pubmed.ncbi.nlm.nih.gov/30103737/. DOI: 10.1186/s12931-018-0843-7.
[6]
Shen G, Lan Y, Zhang K, et al. Comparison of 18F-FDG PET/CT and DWI for detection of mediastinal nodal metastasis in non-small cell lung cancer: A meta-analysis[J/OL]. Plos One, 2017, 12(3) [2022-04-10]. https://pubmed.ncbi.nlm.nih.gov/28253364/. DOI: 10.1371/journal.pone.0173104.
[7]
Juan MW, Yu J, Peng GX, et al. Correlation between DCE-MRI radiomics features and Ki-67 expression in invasive breast cancer[J]. Oncol Lett, 2018, 16(4): 5084-5090. DOI: 10.3892/ol.2018.9271.
[8]
Karaman A, Durur-Subasi I, Alper F, et al. Correlation of diffusion MRI with the Ki-67 index in non-small cell lung cancer[J]. Radiol Oncol, 2015, 49(3): 250-255. DOI: 10.1515/raon-2015-0032.
[9]
Jiang JX, Zhao JL, Zhang Q, et al. Endometrial carcinoma: diffusion-weighted imaging diagnostic accuracy and correlation with Ki-67 expression[J/OL]. Clin Radiol, 2018, 73(4) [2022-04-10]. https://pubmed.ncbi.nlm.nih.gov/29246587/. DOI: 10.1016/j.crad.2017.11.011.
[10]
Li HM, Liu J, Qiang JW, et al. Endometrial Stromal Sarcoma of the Uterus: Magnetic Resonance Imaging Findings Including Apparent Diffusion Coefficient Value and Its Correlation With Ki-67 Expression[J]. Int J Gynecol Cancer, 2017, 27(9): 1877-1887. DOI: 10.1097/IGC.0000000000001114.
[11]
Chen J, Guo Y, Guo Y, et al. Preoperative assessment of microvascular invasion of hepatocellular carcinoma using non-Gaussian diffusion-weighted imaging with a fractional order calculus model: A pilot study[J/OL]. Magn Reson Imaging, 2021 [2022-04-10]. https://pubmed.ncbi.nlm.nih.gov/34506910/. DOI: 10.1016/j.mri.2021.09.003.
[12]
Sui Y, Wang H, Liu G, et al. Differentiation of Low- and High-Grade Pediatric Brain Tumors with High b-Value Diffusion-weighted MR Imaging and a Fractional Order Calculus Model[J]. Radiology, 2015, 277(2): 489-96. DOI: 10.1148/radiol.2015142156.
[13]
Chen W, Zhu LN, Dai YM, et al. Differentiation of salivary gland tumor using diffusion-weighted imaging with a fractional order calculus model[J/OL]. Br J Radiol, 2020, 93(1113) [2022-04-10]. https://pubmed.ncbi.nlm.nih.gov/32649236/. DOI: 10.1259/bjr.20200052.
[14]
Li Z, Dan G, Tammana V, et al. Predicting the aggressiveness of peripheral zone prostate cancer using a fractional order calculus diffusion model[J/OL]. Eur J Radiol, 2021, 143 [2022-04-10]. https://pubmed.ncbi.nlm.nih.gov/34464907/. DOI: 10.1016/j.ejrad.2021.109913.
[15]
Sheng R, Zhang Y, Sun W, et al. Staging Chronic Hepatitis B Related Liver Fibrosis with a Fractional Order Calculus Diffusion Model[J/OL]. Acad Radiol, 2021 [2022-04-10]. https://pubmed.ncbi.nlm.nih.gov/34429260/. DOI: 10.1016/j.acra.2021.07.005.
[16]
Feng C, Wang Y, Dan G, et al. Evaluation of a fractional-order calculus diffusion model and bi-parametric VI-RADS for staging and grading bladder urothelial carcinoma[J]. Eur Radiol, 2022, 32(2): 890-900. DOI: 10.1007/s00330-021-08203-2.
[17]
Luo Y, Jiang H, Meng N, et al. A comparison study of monoexponential and fractional order calculus diffusion models and 18F-FDG PET in differentiating benign and malignant solitary pulmonary lesions and their pathological types[J/OL]. Front Oncol, 2022, 12 [2022-04-10]. https://pubmed.ncbi.nlm.nih.gov/35936757/. DOI: 10.3389/fonc.2022.907860.
[18]
Nguyen XC, Lee WW, Chung JH, et al. FDG uptake, glucose transporter type 1, and Ki-67 expressions in non-small-cell lung cancer: correlations and prognostic values[J]. Eur J Radiol, 2007, 62(2): 214-219. DOI: 10.1016/j.ejrad.2006.12.008.
[19]
Zhang A, Wang X, Fan C, et al. The Role of Ki67 in Evaluating Neoadjuvant Endocrine Therapy of Hormone Receptor-Positive Breast Cancer[J/OL]. Front Endocrinol (Lausanne), 2021, 12 [2022-04-10]. https://pubmed.ncbi.nlm.nih.gov/34803903/. DOI: 10.3389/fendo.2021.687244.
[20]
Aung TN, Acs B, Warrell J, et al. A new tool for technical standardization of the Ki67 immunohistochemical assay[J]. Mod Pathol, 2021, 34(7): 1261-1270. DOI: 10.1038/s41379-021-00745-6.
[21]
Ren H, Ma J, Wang J. Correlation between apparent diffusion coefficient and Ki-67 in different pathological types of lung cancer[J]. Transl Cancer Res, 2021, 10(12): 5364-5371. DOI: 10.21037/tcr-21-2515.
[22]
Surov A, Caysa H, Wienke A, et al. Correlation Between Different ADC Fractions, Cell Count, Ki-67, Total Nucleic Areas and Average Nucleic Areas in Meningothelial Meningiomas[J]. Anticancer Res, 2015, 35(12): 6841-6846.
[23]
Zhang XY, Sun YS, Tang L, et al. Correlation of diffusion-weighted imaging data with apoptotic and proliferation indexes in CT26 colorectal tumor homografts in balb/c mouse[J]. J Magn Reson Imaging, 2011, 33(5): 1171-1176. DOI: 10.1002/jmri.22558.
[24]
Mori N, Ota H, Mugikura S, et al. Luminal-type breast cancer: correlation of apparent diffusion coefficients with the Ki-67 labeling index[J]. Radiology, 2015, 274(1): 66-73. DOI: 10.1148/radiol.14140283.
[25]
Sun M, Cheng J, Zhang Y, et al. Application of DWIBS in malignant lymphoma: correlation between ADC values and Ki-67 index[J]. Eur Radiol, 2018, 28(4): 1701-1708. DOI: 10.1007/s00330-017-5135-y.
[26]
Seo N, Chung YE, Park YN, et al. Liver fibrosis: stretched exponential model outperforms mono-exponential and bi-exponential models of diffusion-weighted MRI[J]. Eur Radiol, 2018, 28(7): 2812-2822. DOI: 10.1007/s00330-017-5292-z.
[27]
Zhang Q, Ouyang H, Ye F, et al. Multiple mathematical models of diffusion-weighted imaging for endometrial cancer characterization: Correlation with prognosis-related risk factors[J/OL]. Eur J Radiol, 2020, 130 [2022-04-10]. https://pubmed.ncbi.nlm.nih.gov/32673928/. DOI: 10.1016/j.ejrad.2020.109102.
[28]
Jerome NP, Vidić I, Egnell L, et al. Understanding diffusion-weighted MRI analysis: Repeatability and performance of diffusion models in a benign breast lesion cohort[J/OL]. NMR Biomed, 2021, 34(7) [2022-04-10]. https://pubmed.ncbi.nlm.nih.gov/33738878/. DOI: 10.1002/nbm.4508.
[29]
Usuda K, Ishikawa M, Iwai S, et al. Pulmonary Nodule and Mass: Superiority of MRI of Diffusion-Weighted Imaging and T2-Weighted Imaging to FDG-PET/CT[J/OL]. Cancers (Basel), 2021, 13(20) [2022-04-10]. https://pubmed.ncbi.nlm.nih.gov/34680313/. DOI: 10.3390/cancers13205166.
[30]
Yuan Z, Niu XM, Liu XM, et al. Use of diffusion-weighted magnetic resonance imaging (DW-MRI) to predict early response to anti-tumor therapy in advanced non-small cell lung cancer (NSCLC): a comparison of intravoxel incoherent motion-derived parameters and apparent diffusion coefficient[J]. Transl Lung Cancer Res, 2021, 10(8): 3671-3681. DOI: 10.21037/tlcr-21-610.
[31]
Usuda K, Funasaki A, Sekimura A, et al. FDG-PET/CT and diffusion-weighted imaging for resected lung cancer: correlation of maximum standardized uptake value and apparent diffusion coefficient value with prognostic factors[J/OL]. Med Oncol, 2018, 35(5) [2022-04-10]. https://pubmed.ncbi.nlm.nih.gov/29633024/. DOI: 10.1007/s12032-018-1128-1.
[32]
Tang L, Sui Y, Zhong Z, et al. Non-Gaussian diffusion imaging with a fractional order calculus model to predict response of gastrointestinal stromal tumor to second-line sunitinib therapy[J]. Magn Reson Med, 2018, 79(3): 1399-1406. DOI: 10.1002/mrm.26798.
[33]
刘贯中. 磁共振弥散成像分数微积分模型在儿童脑肿瘤中的临床应用[D]. 上海: 第二军医大学, 2012.
Liu GZ. Studies of Anomalous Diffusion in Pediatric Brain Tumor Using Fractional Order Calculus modelling[D]. Shanghai: Naval Medical University, 2012.
[34]
Karam MB, Doroudinia A, Behzadi B, et al. Correlation of quantified metabolic activity in nonsmall cell lung cancer with tumor size and tumor pathological characteristics[J/OL]. Medicine (Baltimore), 2018, 97(32) [2022-04-10]. https://pubmed.ncbi.nlm.nih.gov/30095621/. DOI: 10.1097/MD.0000000000011628.
[35]
Park S, Lee E, Rhee S, et al. Correlation between Semi-Quantitative (18)F-FDG PET/CT Parameters and Ki-67 Expression in Small Cell Lung Cancer[J]. Nucl Med Mol Imaging, 2016, 50(1): 24-30. DOI: 10.1007/s13139-015-0363-z.
[36]
杨鳗力, 游金辉. 葡萄糖转运蛋白1与非小细胞肺癌18F-FDGPET/CT显像的相关性研究及临床应用进展[J]. 生物医学工程学杂志, 2021, 38(2): 399-404. DOI: 10.7507/1001-5515.202010004.
Yang ML, You JH. Progress in the clinical application and correlation between glucose transporter-1 and 18F-FDG PET/CT imaging for non-small cell lung cancer[J]. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi, 2021, 38(2): 399-404. DOI: 10.7507/1001-5515.202010004.
[37]
Wang Y, Qiao X, Yang X, et al. The role of a drug-loaded poly (lactic co-glycolic acid) (PLGA) copolymer stent in the treatment of ovarian cancer[J]. Cancer Biol Med, 2020, 17(1): 237-250. DOI: 10.20892/j.issn.2095-3941.2019.0169.
[38]
Grant L, Banerji S, Murphy L, et al. Androgen Receptor and Ki67 Expression and Survival Outcomes in Non-small Cell Lung Cancer[J]. Horm Cancer, 2018, 9(4): 288-294. DOI: 10.1007/s12672-018-0336-7.

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