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临床研究
全容积ADC直方图分析联合ADC值术前预测直肠癌肿瘤沉积的价值
冯飞文 刘原庆 胡粟 胡春洪

Cite this article as: FENG F W, LIU Y Q, HU S, et al. The value of whole-volume ADC histogram analysis combined with ADC value in preoperatively prediction of tumor deposits in rectal cancer[J]. Chin J Magn Reson Imaging, 2024, 15(4): 88-92.本文引用格式:冯飞文, 刘原庆, 胡粟, 等. 全容积ADC直方图分析联合ADC值术前预测直肠癌肿瘤沉积的价值[J]. 磁共振成像, 2024, 15(4): 88-92. DOI:10.12015/issn.1674-8034.2024.04.014.


[摘要] 目的 探讨基于肿瘤全容积表观扩散系数(apparent diffusion coefficient, ADC)直方图参数联合ADC值在术前预测直肠癌肿瘤沉积(tumor deposits, TDs)中的应用价值。材料与方法 回顾性分析苏州大学附属第一医院2016年6月至2023年6月术前行直肠MRI检查且经病理确诊的111例直肠癌患者的临床及影像学资料,依据病理结果将其分为TDs阳性组(n=30)和TDs阴性组(n=81),在ADC图像上手动勾画每一层肿瘤病灶感兴趣区(region of iterest, ROI)并提取ADC直方图参数,包括第10百分位数(ADC10%)、第90百分位数(ADC90%)、最大值(ADCmax)、最小值(ADCmin)、均数(ADCmean)、中位数(ADCmedian)、峰度及偏度;同时测量肿瘤最大层面的平均ADC值。分析比较两组患者间ADC值及ADC直方图参数的差异,将差异具有统计学意义的参数纳入多因素logistic回归分析构建联合模型,利用ROC曲线分析ADC值、全容积ADC直方图参数及两者联合模型的预测效能。采用DeLong检验比较各AUC间的差异。结果 ADC值、ADC10%、ADC90%、ADCmax、ADCmean、ADCmedian及峰度在TDs阳性组和阴性组间差异具有统计学意义(P<0.05),以ADC90%的预测效能最高(AUC、敏感度、特异度分别为0.778、80.0%、65.4%)。由ADC值、ADC10%、ADC90%、ADCmean构建的联合模型AUC、敏感度、特异度分别为0.940、86.7%、93.8%,其诊断效能优于ADC值(AUC为0.645)及各全容积ADC直方图参数(AUC为0.649~0.778),差异均有统计学意义(P<0.05)。结论 全容积ADC直方图参数及肿瘤最大层面的ADC值可用于术前预测直肠癌TDs,尤其当两者联合时具有较高的预测效能。
[Abstract] Objective To explore the value of tumoral whole-volume apparent diffusion coefficient (ADC) histogram parameters combined with ADC value in preoperative prediction of tumor deposits (TDs) in rectal cancer.Materials and Methods The clinical and radiological data of 111 patients with pathologically confirmed rectal cancer who underwent preoperative rectal MRI examinations from June 2016 to June 2023 were retrospectively analyzed. The patients were grouped as TDs-positive group (n=30) and TDs-negative group (n=81) according to the pathological results. ROI was manually delineated on each slice of the tumor on the ADC images and histogram parameterswere obtained, including the ADC10%, ADC90%, maximum value (ADCmax), minimum value (ADCmin), mean value (ADCmean), median value (ADCmedian), kurtosis, and skewness. And the ADC value of the largest level of the tumor was measured. The differences in ADC value and ADC histogram parameters between the two groups were compared. A combined model was constructed based on factors with statistically significant differences using multivariate logistic regression analysis. Receiver operating characteristic curve (ROC) analysis was used to analyze the predictive performance of ADC value, whole-volume ADC histogram parameters, and the combined model. DeLong test was used to compare the differences of AUCs.Results The ADC value, ADC10%, ADC90%, ADCmax, ADCmean, ADCmedian, and kurtosis were statistically different between the TDs-positive and TDs-negative groups (P<0.05). ADC90% had the highest predictive performance with an AUC of 0.778 (sensitivity, 80.0%; specificity, 65.4%). The diagnostic performance of the combined model (AUC, 0.940; sensitivity, 86.7%; specificity, 93.8%) was superior to that of ADC value alone (AUC, 0.645) and whole-volume ADC histogram parameters (AUC ranging from 0.649 to 0.778) (P<0.05).Conclusions Whole-volume ADC histogram parameters and the ADC value of the largest level of tumor can be used for preoperative prediction of TDs in rectal cancer, and the combined model can improve the predictive performance.
[关键词] 胃肠道肿瘤;直肠癌;肿瘤沉积;扩散加权成像;直方图;磁共振成像
[Keywords] gastrointestinal neoplasms;rectal cancer;tumor deposits;diffusion-weighted imaging;histogram;magnetic resonance imaging

冯飞文 1   刘原庆 1, 2   胡粟 1, 2*   胡春洪 1, 2  

1 苏州大学附属第一医院放射科,苏州 215006

2 苏州大学影像医学研究所,苏州 215006

通信作者:胡粟,E-mail:husu@suda.edu.cn

作者贡献声明:胡粟设计本研究的方案,对稿件重要内容进行了修改;冯飞文起草和撰写稿件,获取、分析和解释本研究的数据;刘原庆、胡春洪获取、分析或解释本研究的数据,对稿件重要内容进行了修改;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。


收稿日期:2023-10-13
接受日期:2024-03-21
中图分类号:R445.2  R735.37 
文献标识码:A
DOI: 10.12015/issn.1674-8034.2024.04.014
本文引用格式:冯飞文, 刘原庆, 胡粟, 等. 全容积ADC直方图分析联合ADC值术前预测直肠癌肿瘤沉积的价值[J]. 磁共振成像, 2024, 15(4): 88-92. DOI:10.12015/issn.1674-8034.2024.04.014.

0 引言

       结直肠癌是消化系统常见的恶性肿瘤之一,其中近40%为直肠癌,其发病率及死亡率逐年增高[1]。肿瘤沉积(tumor deposits, TDs)是指结直肠癌原发病灶周围脂肪组织或系膜内与原发肿瘤不连续的孤立肿瘤结节,其位于原发病灶淋巴引流区域内且病理上无明显的淋巴、血管及神经结构[2]。TDs与直肠癌侵袭性强、局部复发和远处转移发生率高密切相关,是预后不佳的独立危险因素[3, 4, 5],第八版美国癌症联合委员会(American Joint Committee on Cancer, AJCC)结直肠癌分期指南将存在TDs而淋巴结转移阴性定义为N1C期,将其临床分期至少划分为Ⅲ期,此类患者常推荐术前新辅助放化疗以预防局部复发及远处转移[6, 7, 8]。因此术前准确评估直肠癌TDs状态对精准评估患者临床分期、制订合理诊疗决策及改善预后具有重大意义。

       表观扩散系数(apparent diffusion coefficient, ADC)是扩散加权成像(diffusion-weighted imaging, DWI)生成的量化组织内水分子扩散受限程度的参数,已广泛应用于直肠癌诊疗中,有助于无创评估病灶生物学特征并指导临床决策[9, 10, 11]。但多数研究仅测量病灶单一层面的平均ADC值,并不能准确、全面反映肿瘤的整体异质性[12, 13],而基于肿瘤全容积的ADC直方图分析可从多维度分析感兴趣容积(volume of interest, VOI)内病灶所有体素分布特征,一方面可以避免传统单层面感兴趣区(region of iterest, ROI)勾画的主观性,另一方面可以提供有关肿瘤微环境更丰富的信息,进而更准确、全面反映肿瘤内部结构的异质性[14, 15, 16]。已有研究表明ADC直方图参数在预测直肠癌的KRAS基因状态、病理T/N分期以及免疫组化标志物等方面具有一定的研究价值[17, 18],但这些研究样本量小,并且仅探讨了部分ADC直方图参数的价值,未充分挖掘ADC直方图分析在直肠癌中的应用价值。目前,ADC直方图分析在术前评估直肠癌分期、组织学分级、淋巴结转移及新辅助化疗疗效等方面均展现出了良好的临床应用价值[19, 20, 21, 22]。但全容积ADC直方图分析能否用于术前预测直肠癌TDs尚未见相关文献报道,因此,本研究旨在探讨基于肿瘤全容积的ADC直方图参数联合ADC值术前预测直肠癌TDs的临床价值。

1 材料与方法

1.1 研究对象

       回顾性分析2016年6月至2023年6月苏州大学附属第一医院经病理确诊的111例直肠癌患者的临床及MRI图像资料。纳入标准:(1)行直肠癌根治性切除术且经病理确诊为直肠腺癌;(2)术前未接受抗肿瘤治疗;(3)术前行直肠MRI常规及DWI扫描且图像信息完整。排除标准:(1)临床或MRI资料不全者;(2)图像伪影大或无法识别病灶完成分析者;(3)同时合并其他恶性肿瘤者。本研究遵守《赫尔辛基宣言》,经苏州大学附属第一医院伦理委员会批准,免除受试者知情同意,批准文号:2023422。

1.2 研究方法

1.2.1 MRI图像扫描

       所有直肠MRI检查均使用3.0 T磁共振扫描仪(Signa HDxt,GE,美国)进行,采用16通道体表相控阵线圈,患者取仰卧位、头先进,扫描序列及参数:(1)轴位T1WI序列,TR 400~700 ms,TE 8~10 ms,FOV 280 mm×280 mm,矩阵512×512,层厚4 mm,层间距3 mm;(2)轴位、冠状位和矢状位T2WI序列,TR 3000~5100 ms,TE 90~119 ms,FOV 280 mm×280 mm,矩阵512×512,层厚4 mm,层间距3 mm;(3)DWI序列,TR 4500 ms,TE 65 ms,层厚3 mm,层间距3 mm,FOV 280 mm×280 mm,矩阵256×256,b值取0及1000 s/mm2

1.2.2 MRI图像分析及处理

       将ADC图像以DICOM格式导入ITK-SNAP软件(https://www.itksnap.org)中,参考T2WI图像,沿病灶边缘逐层勾画ROI并生成三维VOI(图1),勾画时注意避开肠腔内液体、气体及内容物。利用Feature Explorer软件(V4.2.0,https://github.com/salan668/FAE)提取每个病灶VOI的一阶直方图特征:第10百分位数(ADC10%)、第90百分位数(ADC90%)、最大值(ADCmax)、最小值(ADCmin)、均数(ADCmean)、中位数(ADCmedian)、峰度及偏度。于肿瘤最大层面测量病灶的平均ADC值,每个病灶测量三次取平均值。病灶ROI勾画以及ADC值测量由两名分别具有5年工作经验的放射科住院医师和10年工作经验的副主任医师完成,意见出现分歧时经协商决定。

图1  直肠癌原发灶VOI勾画示意图。在直肠癌原发灶ADC序列(b值为1000 s/mm2)图像(1A)上沿病灶边缘逐层勾画ROI(1B),生成肿瘤全容积的VOI(1C)。VOI:三维感兴趣区;ADC:表观扩散系数;ROI:感兴趣区。
Fig. 1  Schematic diagram of VOI outlining for rectal cancer primary foci. ADC (b-value is 1000 s/mm2) image (1A) of rectal cancer primary foci, layer-by-layer outlining of ROIs along the edge of the lesion (1B), generation of VOIs for the whole volume of the tumor (1C). VOI: volume of interest; ADC: apparent diffusion coefficient; ROI: region of interest.

1.2.3 TDs的病理评估

       TDs状态由具有10年工作经验的病理科副主任医师参照AJCC第八版结直肠癌分期指南[2]完成。根据病理结果分为TDs阳性组和TDs阴性组。

1.3 统计学分析

       统计学分析采用SPSS 23.0(IBM, Armonk, NY, USA)、MedCalc 15.8软件。采用组内相关系数(intra-class correlation coefficient, ICC)评价两名医师ADC直方图参数测量值的一致性,ICC值≤0.40为一致性差,0.40<ICC值≤0.60为一致性中等,0.60<ICC值≤0.75为一致性好,0.75<ICC值≤1.00为一致性非常好。符合正态分布的计量资料采用独立样本t检验进行组间比较,不符合正态分布者组间比较则采用Mann-Whitney U检验。计数资料的组间比较采用χ2检验进行。采用二元多因素logistic回归分析,利用逐步向后选择法从组间差异分析具有统计学差异的参数中筛选出独立预测因素,分别建立基于ADC直方图参数、ADC直方图参数联合ADC值的逻辑回归模型。各参数及模型TDs预测效能的比较采用ROC曲线分析,分别计算曲线下面积(area under the curve, AUC)、95%可信区间(confidence interval, CI)、敏感度及特异度。AUC间差异的比较采用DeLong检验。P<0.05为差异具有统计学意义。

2 结果

2.1 一致性评价

       2名医师ADC直方图参数测量值的一致性非常好(ICC>0.75)。ADC值、ADC10%、ADC90%、ADCmax、ADCmin、ADCmean、ADCmedian、峰度及峰度的ICC分别为0.799(0.739~0.814)、0.882(0.851~0.890)、0.813(0.792~0.901)、0.854(0.801~0.892)、0.798(0.749~0.801)、0.888(0.821~0.899)、0.791(0.745~0.829)、0.862(0.790~0.892)、0.834(0.800~0.876)。

2.2 临床病理特征

       111例直肠癌患者中,男71例、女40例,年龄32~86岁,平均62岁;TDs阳性组30例,TDs阴性组81例。两组患者的年龄及性别间差异无统计学意义(P>0.05;表1),而肿瘤病理T分期及分化程度的差异有统计学意义(P<0.05;表1)。

表1  TDs阳性及阴性组直肠癌患者临床病理特征
Tab. 1  Clinicopathological characteristics of rectal cancer patients in TDs-positive and negative groups

2.3 全容积ADC直方图参数及ADC值分析

       TDs阳性组与TDs阴性组间ADCmin及偏度的差异无统计学意义(P>0.05)。TDs阳性组ADC10%、ADC90%、ADCmax、ADCmean、ADCmedian均大于TDs阴性组(P<0.05),而其峰度小于TDs阴性组(P<0.05)。TDs阳性组ADC值小于TDs阴性组(P<0.05;表2)。

表2  TDs阳性及阴性组直肠癌患者ADC值及ADC直方图参数比较
Tab. 2  Comparison of ADC values and ADC histogram parameters in rectal cancer patients in TDs-positive and negative groups

2.4 全容积ADC直方图模型、ADC值联合ADC直方图参数联合模型的构建

       多因素logistic回归分析结果发现全容积ADC直方图模型纳入四个独立预测因素(ADC10%、ADC90%、ADCmax、ADCmean),联合模型亦纳入四个独立预测因素(ADC值、ADC10%、ADC90%、ADCmean),详见表3

表3  ADC直方图模型、基于ADC值及ADC直方图参数的联合模型预测TDs的多因素logistic回归分析结果
Tab. 3  Results of multivariate logistic regression analysis of ADC histogram model, combined model based on ADC values and ADC histogram parameters to predict TDs

2.5 各参数及逻辑回归模型的预测效能

       全容积ADC直方图各参数预测TDs的AUC范围为0.649~0.778,以ADC90%的预测效能最高(敏感度为80.0%,特异度为65.4%)(表4图2)。ADC值及ADC直方图模型预测TDs的AUC分别为0.645和0.844(表4图3)。ADC值联合全容积ADC直方图参数的联合模型预测效能最高(AUC为0.940),其AUC显著高于ADC值、ADC10%、ADC90%、ADCmax、ADCmean、ADCmedian、峰度及ADC直方图模型(Z=5.824、3.749、3.278、4.658、3.636、3.737、4.483、2.619,P均<0.01)。当阈值取0.42时,其预测TDs的敏感度为86.7%,特异度为93.8%(表4图3)。

图2  ADC直方图参数诊断直肠癌肿瘤沉积的ROC曲线。
图3  ADC值、ADC直方图模型及联合模型诊断直肠癌肿瘤沉积的ROC曲线。ADC:表观扩散系数;ROC受试者工作特征;AUC:曲线下面积;ADC10%、ADC90%、ADCmax、ADCmean、ADCmedian分别为ADC值的第10、90百分位数及最大值、平均值、中位数。
Fig. 2  ROC curves of ADC histogram parameters for diagnosis of tumor deposits in rectal cancer.
Fig. 3  ROC curves of ADC values, ADC histogram model and combined model for diagnosis of tumor deposits in rectal cancer. ADC: apparent diffusion coefficient; ROC: receiver operating characteristic; AUC: area under the curve; ADC10%, ADC90%, ADCmax, ADCmean, and ADCmedia are the 10th, 90th percentiles, the maximum, mean, and median values of the ADC value, respectively.
表4  ADC值、ADC直方图参数及联合模型对直肠癌TDS的诊断效能
Tab. 4  Diagnostic efficacy of ADC values, ADC histogram parameters and combined models for TDs in rectal cancer

3 讨论

       本研究基于全容积ADC直方图参数及ADC值构建联合模型术前预测直肠癌TDs状态,结果显示基于ADC值、ADC10%、ADC90%、ADCmean的联合模型预测效能最高(AUC、敏感度、特异度分别为0.940、86.7%、93.8%),表明全容积ADC直方图分析在评估直肠癌TDs状态具有良好的临床应用价值。

3.1 全容积直方图参数预测直肠癌TDs的效能分析

       本研究结果显示各全容积ADC直方图参数中,ADC10%、ADC90%、ADCmax、ADCmean、ADCmedian及峰度对直肠癌TDs均具有较好的预测价值,以ADC90%的预测效能最高。百分位数是用来描述VOI内体素分布的集中位置的参数,ADC90%代表整个瘤体内90%的ADC值小于该值,代表病灶内ADC值较高之处。本研究结果显示TDs阳性组ADC10%、ADC90%、ADCmax、ADCmean、ADCmedian大于TDs阴性组,其可能原因如下:一方面TDs阳性组病灶生长活跃,血供更加丰富[9, 23],导致病灶内水分子扩散受限程度相对减低[24];另一方面TDs阳性组病灶侵袭性更强,肿瘤生长速度不均匀,缺氧坏死多见[25],导致其各项ADC参数值较高[26]。峰度可反映病灶内部整体ADC值的分布状态,代表VOI体素组成的复杂性及病灶内部的异质性,峰度越小,代表参数分布越不均匀[27]。本研究发现TDs阳性组ADC峰度小于TDs阴性组,说明TDs阳性组整个瘤体内部参数分布更不均匀,提示其病灶内部的异质性更高[3]

       本研究发现TDs阳性组病灶最大层面平均ADC值小于TDs阴性组,这与YUAN等[10]结果一致,提示TDs阳性组最大层面病灶内部细胞排列密集、密度高,但其预测TDs的效能相对较低(AUC=0.645),且敏感度仅50%,说明仅通过测量最大层面的平均ADC值并不能客观、全面反映病灶水分子扩散受限特征和整体异质性,难以实现对TDs的准确预测。

       本研究构建的ADC直方图参数模型同样显示出良好的预测效能(AUC为0.844),说明联合ADC直方图参数可全面反映病灶内部微观的病理生理改变。进一步联合病灶最大层面平均ADC值及直方图参数构建联合模型,其预测效能显著高于各全容积ADC直方图参数、ADC值及ADC直方图参数模型,这表明联合模型可全面、客观反映肿瘤侵袭性及内部结构异质性,有效提高术前准确评估直肠癌TDs状态的预测效能。

3.2 本研究的局限性

       本研究尚存在一些局限性:(1)本研究为回顾性研究,不可避免存在选择偏移;(2)本研究样本量相对较少,今后需进一步扩大样本量验证本研究的结果;(3)本研究仅分析了b值为1000 s/mm2的DWI生成的ADC图像,未探讨不同b值的ADC直方图参数对TDs的预测效能。

4 结论

       综上所述,全容积ADC直方图参数(ADC10%、ADC90%、ADCmax、ADCmean、ADCmedian及峰度)在术前预测直肠癌TDs中有一定的应用价值,联合ADC值及全容积ADC直方图参数构建的联合模型预测效能最佳,有望成为术前无创准确评估直肠癌TDs状态的有效工具。

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