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临床研究
小视野扩散峰度成像技术在子宫内膜样腺癌分级评估中的价值研究
朱柳红 路伟宏 王艳微 吴仆射 王福南 刘豪 周建军

Cite this article as: ZHU L H, LU W H, WANG Y W, et al. Study the value of reduced field-of-view diffusion kurtosis imaging in histological evaluation of endometrial adenocarcinoma[J]. Chin J Magn Reson Imaging, 2025, 16(2): 77-82.本文引用格式:朱柳红, 路伟宏, 王艳微, 等. 小视野扩散峰度成像技术在子宫内膜样腺癌分级评估中的价值研究[J]. 磁共振成像, 2025, 16(2): 77-82. DOI:10.12015/issn.1674-8034.2025.02.012.


[摘要] 目的 探讨小视野扩散峰度成像(reduced field-of-view diffusion kurtosis imaging, rFOV-DKI)技术在鉴别子宫内膜样腺癌组织学分级中的潜力。材料与方法 本研究共纳入48例经病理证实的子宫内膜样腺癌患者。依据国际妇产科联盟(The International Federation of Gynecology and Obstetrics, FIGO)分级法,受试者分为低级别组(G1、G1~2和G2,n=30)和高级别组(G3,n=18)。所有受试者均于3.0 T MRI扫描仪下行常规盆腔平扫加增强及rFOV-DKI序列扫描。参照常规矢状位T2加权图像在rFOV-DKI序列图像手动勾画感兴趣区(region of interest, ROI)。计算ROI的扩散峰度成像(diffusion kurtosis imaging, DKI)衍生参数,包括平均扩散率(mean diffusivity, MD)、轴向扩散率(axial diffusivity, Da)、径向扩散率(radial diffusivity, Dr)、平均峰度(mean kurtosis, MK)、轴向峰度(axial kurtosis, Ka)和径向峰度(radial kurtosis, Kr)。比较分析各rFOV-DKI参数在低级别组和高级别组间的差异,同时采用受试者工作特征(receiver operating characteristic, ROC)曲线方法评估每个参数的诊断性能。采用DeLong方法对比各参数ROC曲线下面积(area under the curve, AUC)的差异。结果 低级别组MD、Da和Dr的平均值[(0.93±0.08)µm2/ms、(1.14±0.10)µm2/ms、(0.83±0.08)µm2/ms]高于高级别组的平均值[(0.80±0.08)µm2/ms、(1.05±0.07)µm2/ms、(0.74±0.06)µm2/ms;P <0.05],而MK、Ka和Kr的平均值(1.15±0.10、1.36±0.10、0.97±0.13)则低于高级别组(1.33±0.11、1.64±0.11、1.08±0.09)(P<0.05)。Ka值在区分低级别组和高别级组时具有最高的诊断准确性,ROC曲线下面积(area under the curve, AUC)为0.98(95% CI:0.89~1.00),其次是MK [AUC=0.90(95% CI:0.78~0.97)]和MD [AUC=0.88(95% CI:0.76~0.96)]。MK与Ka和MD的AUC间差异均没有统计学意义(Z=1.81和0.53,P=0.07和0.59),而Ka和MD的AUC间差异具有统计学意义(Z=2.40,P=0.02)。在所有DKI衍生参数中,Ka在区分低级别组和高级别组方面表现最好,截断值为1.46,敏感度和特异度分别为100%和90%。结论 基于非高斯扩散加权模型的rFOV-DKI可作为区分子宫内膜样腺癌组织学分级的潜在影像学工具,用于子宫内膜样腺癌的无创术前分级。
[Abstract] Objective To explore the potential performance of reduced field-of-view diffusion kurtosis imaging (rFOV-DKI) in the differentiating the different histological grades of endometrial adenocarcinoma.Materials and Methods A total of 48 patients with pathologically confirmed endometrial adenocarcinoma were enrolled in our study after getting institutional review board approval. According to the two-rank classification method of the International Federation of Gynecology and Obstetrics (FIGO), the participants were divided to low-grade group (G1, G1-2 and G2, n = 30) and high-grade group (G3, n = 18). All participants underwent contrast enhancement MR examinations including routine sequences and additional rFOV-DKI sequence on a 3.0 T MRI scanner. The data was postprocessed by the functional tool on the workstation (AW4.6, GE Healthcare). With the reference of sagittal T2WI images, the lesion ROI (region of interest) was outlined. Derived parameters of DKI, including mean diffusivity (MD), axial diffusivity (Da), radial diffusivity (Dr), mean kurtosis (MK), axial kurtosis (Ka), and radial kurtosis (Kr) were all calculated. The DKI parameters of low-grade group and high-grade group were compared. The receiver operating characteristic (ROC) curve was used to evaluate each parameter's diagnostic performance.Results Mean values of MD, Da and Dr of low-grade group [(0.93 ± 0.08) µm2/ms, (1.14 ± 0.10) µm2/ms, (0.83 ± 0.08) µm2/ms] were significantly higher than those of high-grade group [(0.80 ± 0.08) µm2/ms, (1.05 ± 0.07) µm2/ms, (0.74 ± 0.06) µm2/ms; all P < 0.05]. While mean values of MK, Ka and Kr of low-grade group (1.15 ± 0.10, 1.36 ± 0.10, 0.97 ± 0.13) were significantly lower than those of high-grade group (1.33 ± 0.11, 1.64 ± 0.11, 1.08 ± 0.09). The Ka values had the highest diagnostic accuracy in differentiating low-grade group from high-grade group, AUC = 0.98 (95% CI: 0.89 to 1.00), followed by MK [AUC = 0.90 (95% CI: 0.78 to 0.97)] and MD [AUC is 0.88 (95% CI: 0.76 to 0.96)]. There were no significant differences between AUCs of MK and Ka (Z = 1.81, P = 0.07), and AUCs of MK and MD (Z = 0.53, P = 0.59), while significant differences were found between that of Ka and MD (Z = 2.40, P = 0.02). Ka performed best (sensitivity: 100%, specificality: 90%) in the differentiation between low-grade group from high-grade group among all DKI derived parameters.Conclusions Kurtosis indices from rFOV-DKI based on the non-Gaussian diffusion-weighted model can be acted as a potential tool in the grade differentiation of endometrial adenocarcinoma, and can be a useful compensation to the conventional MRI.
[关键词] 子宫内膜样腺癌;磁共振成像,小视野扩散峰度成像,组织学分级,平均峰度,轴向峰度
[Keywords] endometrial adenocarcinoma;magnetic resonance imaging;reduced field-of-view diffusion kurtosis imaging;histological grade;mean kurtosis;axial kurtosis

朱柳红 1   路伟宏 2   王艳微 3   吴仆射 4   王福南 1   刘豪 1, 5*   周建军 1, 5  

1 复旦大学附属中山医院厦门医院放射诊断科,厦门 361015

2 复旦大学附属中山医院厦门医院妇科,厦门 361015

3 厦门医学院附属第二医院放射影像科,厦门 361012

4 通用电气医疗(中国)有限公司磁共振科研部,北京 100176

5 复旦大学附属中山医院放射诊断科,上海 200032

通信作者:刘豪,E-mail: liuhaozsxm@163.com

作者贡献声明:刘豪审核本研究的方案,对全文内容进行了审核并对稿件主要部分内容进行了修改;朱柳红设计本研究的方案,起草和撰写稿件,获取、分析和解释本研究的数据,获得了福建省自然科学基金资助;路伟宏推荐临床患者并进行患者病理随访,分析及解释本研究的数据,对稿件的内容进行了修改;王艳微和王福南审阅患者影像并进行诊断、数据采集、结果分析及解释,对稿件的内容进行了修改;吴仆射优化扫描序列参数并进行技术指导,分析及解释本研究的数据,对稿件的技术部分内容进行了修改;周建军参与了本研究的构思和设计,对稿件重要内容进行了修改;全体作者对最终要发表的论文版本进行了全面的审阅和把关,全体作者都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。


基金项目: 福建省自然科学基金项目 2022J011425
收稿日期:2024-07-29
接受日期:2025-01-10
中图分类号:R445.2  R737.33 
文献标识码:A
DOI: 10.12015/issn.1674-8034.2025.02.012
本文引用格式:朱柳红, 路伟宏, 王艳微, 等. 小视野扩散峰度成像技术在子宫内膜样腺癌分级评估中的价值研究[J]. 磁共振成像, 2025, 16(2): 77-82. DOI:10.12015/issn.1674-8034.2025.02.012.

0 引言

       子宫内膜癌(endometrial carcinoma, EC)是原发于子宫内膜上皮的女性生殖道恶性肿瘤。随着肥胖人群的增加以及激素替代疗法的应用,EC的发病率逐年增长且有年轻化的趋势[1, 2],是最常见的妇科生殖系统恶性肿瘤之一[3, 4]。在EC的病理分型中,内膜样腺癌是EC最常见的病理分型之一,发生率约占EC 80%以上。国际妇产科联盟(The International Federation of Gynecology and Obstetrics, FIGO)(2023)根据腺体结构和实性区的比例可分为G1、G2、G3等三级,同时国际妇科病理学会建议参照FIGO将内膜样腺癌分为低级别(G1、G2)和高级别(G3),不同的级别与病灶的肌层侵犯及淋巴结转移风险等存在高相关性[5],预后不同[6, 7],因此提高术前分级诊断对于临床有重要临床意义。子宫内膜活体组织病理学检查已成为EC术前检查的常规诊断手段,但由于子宫内膜病变的异质性,术前活检存在约10%的假阳性,在重复性和准确性上有一定限制[1]。在影像学检查方面,MRI是子宫内膜病变评估的最优影像手段[8],其中扩散加权成像(diffusion-weighted imaging, DWI)技术可无创地检测组织内水分子的扩散运动,已广泛运用于临床实践[9, 10, 11]。近年来,作为DWI序列的优秀分支,扩散峰度成像(diffusion kurtosis imaging, DKI)技术已逐步运用于人体各个部位肿瘤的诊断及鉴别诊断[12, 13, 14]。然而,现有的DKI技术仍然是基于全视野的DWI技术,在运用于腹部及盆腔脏器时,相位误差的积累会导致图像变形和伪影[15, 16]。小视野DWI技术可克服分辨率低及易受伪影干扰等问题[17, 18]。目前,将小视野DWI技术与DKI相结合的技术,即小视野扩散峰度成像(reduced field-of-view diffusion kurtosis imaging, rFOV-DKI)技术,运用于EC的研究尚未见报道。为此,本研究将探索rFOV-DKI衍生参数在鉴别子宫内膜样腺癌组织学分级的能力,以期无创地评估子宫内膜样腺癌的术前分级,从而为临床提供更准确的术前指导。

1 材料与方法

1.1 一般资料

       前瞻性分析了2022年9月至2024年2月在复旦大学附属中山医院厦门医院行妇科手术的61例EC患者的数据。纳入标准:(1)经临床医师依据术前活检提示子宫内膜样腺癌患者或依据临床综合判断高度怀疑EC患者;(2)磁共振检查前,患者未经任何治疗。排除标准:(1)病理非子宫内膜样腺癌患者;(2)因肠道蠕动或检查过程中身体挪动造成的rFOV-DKI图像伪影重而导致无法测量的患者;(3)肿瘤矢状位最大径<8 mm影响测量的患者。本研究遵守《赫尔辛基宣言》,并经复旦大学附属中山医院厦门医院伦理委员会批准,批文号为:Y2021-025,全体受试者均签署了知情同意书。

1.2 数据采集

       本研究利用美国GE Discovery MR 750w 3.0 T磁共振扫描仪及配套16通道腹部相控阵线圈对每位受试者进行扫描。扫描序列包括常规轴位及矢状位T2加权成像(T2 weighted imaging, T2WI)、DWI、基于水脂分离技术的屏气容积内插3D梯度回波(liver acquisition with volume acceleration, LAVA-Flex)以及rFOV-DKI等序列。对比剂为钆布醇注射液(加乐显,拜耳公司),对比剂注射流速2.0 mL/s,用量0.1 mL/kg。

1.2.1 常规序列

       (1)轴位T2WI(包括压脂像)序列参数:TR 5570 ms,TE 62 ms,FOV 280 mm×280 mm,层厚4.0 mm,层间距1.0 mm,矩阵352×352;(2)矢状位T2WI压脂序列参数:TR 6515 ms,TE 80 ms,FOV 280 mm×280 mm,层厚4.0 mm,层间距1.0 mm,矩阵352×352;(3)常规轴位DWI序列参数:TR 6091 ms,TE 63.3~197.0 ms,FOV 320 mm×320 mm,层厚4.0 mm,层间距1.0 mm,b值0、50、1000 s/mm2,矩阵128×120;(4)轴位屏气LAVA-Flex三期动态增强序列参数:TR 5.5 ms,TE 1.3 ms,FOV 360 mm×360 mm,层厚2.5 mm,层间距0 mm,矩阵292×256,轴位动脉期、轴位静脉期、矢状位静脉期、冠状位静脉期及轴位延迟期分别于钆剂注射后30 s,60 s,90 s,120 s及150 s进行扫描。

1.2.2 rFOV-DKI序列

       序列参数:矢状位成像,自由呼吸,扩散方向数15个,b值分别为0、800及1600 s/mm2,平均采集次数均为5,激励模式Focus,层厚4.0 mm,间距1.0 mm,FOV 220 mm×100 mm,TR 2000 ms,TE 81.2~203.0 ms,矩阵128×60,带宽250 kHz,层数11,采集时间为312 s。

1.3 数据分析

       所有受试者的rFOV-DKI数据均传至GE AW4.6工作站,利用“Functool-DKI”软件进行后处理。rFOV-DKI序列后处理衍生参数图有扩散相关系数及峰度相关系数。其中扩散相关系数包括平均扩散率(mean diffusivity, MD)、轴向扩散率(axial diffusivity, Da)及径向扩散率(radial diffusivity, Dr),峰度相关系数包括平均峰度(mean kurtosis, MK)、轴向峰度(axial kurtosis, Ka)和径向峰度(radial kurtosis, Kr)。其中,MD反映水分子整体扩散水平;MK是DKI的主要参数,代表所有梯度方向上峰度的平均值,结构越复杂,水分子非正态分布扩散受限越显著,MK值越大;Kr和Ka分别是垂直和平行于主要弥散方向上峰度的平均值。对于每位患者,由2名具有5年以上MRI诊断经验的医师先行浏览患者所有序列,参考其矢状位T2WI序列及rFOV-DKI从低到高b值图像,于高b值(1600 s/mm2)图像上勾勒肿瘤矢状位最大层面ROI,勾勒时尽量避开病灶内出血及囊变坏死区,并计算获得每个感兴趣区(region of interest, ROI)的DKI参数值。

1.4 统计学分析

       基于SPSS 25.0(IBM, USA)软件平台进行统计学分析。采用Kolmogorov-Smirnov法检验计量资料是否符合正态分布,符合正态分布时数据描述用均数±标准差表述,采用独立样本t检验进行分析,否则用中位数(上下四分位数)表述,采用Mann-Whitney U检验进行分析。组内相关系数(intra-class correlation coefficient, ICC)用于评估测量参数的观察者间一致性。ICC值<0.4、0.40~0.75和>0.75分别表示一致性较差、一般和良好。本研究采用以上方法比较及分析各DKI参数在低级别组和高级别组间的差异。同时对每个DKI参数绘制ROC曲线,计算AUC、敏感度、特异度及截断值,以评估各参数鉴别不同组别子宫内膜样腺癌的效能。采用DeLong方法对比各参数ROC曲线下面积的差异。样本量评估采用PASS 15.0(NCSS, USA)软件进行。P<0.05表示差异具有统计学意义。

2 结果

2.1 患者基线资料和观察者间一致性

       13例患者被排除后最终纳入研究的受试者共48名,排除原因包括:8例患者的病理为非子宫内膜样腺癌、3例患者图像伪影重、2例患者肿瘤最大径<8 mm。最终纳入研究的48名受试者均为女性,年龄47~77(59.75±7.95)岁,病理等级为G1、G1~G2、G2及G3的患者分别为6例、3例、21例及18例。依据FIGO分级两分类法,患者分为低级别组(G1、G1~G2、G2;n=30)和高级别组(G3;n=18)。患者的基线资料如表1所示,高级别组的肿瘤标志物水平高于低级别组(P<0.05),这与肿瘤的生物学特性相关。在所有ROI中,各参数测量值的观察者间一致性良好,组内相关系数ICC均>0.75(表2)。

表1  患者基线资料
Tab. 1  Baseline information of patients
表2  观察者间一致性分析
Tab. 2  Interobserver consistency analysis

2.2 不同级别子宫内膜样腺癌rFOV-DKI衍生参数对比

       对于扩散相关系数(MD、Da和Dr)而言,低级别组的平均值均高于高级别组;而低级别组峰度相关系数(MK、Ka和Kr)的平均值则均低于高级别组,差异均具有统计学意义(P<0.01)(表3图1~2)。

图1  女,46岁,子宫异常出血,术后病理结果为子宫内膜样腺癌Ⅱ级(G2),侵犯浅肌层(<1/2)。矢状位T2WI图(1A)上可见子宫腔内一信号稍高团块影(箭头),于rFOV-DKI序列中,随着b值的增高,病灶弥散受限越为明显(1B~1D);病灶在MD图(1E)上表现为明显的低信号(病灶MD值为0.85×10-3 mm2/s);在MK图(1F)及Ka图(1G)上表现为高信号(病灶MK及Ka值分别为1.16、1.40)。图2 女,63岁,绝经后出血,术后病理结果为子宫内膜样腺癌Ⅲ级(G3),侵犯深肌层(>1/2)。矢状位T2WI图(2A)上可见子宫腔内一信号稍高团块影(箭头),于rFOV-DKI序列中,随着b值的增高,病灶弥散受限越为明显(2B~2D);病灶在DKI后处理衍生参数MD图(2E)上表现为明显的低信号(病灶MD值为0.70×10-3 mm2/s);在MK图(2F)及Ka图(2G)上表现为高信号(病灶MK及Ka值分别为1.47、1.86)。rFOV-DKI:小视野扩散峰度成像;MD:平均扩散率;MK:平均峰度;Ka:轴向峰度。
Fig. 1  Female, 46-year-old, patient with abnormal uterine bleeding, the postoperative pathological indicates that the patient has endometrial adenocarcinoma, grade Ⅱ (G2), which has invaded the superficial myometrium (less than half the depth). On the sagittal T2-weighted image (1A), a slightly hyperintense mass is visible within the uterine cavity (arrowhead), in the rFOV-DKI sequence, the diffusion signal intensity of the lesion becomes more apparent (1B-1D) as the b-value increases; on the MD map (1E), the lesion exhibits significantly low signal intensity (MD value is 0.85 × 10-3 mm2/s); and it shows high signal intensity on both the MK map (1F) and the Ka map (1G), with MK and Ka values of 1.16 and 1.40, respectively. Fig. 2 Female, 63-year-old, patient with postmenopausal bleeding, the postoperative pathological indicates that the patient has endometrial adenocarcinoma, grade Ⅲ (G3), invading the deep myometrium (greater than half the depth). On the sagittal T2-weighted image (2A), a slightly hyperintense mass is visible within the uterine cavity (arrowhead), in the rFOV-DKI sequence, the diffusion signal intensity of the lesion becomes more apparent (2B-2D) as the b-value increases; on the MD map (2E), the lesion exhibits significantly low signal intensity (MD value is 0.70 × 10-3 mm2/s); and it shows high signal intensity on both the MK map (2F) and the Ka map (2G), with MK and Ka values of 1.47 and 1.86, respectively. rFOV-DKI: reduced field-of-view diffusion kurtosis imaging; MD: mean diffusivity; MK: mean kurtosis; Ka: axial kurtosis.
表3  不同级别子宫内膜样腺癌rFOV-DKI衍生参数对比表
Tab. 3  Comparison of mean values of rFOV-DKI derived parameters between low-grade and high-grade endometrial adenocarcinoma

2.3 rFOV-DKI衍生参数对比在鉴别不同组别中的ROC分析

       在所有rFOV-DKI衍生参数中,Ka值在区分子宫内膜样腺癌低级别组和高级别组时具有最高的诊断准确性(AUC=0.98),其次是MK(AUC=0.90)和MD(AUC=0.88)(图3)。MK与Ka的AUC之间(Z=1.81,P=0.07)和MK与MD的AUC之间(Z=0.53,P=0.59)差异均没有统计学意义,而Ka与MD的AUC之间差异存在统计学意义(Z=2.40,P=0.02)。在所有DKI衍生参数中,Ka在区分低级别组和高级别组方面表现最好,敏感度和特异度分别为100.0%和90.0%(表4)。

图3  MK、Ka及MD在鉴别不同组别子宫内膜样腺癌中的ROC曲线。MK:平均峰度;Ka:轴向峰度;MD:平均扩散率;ROC:受试者工作特征。
Fig. 3  ROC curves of MK, Ka and MD in distinguishing low-grade group from high-grade group. ROC: receiver operating characteristic; MK: mean kurtosis; Ka: axial kurtosis; MD: mean diffusivity.
表4  rFOV-DKI衍生参数在鉴别不同组别子宫内膜样腺癌中效能分析
Tab. 4  Efficiency analysis of rFOV-DKI derived parameters in the distinguishing low-grade endometrial adenocarcinoma from high-grade

2.4 样本量评估

       对本研究中有统计学意义的三个DKI参数(MD、MK、Ka)进行样本量评估,即在已知低级别组样本量(N1)情况下估算高级别组样本量(N2)。利用PASS 15.0软件平台,设置检验方向为双侧检验,把握度Power=95%,显著性水平α=0.05,研究对象的失访率为10%。例如,低级别及高级别组MD的均值分别为0.93和0.80,两组总的标准差为0.097,已知N1为30,估算所得的N2至少为13。本研究中N2=18>13,MD的研究结果可支持研究结论的广泛适用性。MK和Ka以此类推,如表5所示。

表5  已知低级别组样本量情况下估算高级别组样本量
Tab. 5  Estimating the sample size for the high-grade group given the sample size of the low-grade group

3 讨论

       本研究采用rFOV-DKI技术对子宫内膜样腺癌进行成像,对比分析rFOV-DKI衍生参数在高级别和低级别子宫内膜样腺癌的差异,并运用ROC曲线评估各衍生参数在鉴别不同组别子宫内膜样腺癌的诊断效能。研究结果发现,子宫内膜样腺癌高级别组的峰度相关参数(MK、Ka和Kr)均高于低级别组,且MK和Ka在鉴别高低级别子宫内膜样腺癌中的诊断性能较扩散相关参数更高。本研究结果为子宫内膜样腺癌的无创术前分级提供了一种新的影像学诊断方法。

3.1 rFOV-DKI特点分析

       传统的DKI技术是基于全视野平面回波成像(echo planar imaging, EPI)的DWI技术,EPI的采集方式会产生相位误差的积累,导致明显的图像变形和伪影,而且由于成像视野较大,容易受盆腔积气及肠道蠕动所产生的磁敏感伪影及运动伪影的干扰,在3.0 T设备上尤为突出,定量值测量准确性受影响[19]。rFOV-DWI技术与传统EPI-DWI技术不同,其180°重聚脉冲不与90°激励脉冲平行,而是呈一定夹角进行激发;且在两端斜面产生的信号可通过添加过采样得以消除。因此,rFOV-DWI技术不仅可以提高局部分辨率,还可以大大减轻图像的变形和伪影。目前该技术已逐步运用于宫颈癌、直肠癌、胰腺癌及子宫内膜病变等的评估,与传统的DWI技术相比,rFOV-DWI在显示肿瘤周围组织浸润及分期等方面更具有潜力[20, 21, 22]

3.2 扩散相关参数与峰度相关参数在鉴别高低级别子宫内膜样腺癌的效能对比研究

       在本研究中,子宫内膜样腺癌高级别组的扩散相关参数值(MD、Da和Dr)均低于低级别组,而高级别组的峰度相关参数(MK、Ka和Kr)均高于低级别组,差异均具有统计学意义。值得一提的是,Ka值在区分低级别和高别级子宫内膜样腺癌时具有最高的诊断准确性(AUC=0.98),其次是MK(AUC=0.90)及MD(AUC=0.88),其余参数AUC较低;MK与Ka的AUC之间差异没有统计学意义,而Ka和MD的AUC之间差异存在统计学意义,说明峰度相关系数在鉴别高低级别子宫内膜样腺癌的诊断性能优于扩散相关系数。在峰度相关系数与扩散相关系数在内膜癌分级诊断性能对比方面,本研究的结果与YUE等[23]及TIAN等[24]的研究结果相近,均表明峰度相关参数能够更有效地评估EC的病理特征。

3.3 峰度相关参数在鉴别高低级别子宫内膜样腺癌中的效能机制探讨

       在子宫内膜样腺癌的不同分级中,细胞特点存在明显差异:G1级别的癌细胞异型性较小;G2级别的细胞异型性介于G1和G3之间,表现出更明显的细胞异型性;而G3级别的组织则具有更高的细胞异型性和核增殖指数,通常伴随着更高的细胞密度和异常的细胞结构。这种组织结构的复杂性和异质性可以通过DKI峰度参数得到量化[25]。峰度参数中的MK代表了在所有梯度方向下多b值扩散峰度的平均值,组织结构越复杂(如细胞密度增加、细胞异型性明显以及细胞核多形性显著等),MK值越高;Ka和Kr分别代表平行和垂直于扩散张量长轴方向的扩散峰度平均值,它们分别反映了沿着和垂直于轴突方向的扩散特性[26]。峰度相关参数已被证实在肿瘤的异质性[27]、肿瘤分级评估[28, 29, 30]以及与肿瘤病理类型和预后的相关性[31, 32]等方面均具有较大的潜力。在本研究中,我们发现高级别子宫内膜样腺癌组的峰度相关系数高于低级别组,这表明高级别组的组织结构较低级别组更为复杂,异质性更强。此研究结果与不同级别子宫内膜样腺癌的病理特征相吻合,进一步证实了DKI技术在评估子宫内膜样腺癌组织学特征方面的潜力。

3.4 本研究的局限性

       (1)由于其他类型的EC发病率较低,仅对常见的子宫内膜样腺癌进行了研究,研究结果不可代表所有的EC特性;(2)高级别组的子宫内膜样腺癌病例数虽有限,虽样本量评估结果显示研究结果可支持研究结论的广泛适用性,但大样本量可使研究结论具有说服力;(3)本研究虽然采用了rFOV-DKI的技术,分辨率得到了提升,但仍然无法对肿瘤矢状位最大径<8 mm的子宫内膜样腺癌数据进行分析,这与肿瘤在高b值时的低信噪比有关。为拓展本研究的临床应用范围,未来将进一步探索DKI在预测EC预后及治疗反应方面的潜力。

4 结论

       综上所述,基于非高斯扩散加权模型的rFOV-DKI技术可作为区分子宫内膜样腺癌组织学分级的潜在影像学工具,为子宫内膜样腺癌分级提供了新的无创影像学方法。

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