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临床研究
集成MRI联合MUSE-DWI技术术前评估直肠癌神经脉管侵犯:基于瘤体及瘤周区域MRI定量参数分析研究
祝叶 成东亮 邓琦 叶健凌 杨本坤 杨云竣 刘健萍

Cite this article as: ZHU Y, CHENG D L, DENG Q, et al. Magnetic resonance image compilation combined with MUSE-DWI for preoperative evaluation of neurovascular invasion in rectal cancer: Quantitative analysis of intratumoral and peritumoral parameters[J]. Chin J Magn Reson Imaging, 2026, 17(9): 136-143.本文引用格式:祝叶, 成东亮, 邓琦, 等. 集成MRI联合MUSE-DWI技术术前评估直肠癌神经脉管侵犯:基于瘤体及瘤周区域MRI定量参数分析研究[J]. 磁共振成像, 2026, 17(9): 136-143. DOI:10.12015/issn.1674-8034.2026.09.018.


[摘要] 目的 前瞻性评估瘤内及瘤周集成磁共振成像(magnetic resonance image compilation, MAGiC)联合复合灵敏度编码扩散加权成像(multiplexed sensitivity-encoding diffusion-weighted imaging, MUSE-DWI)在术前预测直肠癌神经脉管侵犯(neurovascular invasion, NVI)中的价值。材料与方法 前瞻性纳入70例直肠癌患者,术前均行MAGiC及MUSE-DWI序列扫描,并测量瘤内(肿瘤实性部分)及瘤周系膜区纵向弛豫时间(T1值)、横向弛豫时间(T2值)、质子密度(proton density, PD)及表观扩散系数(apparent diffusion coefficient, ADC)。以术后病理结果为金标准,将神经侵犯(perineural invasion, PNI)或脉管侵犯(lymphovascular invasion, LVI)任一阳性者归为NVI阳性组(31例),两者均阴性者为NVI阴性组(39例)。单因素分析比较两组临床资料、MAGiC定量参数及ADC值的差异;将差异有统计学意义的参数纳入二元logistic回归分析,筛选NVI的独立影响因子并构建联合诊断模型。采用受试者工作特征(receiver operating characteristic, ROC)曲线评价各参数及联合参数的诊断效能,并通过校准曲线与决策曲线分析(decision curve analysis, DCA)评估联合诊断模型的校准度及临床实用性。结果 单因素分析显示,NVI阳性组瘤内T1值(T1内)、瘤内T2值(T2内)、瘤周PD值(PD周)及瘤内ADC值(ADC内)显著低于阴性组(P<0.05),瘤周T1值(T1周)及瘤周ADC值(ADC周)显著高于阴性组(P<0.05)。其中T1周、T2内及PD周是直肠癌NVI(PNI和/或LVI)的独立影响因子。三者联合诊断NVI的ROC曲线下面积(area under the curve, AUC)最高,为0.906 [95%置信区间(confidence interval, CI):0.812~0.963],高于瘤内参数组合[T1内+T2内+ADC内;AUC(95% CI):0.868(0.765~0.937)]及瘤周参数组合[T1周+PD周+ADC周;AUC(95% CI):0.829(0.720~0.908)],DeLong检验显示差异不显著(Z=1.497、1.221,P=0.134、0.222)。校准曲线与DCA显示该联合模型具有良好的校准度与临床实用性。瘤周参数组合诊断NVI的AUC值稍高于瘤内参数组合,但差异无统计学意义(Z=0.562,P=0.574)。亚组分析显示,该联合模型预测LVI(25例阳性)和PNI(19例阳性)的AUC(95% CI)分别为0.891(0.793~0.953)和0.827(0.717~0.907)。结论 MAGiC联合MUSE-DWI定量参数可在术前有效评估直肠癌NVI。瘤内与瘤周独立影响因子联合可获得良好的诊断效能,为直肠癌术前风险分层及个体化治疗决策提供客观的定量影像学依据。
[Abstract] Objective To prospectively investigate the preoperative predictive value of intratumoral and peritumoral magnetic resonance image compilation (MAGiC) combined with multiplexed sensitivity-encoding diffusion-weighted imaging (MUSE-DWI) for neurovascular invasion (NVI) in rectal cancer.Materials and Methods Seventy patients with rectal cancer were prospectively enrolled. All patients underwent preoperative MAGiC and MUSE-DWI sequences, and intratumoral (solid tumor component) and peritumoral mesorectal quantitative parameters were measured, including longitudinal relaxation time (T1), transverse relaxation time (T2), proton density (PD), and apparent diffusion coefficient (ADC). Using postoperative pathological results as the gold standard, patients with either perineural invasion (PNI) or lymphovascular invasion (LVI) were assigned to the NVI-positive group (n = 31), and those with neither were assigned to the NVI-negative group (n = 39). Univariate analysis was used to compare clinical data, MAGiC-derived quantitative parameters, and ADC values between the two groups. Parameters that differed significantly were entered into binary logistic regression to identify independent predictors of NVI and to develop a combined diagnostic model. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of individual parameters and the combined model. Calibration curves and decision curve analysis (DCA) were used to assess the calibration and clinical utility of the combined model.Results Univariate analysis showed that intratumoral T1 (T1intra), intratumoral T2 (T2intra), peritumoral PD (PDperi), and intratumoral ADC (ADCintra) were significantly lower in the NVI-positive group than in the NVI-negative group (all P < 0.05), while peritumoral T1 (T1peri) and peritumoral ADC (ADCperi) were significantly higher (all P < 0.05). T1peri, T2intra, and PDperi were independent predictors of rectal cancer NVI (PNI and/or LVI). The combination of these three parameters yielded the highest area under the ROC curve (AUC) of 0.906 [95% confidence interval (CI): 0.812 to 0.963] for diagnosing NVI, which was higher than the intratumoral parameter combination (T1intra + T2intra + ADCintra) and the peritumoral parameter combination (T1peri + PDperi + ADCperi), with AUC (95% CI) of 0.868 (0.765 to 0.937) and 0.829 (0.720 to 0.908), respectively. DeLong test showed no statistically significant differences between the combined model and the intratumoral or peritumoral models (Z = 1.497 and 1.221, P = 0.134 and 0.222, respectively). The calibration curve and DCA demonstrated good calibration and clinical utility of the combined model. The AUC of the peritumoral parameter combination was slightly higher than that of the intratumoral combination, but the difference was also not statistically significant (Z = 0.562, P = 0.574). Subgroup analysis showed that the combined model predicted LVI (25 positive cases) and PNI (19 positive cases) with AUC (95% CI) of 0.891 (0.793 to 0.953) and 0.827 (0.717 to 0.907), respectively.Conclusions MAGiC combined with MUSE-DWI quantitative parameters enables effective preoperative assessment of NVI in rectal cancer. The combination of independent intratumoral and peritumoral predictors provides good diagnostic performance and offers objective quantitative imaging evidence for preoperative risk stratification and individualized treatment decision-making.
[关键词] 直肠癌;结直肠肿瘤;神经脉管侵犯;集成磁共振成像;磁共振成像;复合灵敏度编码扩散加权成像;术前诊断;风险分层
[Keywords] rectal cancer;colorectal neoplasms;neurovascular invasion;magnetic resonance image compilation;magnetic resonance imaging;multiplexed sensitivity-encoding diffusion-weighted imaging;preoperative diagnosis;risk stratification

祝叶    成东亮    邓琦    叶健凌    杨本坤    杨云竣    刘健萍 *  

佛山市第一人民医院(南方科技大学附属佛山医院)影像中心,佛山 528010

通信作者:刘健萍,E-mail:nanhailiujp@163.com

作者贡献声明::刘健萍设计本研究的方案,对稿件重要内容进行了修改;祝叶起草和撰写稿件,获取、分析和解释本研究的数据;成东亮、邓琦、叶健凌、杨本坤、杨云竣获取、分析和解释本研究的数据,对稿件重要内容进行了修改;杨云竣获得广东省医学科学技术研究基金项目资助;祝叶获得佛山市卫生健康局医学科研项目资助;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: 广东省医学科学技术研究基金项目 B2025808 佛山市卫生健康局医学科研项目 20250015
收稿日期:2026-05-16
接受日期:2026-08-18
中图分类号:R445.2  R735.37 
文献标识码:A
DOI: 10.12015/issn.1674-8034.2026.09.018
本文引用格式:祝叶, 成东亮, 邓琦, 等. 集成MRI联合MUSE-DWI技术术前评估直肠癌神经脉管侵犯:基于瘤体及瘤周区域MRI定量参数分析研究[J]. 磁共振成像, 2026, 17(9): 136-143. DOI:10.12015/issn.1674-8034.2026.09.018.

0 引言

       直肠癌神经脉管侵犯(neurovascular invasion, NVI)与淋巴结转移及肿瘤浸润深度密切相关,是独立预后不良因子,并已被推荐纳入风险分层,作为术前新辅助放化疗的决策依据[1, 2, 3]。因此术前精准识别NVI对制订治疗方案至关重要。

       高分辨率MRI是术前评估T/N分期的关键方法,但其在诊断NVI方面存在局限性[4]。集成磁共振成像(magnetic resonance image compilation, MAGiC)序列是近年来开发的新技术,单次扫描可实现纵向弛豫时间(T1值)、横向弛豫时间(T2值)及质子密度(proton density, PD)值的标准化定量测量,具有快速、后处理便捷及可重复性高等优点[5, 6, 7],已用于多种恶性肿瘤研究[8, 9, 10],但针对直肠癌预后评估的研究较少,且多局限于原发灶,缺乏对瘤周区域的探讨。复合灵敏度编码扩散加权成像(multiplexed sensitivity-encoding diffusion-weighted imaging, MUSE-DWI)应用多频带同时激发联合灵敏度编码,相比传统DWI,能纠正运动引起的相位误差,减少几何失真及磁敏感伪影,从而改善直肠轮廓及病灶显影度,提高瘤内及瘤周区域表观扩散系数(apparent diffusion coefficient, ADC)测量的准确性与可靠性[11, 12]。目前尚无应用MUSE-DWI诊断直肠癌NVI的研究。因此,本研究采用MAGiC联合MUSE-DWI,测量原发灶及瘤周区域的T1、T2、PD及ADC定量参数,探讨其在术前诊断直肠癌NVI的价值。

1 材料与方法

1.1 研究对象

       本研究为前瞻性研究,纳入从2025年7月30日至2026年4月30日就诊于本院胃肠外科及肛肠外科的直肠癌患者70例。本研究遵守《赫尔辛基宣言》,经佛山市第一人民医院伦理委员会批准[批号:伦审研(2025)第201号],并已在国家卫生健康委员会医学研究登记备案系统备案(备案编号:MR-44-25-061603),所有患者均已签署知情同意书。纳入标准:(1)临床或通过肠镜检查高度怀疑患有直肠癌的个体;(2)术前未接受新辅助放化疗、靶向或免疫治疗;(3)术前2周内完成MAGiC及MUSE-DWI序列扫描,且图像质量满足测量要求;(4)术后病理明确神经侵犯、脉管侵犯状态。排除标准:(1)图像存在明显运动伪影、金属伪影,无法准确测量;(2)肿瘤广泛坏死、囊变或体积过小(最大径<1 cm),无法勾画感兴趣区(region of interest, ROI);(3)合并其他恶性肿瘤或严重系统性疾病;(4)临床、病理或影像图像任一资料不全。

1.2 MRI检查方法

       使用美国GE Signa Architect 3.0 T MR扫描仪对所有患者进行扫描。检查前嘱患者排便排尿,禁食水4 h以上。先行常规盆腔MR扫描序列扫描。采用垂直于肿瘤直肠段长径的斜轴位行MUSE-DWI及MAGiC序列扫描。MUSE-DWI序列扫描参数:重复时间(repetition time, TR)4392 ms,回波时间(echo time, TE)72.4 ms,视野(field of view, FOV)36 cm×36 cm,矩阵=160×168,层厚5.5 mm,层间距0.5 mm,带宽250 kHz,b=0、800 s/mm²,采集时间2 min 38 s。MAGiC序列扫描参数:TR 4000 ms,TE 13.7/89.2 ms,FOV 26 cm×26 cm,矩阵320×256,层厚4 mm,无层间距,带宽50 kHz,采集时间为4 min。

1.3 图像处理及分析

       由两位分别有10年和15年腹部MR诊断经验的影像诊断医生用双盲法对MR特征进行评估,包括肿瘤长度、位置、厚度及直肠系膜筋膜(mesorectal fascia, MRF)状态。肿瘤长度与位置在矢状位T2WI上测量,肿瘤下缘距肛缘≤5 cm为下段,>5 cm且<10 cm为中段,≥10 cm为上段。肿瘤厚度与MRF状态在轴位T2WI上测量,肿瘤、血管侵犯或肿瘤结节距MRF<1 mm为MRF阳性。如果两名医生测量结果产生异议,则重新一起评价图像并商量决定。

       通过MAGiC viewer(版本100.1.1)打开扫描得到的MAGiC原始图像后,在未知晓病理结果的情况下,上述两位医生在MAGiC生成的T2WI上手动勾画肿瘤的瘤内及瘤周ROI,使用软件自动计算瘤内及瘤周平均T1、T2及PD值。将MUSE-DWI图导入GE AW47工作站上,经ReadyView后处理软件生成ADC图。MUSE-DWI与MAGiC的T2WI图像先校准,参照MAGiC T2WI图在MUSE-DWI上手动勾画ROI,然后自动匹配到ADC图上,软件自动计算瘤内及瘤周平均ADC值。ROI勾画原则:在肿瘤的最大层面及其相邻上下两层,手动进行ROI勾画。瘤内ROI放置的范围应尽量扩大,同时要避开出血、坏死和囊变等区域。瘤周ROI在上述层面直肠肿瘤原发灶壁外缘向外手动扩展5 mm内的系膜脂肪区域。对于肿瘤与周围组织边界不清的层面,由两名医师参照肿瘤在相邻清晰层面的位置共同商讨确定边界。肿瘤壁外系膜脂肪宽度不足5 mm者,则以实际存在的全部系膜脂肪区域作为瘤周ROI,但要求宽度≥2 mm;若<2 mm则剔除该层面,仅采用其他层面数据。尽量确保MAGiC T2WI和ADC图像上的ROI大小保持一致。测量示意图见图1。最终分析的结果是基于两位医生测量的上述三个层面瘤内T1值(T1内)、瘤周T1值(T1周)、瘤内T2值(T2内)、瘤周T2值(T2周)、瘤内PD值(PD内)、瘤周PD值(PD周)、瘤内ADC值(ADC内)及瘤周ADC值(ADC周)的平均值。

图1  男,80 岁,直肠癌患者。1A~1B:合成T2WI 图瘤内及瘤周感兴趣区(ROI)勾画,肿瘤累及整个肠圈,呈稍高信号;1C:纵向弛豫定量(T1 mapping)图;1D:横向弛豫定量(T2 mapping)图;1E:PD定量图;1F:复合灵敏度编码扩散加权成像图(b=800 s/mm2)显示整个肠圈呈弥散受限高信号;1G:表观扩散系数(ADC)伪彩图。
Fig. 1  An 80-year-old male patient with rectal cancer. 1A-1B: MAGiC T2-weighted images showing intratumoral and peritumoral region of interest (ROI) delineation. The tumor involves the entire bowel loop, presenting with a slightly hyperintense signal. 1C: The longitudinal relaxation time mapping (T1 mapping). 1D: The transverse relaxation time mapping (T2 mapping). 1E: Proton density (PD) mapping. 1F: Multiplexed sensitivity-encoding diffusion-weighted imaging (MUSE-DWI) at b = 800 s/mm2 demonstrates marked diffusion restriction in the entire bowel loop (hyperintense). 1G: Apparent diffusion coefficient (ADC) pseudo-color map.

1.4 NVI病理状态评估

       NVI由两名高年资(具有10年以上工作经验)病理医师参照第8版美国癌症联合委员会(American Joint Committee on Cancer, AJCC)结直肠癌临床和病理分类指南[13]独立评估(AJCC第9版提案的主要更新为纳入肿瘤沉积并重新平衡T/N分期权重[14, 15],不影响NVI的病理定义),包括神经侵犯(perineural invasion, PNI)和脉管侵犯(lymphovascular invasion, LVI)。PNI定义为肿瘤细胞浸润神经外膜、神经束膜或神经内膜;LVI定义为肿瘤细胞侵入淋巴管或血管腔,伴内皮细胞衬覆。PNI和/或LVI至少一项阳性者归为NVI阳性;两者均为阴性者归为NVI阴性。本研究将PNI和/或LVI阳性定义为NVI阳性,主要基于以下考虑:(1)两者均为肿瘤侵袭性行为的病理标志,术前临床决策中,PNI或LVI任一阳性均提示高危,需考虑强化治疗[1, 2, 3];(2)既往研究表明[16, 17],PNI和/或LVI阳性患者的无病生存期和总生存期显著劣于双阴性患者;(3)既往影像学研究已采用相同定义进行PNI/LVI的术前预测研究[18, 19]

1.5 统计学分析

       采用SPSS 27.0、MedCalc 20.0.3及R(版本4.1.6)软件对所有数据进行统计学分析。采用组内相关系数(intra-class correlation coefficient, ICC)评估两位医生测量各定量参数的一致性。ICC类型为双向随机效应模型,采用绝对一致性定义,报告平均测量值的ICC值,即ICC(2, 2)。选用ICC值为0.81~1.00的参数。两位医生在测量时互不知晓对方结果及病理结果,采用双盲法评估。最终建模采用两位医生测量的平均值。计量资料单因素分析采用独立样本t检验或Mann-Whitney U检验,不符合正态分布的数据使用Mann-Whitney U检验。符合正态分布的用独立样本t检验。P<0.05为差异有统计学意义。计数资料单因素分析,组间比较采用卡方检验或Fisher精确检验。应用二元logistic回归筛选独立影响因子并建立联合诊断模型,采用Enter法(强制进入)纳入变量。纳入回归前,采用方差膨胀因子(variance inflation factor, VIF)评估变量间共线性,VIF<5视为无显著共线性。采用Bootstrap自抽样1000次对联合模型进行内部验证,采用百分位法(取第2.5和第97.5百分位数)计算95% CI。绘制受试者工作特征(receiver operating characteristic, ROC)曲线,计算曲线下面积(area under the curve, AUC)及95%置信区间(confidence interval, CI)、敏感度、特异度及最佳截断值。使用DeLong检验比较不同模型AUC的差异。检验标准为α=0.05。

2 结果

2.1 基本临床及影像资料表现

       初始纳入134例直肠癌患者,术前接受新辅助放化疗排除38例、无本院NVI病理排除11例、病理为非腺癌的其他类型直肠肿瘤排除10例、病灶较小(直径<1 cm)或图像质量较差排除3例、合并其他肿瘤排除2例,最终70例患者纳入本研究。根据病理NVI状态,阴性组有39例,阳性组有31例(其中单纯PNI阳性6例、单纯LVI阳性12例、PNI及LVI同时阳性13例)。两组间的性别、年龄、肿瘤位置、长径、厚度、MRF状态、癌胚抗原(carcinoembryonicantigen, CEA)及糖类抗原19-9(carbohydrate antigen19-9, CA19-9)水平差异均无统计学意义(P>0.05)(表1)。

表1  两组间临床及MRI特征对比
Tab. 1  Comparison of clinical and MRI characteristics between the two groups

2.2 两组瘤内及瘤周MAGiC及MUSE-DWI定量参数比较

       两位医生在各定量参数观察间一致性良好(表2)。两组间T1内、T1周、T2内、PD周、ADC内及ADC周差异具有统计学意义(P<0.05),详见表3

表2  瘤内及瘤周MAGiC及MUSE-DWI定量参数一致性检验
Tab. 2  Consistency test of intratumoral and peritumoral quantitative parameters derived from MAGiC and MUSE-DWI
表3  瘤内及瘤周MAGiC及MUSE-DWI定量参数组间比较
Tab. 3  Comparison of intratumoral and peritumoral MAGiC and MUSE-DWI quantitative parameters between the two groups

2.3 直肠癌NVI独立影响因素筛选及效能评估

       将上述单因素分析中P值<0.05的因子行共线性诊断,显示各纳入变量的VIF均<2(T1周=1.71,T2内=1.15,PD周=1.31,ADC内=1.14,ADC周=1.64),提示无明显多重共线性。并将其全部纳入二元logistic回归,结果显示诊断直肠癌NVI的独立影响因子为T1周、T2内及PD周(表4)。其三者联合诊断AUC(95% CI)为0.906(0.812~0.963),稍高于瘤内参数联合(T1内+T2内+ADC内)及瘤周参数联合(T1周+PD周+ADC周)的诊断效能,但DeLong检验显示差异无统计学意义(Z=1.497、1.221,P=0.134、0.222)。瘤周参数联合诊断NVI的AUC值稍高于瘤内参数联合,但差异无统计学意义(Z=0.562,P=0.574)(表5图2)。

图2  各影响因素单独(2A)及瘤内参数联合、瘤周参数联合、瘤内瘤周参数联合(T1内+T2内+ADC内、T1周+PD周+ADC周、T1周+T2内+PD周)(2B)诊断直肠癌NVI的ROC曲线。T1内为瘤内纵向弛豫时间;T1周为瘤周纵向弛豫时间;T2内为瘤内横向弛豫时间;PD周为瘤周质子密度;ADC内为瘤内表观扩散系数;ADC周为瘤周表观扩散系数;NVI为神经脉管侵犯;ROC为受试者工作特征。
Fig. 2  ROC curves for diagnosing rectal cancer NVI using individual parameters (2A), combined intratumoral parameters, combined peritumoral parameters, and combined intratumoral and peritumoral parameters (T1intra + T2intra + ADCintra, T1peri + PDperi + ADCperi, T1peri + T2intra + PDperi) (2B). ROC: receiver operating characteristic; NVI: neurovascular invasion; T1intra: intratumoral longitudinal relaxation time; T1peri: peritumoral longitudinal relaxation time; T2intra: intratumoral transverse relaxation time; PDperi: peritumoral proton density; ADCintra: intratumoral apparent diffusion coefficient; ADCperi: peritumoral apparent diffusion coefficient.
表4  直肠癌NVI影响因素二元logistic回归分析
Tab. 4  Binary logistic regression analysis of factors influencing NVI in rectal cancer
表5  各参数诊断直肠癌NVI的效能比较
Tab. 5  Comparison of diagnostic performance of each parameter for NVI in rectal cancer

2.4 瘤内-瘤周联合模型验证与临床效用评估

       采用Bootstrap自抽样1000次对联合模型进行内部验证。校正后AUC为0.894(95% CI:0.873~0.906)。校准曲线接近理想对角线(图3A)。Bootstrap校正后的敏感度为82.7%,特异度为81.4%,Brier评分为0.139,校准截距为-0.008,校准斜率为0.856,Hosmer-Lemeshow检验P=0.615,提示模型校准度及拟合度良好。决策曲线分析(decision curve analysis, DCA)显示,在风险阈值为0.05~0.95范围内,联合模型净收益优于“全部干预”及“无干预”策略(图3B),表明该模型具有较好的临床实用价值。

图3  联合模型的校准曲线(3A)与决策曲线分析(3B)。在风险阈值为0.05~0.95范围内,联合模型净收益优于“全部干预”及“无干预”策略。
Fig. 3  Calibration curve (3A) and decision curve analysis (DCA) (3B) of the combined model. The combined model yields a higher net benefit than both extreme strategies over a wide range of threshold probabilities (approximately 0.05 to 0.95), indicating favorable clinical utility.

2.5 亚组分析:LVI与PNI的预测效能

       为进一步验证NVI预测模型(T1周+T2内+PD周)PNI和LVI的独立预测价值,本研究进行了探索性亚组分析。结果显示:该模型在LVI亚组(25例阳性)中的AUC为0.891(95% CI:0.793~0.953),敏感度为80.00%,特异度为88.89%;在PNI亚组(19例阳性)中的AUC为0.827(95% CI:0.717~0.907),敏感度为57.89%,特异度为94.12%,均显示出良好的诊断效能。

3 讨论

       本研究首次采用快速、便捷且可重复性高的MAGiC序列,结合能有效克服直肠蠕动及气体伪影的MUSE-DWI技术,并将视野从瘤内特征拓展至瘤周微环境。结果证实,瘤内-瘤周联合策略(T1周+T2内+PD周)的AUC值最高,从影像定量层面印证了肿瘤-微环境互作在NVI发生中的核心地位[20, 21],为直肠癌NVI的术前无创精准分层提供了多维度影像学评估。

3.1 瘤内MAGiC及MUSE-DWI定量参数诊断直肠NVI的价值

       本研究的t检验及Mann-Whitney U检验结果显示,NVI阳性组的T1内、T2内及ADC内均显著低于阴性组。NVI阳性肿瘤通常具有较高的细胞密度、较大的核浆比及丰富的纤维间质;同时,细胞外间隙缩小、自由水减少及细胞内大分子蛋白浓度升高,进而引起T1、T2及ADC值减低[22, 23, 24, 25]。T2内被证实为NVI的独立影响因子,提示其可能是反映肿瘤细胞密集程度与侵袭性的敏感指标。T1内在两组比较中虽有差异,但因效应量小且样本量有限,在单因素logistic回归中未达显著性,故未纳入多因素模型。既往研究[19, 26, 27]表明,MAGiC及DWI定量参数与壁外静脉侵犯(extramural venous invasion, EMVI)及NVI相关,伴发EMVI及PNI的肿瘤T1、T2值更低,NVI阳性组ADC值显著降低,与本文一致。然而,现有MAGiC技术在直肠癌预后评估中尚存争议:ZHU等[28]发现EMVI阳性组的T2内显著升高;MA等[29]则未发现T1、T2值与EMVI存在显著关联。上述差异可能源于:(1)ROI定义不同,ZHU等仅勾画肿瘤最大层面,而本研究采用多层面平均;(2)纳入人群差异,ZHU等排除了下段直肠癌,而本研究涵盖全段直肠;(3)评估标准不同,MA等采用MRI征象而非病理金标准定义EMVI。这提示MAGiC评估NVI价值可能受ROI选择及选择偏倚影响,未来需多中心验证。

3.2 瘤周MAGiC及MUSE-DWI定量参数诊断直肠NVI的价值

       本研究发现,T1周及PD周是NVI的独立影响因子,单独诊断AUC分别为0.814和0.735。ZHU等[28]证实T1周在评估淋巴结转移及EMVI时显著增高,且伴有EMVI的直肠癌PD周较低,与本研究相近。PD周减低、T1周及ADC周升高,原因可能是肿瘤组织替代了原本具有短T1时间、富含可移动氢质子(-CH2-基团)且水扩散受限的系膜脂肪,同时肿瘤向外浸润引发瘤周间质纤维化、炎性浸润、促结缔组织增生及胶原沉积[30, 31, 32, 33, 34]。ZHU等[28]虽观察到EMVI阳性组的PD周较低,但差异不显著。笔者认为可能与ROI范围的设定有关:该研究选取瘤周15 mm区域,而本研究限定为紧邻肿瘤的5 mm区域。距离较远的区域微环境价值可能较低,这可能因为肿瘤免疫微环境主要分布于边缘[35]。本研究发现“分离现象”,与YUAN等[33]的研究相似:NVI阳性组的ADC内显著降低,而ADC周反而升高。但logistic回归显示无统计学意义,这可能是ADC值预测NVI的价值小于MAGiC定量值及样本量较小所致,未来需扩大样本量验证。

3.3 瘤内参数联合、瘤周参数联合及瘤内瘤周参数联合诊断直肠NVI的价值

       本研究中,瘤周参数联合(AUC=0.868)在诊断效能上数值优于瘤内参数联合(AUC=0.829)。虽然差异未达统计学显著性,这可能受限于本研究的样本量,导致统计效能不足以检出中等程度的差异。但是,从病理学角度看,NVI的本质是肿瘤向周围浸润生长,瘤周区域最先发生微环境改变[36]。而瘤内参数更多反映肿瘤自身增殖活性,对“外向性”侵袭敏感性可能较低。LIANG等[37]证实瘤周影像组学模型预测局部晚期直肠癌预后的效能优于瘤内模型(C指数:0.754 vs. 0.670);BAI等[38]在前列腺癌中的双中心研究也得出类似结论,即瘤周特征预测包膜外侵犯的效能优于瘤内特征(AUC:0.682 vs. 0.556)。本文三个独立影响因子(T1周、T2内、PD周)分别来自瘤周与瘤内,联合诊断AUC达0.906,数值上高于单一区域联合。联合模型与单一区域模型间差异虽未达统计学显著性,但联合模型整合了肿瘤核心区的高细胞密度特征(T2内降低)与侵袭前沿区的微环境改变特征(T1周增高、PD周降低),实现了对NVI更全面的影像表征。该互补增益效应已有验证:LI等[39]联合瘤内与瘤周T2WI影像组学预测可切除直肠癌预后的AUC(0.77)高于单一区域(0.71,0.74);QIN等[40]指出整合瘤内特征与系膜血管、淋巴结特征的联合模型在新辅助治疗反应预测中表现最优。同时本研究预测模型在LVI及PNI亚组中均具有良好的预测效能(AUC=0.891、0.827),进一步验证了该联合模型的稳健性及临床适用性。PNI亚组AUC略低于LVI亚组,可能与PNI的病理机制更复杂、样本量较小有关。

3.4 本研究的局限性

       本研究存在以下局限:(1)本研究为初步研究,瘤周区域固定为外扩5 mm,未探索最佳采样范围,需进一步开展多范围对比且可解释性的研究;(2)样本量有限,仅完成了Bootstrap内部验证,联合模型效能可能被高估,同时DeLong检验提示三组模型间差异未达到统计学意义,因此联合模型的潜在优势需在更大样本及多中心外部数据中进一步验证;(3)由于LVI和PNI的阳性例数有限,亚组分析仅为探索性结果,本研究主要采用NVI联合终点进行分析,虽能够提高阳性事件数并增强模型稳定性,但未分别建立PNI及LVI预测模型,未来仍需扩大样本进一步开展针对不同病理终点的独立预测研究;(4)未设置传统DWI序列对照,无法直接证实MUSE-DWI的增量价值,需前瞻性对照研究;(5)未引入影像组学特征与MAGiC定量参数的融合分析,未来可探索深度结合策略。

4 结论

       本研究前瞻性证实,MAGiC联合MUSE-DWI定量参数可在术前有效评估直肠癌NVI。瘤内联合瘤周多区域定量指标能够同时反映肿瘤及侵袭微环境特征;尽管联合模型与单一区域模型诊断效能差异无统计学意义,但可为直肠癌术前风险分层及个体化治疗决策提供更加全面客观的定量影像学依据。

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