分享:
分享到微信朋友圈
X
临床研究
基线磁共振T2WI纹理预测晚期直肠癌转化治疗原发灶疗效的应用研究
王铮 孟令候 李强 李丽娅 田连芬 梁彬玲 周传集

Cite this article as: Wang Z, Meng LH, Li Q, et al. Application study of MRI T2WI texture baseline predicting the efficacy of advanced rectal cancer transformation therapy for primary tumors[J]. Chin J Magn Reson Imaging, 2022, 13(1): 42-47, 53.本文引用格式:王铮, 孟令候, 李强, 等. 基线磁共振T2WI纹理预测晚期直肠癌转化治疗原发灶疗效的应用研究[J]. 磁共振成像, 2022, 13(1): 42-47, 53. DOI:10.12015/issn.1674-8034.2022.01.009.


[摘要] 目的 探讨基线磁共振成像(magnetic resonance imaging,MRI) T2WI图像纹理分析在晚期直肠癌转化治疗原发灶疗效的预测价值。材料与方法 回顾性分析临床及病理证实为晚期直肠癌的患者66例,基线行盆腔MRI平扫、增强及扩散加权成像(diffusion weighted imaging,DWI)检查。根据平扫及增强图像明确肿瘤的部位及范围,运用Mazda软件提取T2WI图像感兴趣区(region of interest,ROI)纹理,分别运用线性判别分析(linear discriminant analysis,LDA)、非线性判别分析(nonlinear discriminant analysis,NDA)和主要成分分析(principal component analysis,PCA)3种提取方法进行判别分类,筛选出最优方法进行纹理提取。结合术后病理,比较晚期直肠癌患者原发灶疗效敏感组与不敏感组基线形态学特征,比较两组T2WI序列图像纹理特征,构建疗效预测模型。结果 66例晚期直肠癌患者原发灶术后病理肿瘤退缩分级(pathological tumor regression grade,pTRG)显示:pTRG 0级9例,pTRG 1级8例,pTRG 2级35例,pTRG 3级14例,其中敏感组(pTRG 0~2级) 52例和不敏感组(pTRG 3级) 14例。两组患者原发灶累及肠段、与腹膜反折关系、纵向累及长度、占肠腔环周比例、斜轴位最大厚度、肿瘤下缘距肛缘的距离差异均无统计学意义(P均>0.05);Fisher纹理特征提取法下的NDA分类方法误判率最低,故运用该方法提取图像纹理。晚期直肠癌转化治疗原发灶不同疗效组别各纹理特征单因素分析显示:第一百分位数(Percentile,Perc 1%)、S (2,0) DifEntrp、S (3,0) InvDfMom、S (3,-3) SumAverg、S (4,0) InvDfMom、S (4,-4) SumAverg、S (5,0) InvDfMom、S (5,-5) SumAverg、S (2,2) SumVarnc各指标差异均有统计学意义(P均<0.05),S (2,2) SumVarnc、S (3,0) DifEntrp差异无统计学意义(P=0.05、0.052);将单因素分析差异有统计学意义的指标纳入Logistic模型进行多因素分析显示:Perc 1%、S (5,0) InvDfMom为晚期直肠癌原发灶转化治疗不敏感的独立预测因子,运用上述因子构建晚期直肠癌原发灶转化治疗不敏感预测模型曲线下面积(area under the curve,AUC)为0.812,敏感度为92.90%,特异度为60.80%。结论 基于MRI Fisher提取法所提取的T2WI图像纹理特征有助于预测晚期直肠癌原发灶转化治疗疗效,为患者个体化治疗方案的制定提供有价值的参考信息。
[Abstract] Objective To explore the predictive value of baseline magnetic resonance imaging (MRI) T2WI image texture analysis in the treatment of advanced rectal cancer for the primary tumor.Materials and Methods: Retrospective analysis of 66 patients with advanced rectal cancer confirmed clinically and pathologically. All patients underwent pelvic MRI scan, enhancement and diffusion weighted imaging (DWI) examinations before operation. According to the plain scan and enhanced images, the location and range of the tumor were identified, and the Mazda software was used to extract the region of interest (ROI) texture in the T2WI image, and linear discriminant analysis (LDA) and nonlinear discriminant analysis (linear discriminant analysis) were used respectively. Discriminant analysis (NDA) and principal component analysis (PCA) are three extraction methods for discriminative classification, and the best method is selected for texture extraction. Combined with postoperative pathology, the baseline morphological characteristics of the primary focus of patients with advanced rectal cancer were compared between the sensitive group and the insensitive group, and the texture characteristics of the T2WI sequence images of the two groups were compared to construct a curative effect prediction model.Results The pathological tumor regression grade (pTRG) of 66 patients with advanced rectal cancer showed that 9 cases were pTRG 0, 8 cases were pTRG 1, 35 cases were pTRG 2, and 14 cases were pTRG 3. Among them, 52 cases were in the sensitive group (pTRG 0~2) and 14 cases were in the insensitive group (pTRG 3). There was no significant difference between the two groups of patients between the primary tumor involving the intestinal segment, the relationship with the peritoneum reflexion, the length of the longitudinal involvement, the proportion of the circumference of the intestinal cavity, the maximum thickness of the oblique axis, and the distance between the lower edge of the tumor and the anal edge (all P>0.05); the NDA classification method under the Fisher texture feature extraction method has the lowest misjudgment rate, so this method is used to extract the image texture. The univariate analysis of texture characteristics in different treatment groups of the primary tumor of advanced rectal cancer showed: the first percentile (Perc 1%), S (2, 0) DifEntrp, S (3, 0) InvDfMom, S (3, -3) SumAverg, S (4, 0) InvDfMom, S (4, -4) SumAverg, S (5, 0) InvDfMom, S (5, -5) SumAverg, S (2, 2) SumVarnc, all indicators were statistically different (P<0.05), S (2, 2) SumVarnc, S (3, 0) DifEntrp were not statistically different (P=0.05, 0.052); the indicators with differences in univariate analysis were included in Logistic multivariate analysis of the model showed that Perc 1% and S (5, 0) InvDfMom were independent predictors of insensitivity to transformation treatment of primary tumors of advanced rectal cancer, and the above factors were used to construct the prediction of insensitivity of primary tumors of advanced rectal cancer to transformation therapy. The area under the curve (AUC) of the model is 0.812, the sensitivity is 92.90%, and the specificity was 60.80%.Conclusions T2WI image texture features extracted based on MRIFisher extraction method can help predict the efficacy of primary tumor transformation therapy for advanced rectal cancer, and provide valuable reference information for the formulation of individualized treatment plans for patients.
[关键词] 晚期直肠癌;磁共振成像;转化治疗;病理分级;纹理分析;疗效预测
[Keywords] advanced rectal cancer;magnetic resonance imaging;translational therapy;pathological grading;texture analysis;curative effect prediction

王铮 1, 2, 3   孟令候 1#   李强 1, 2, 3*   李丽娅 4   田连芬 4   梁彬玲 4   周传集 4  

1 广西医科大学附属肿瘤医院,南宁 530021

2 广西影像医学临床医学研究中心,南宁 530021

3 广西临床重点专科(医学影像科),南宁 530021

4 广西医科大学研究生学院,南宁 530021

李强,E-mail:448954904@qq.com

#:共同第一作者

全体作者均声明无利益冲突。


基金项目: 广西医疗卫生适宜技术开发与推广应用项目 S2020093 广西重点研发计划 AB19110015 广西医药卫生自筹经费计划课题 Z20200403,Z20200445,Z20210418 广西医科大学教育教学改革立项项目 2020XJGZ05,2020XJGB16,2021XJGA14,2021XJGB56 广西医科大学青年科学基金项目 GXMUYSF202226
收稿日期:2021-05-20
接受日期:2021-11-09
中图分类号:R445.2  R735.37 
文献标识码:A
DOI: 10.12015/issn.1674-8034.2022.01.009
本文引用格式:王铮, 孟令候, 李强, 等. 基线磁共振T2WI纹理预测晚期直肠癌转化治疗原发灶疗效的应用研究[J]. 磁共振成像, 2022, 13(1): 42-47, 53. DOI:10.12015/issn.1674-8034.2022.01.009

       约10%~30%初始评估不可切除晚期直肠癌患者转化治疗结束后可实施根治性手术,较单纯姑息治疗而言,该部分患者预后及生活质量明显得以改善[1]。若能基线预测晚期直肠癌原发灶转化治疗疗效,不仅能为患者治疗方案制订提供决策信息,还能筛选出治疗不敏感的患者,有效减少不良治疗事件的发生,临床意义深远[2, 3]

       近年来,图像纹理分析技术通过计算机提取病变图像像素信息,以纹理数量及空间分布特征来反映病灶内部生物学特征,已逐步应用于疾病诊断、病理分级、预后评价等诸多方面。预测晚期直肠癌转化治疗原发灶局部获益的相关报道较少。因此,笔者通过分析比较晚期直肠癌患者转化治疗原发灶根治术后不同疗效组别间基线磁共振成像(magnetic resonance imaging,MRI) T2WI参数图纹理特征差异,旨在寻找疗效预测因子并构建预测模型,为临床诊疗提供有价值的参考信息。

1 资料与方法

1.1 一般资料

1.1.1 纳入标准

       回顾性分析2016年6月至2020年6月广西医科大学附属肿瘤医院结直肠肛门外科病区入院初诊为直肠癌的患者。纳入标准:(1)经直肠指诊及肠镜病理证实为直肠癌,临床及CT基线评估符合晚期直肠癌,不伴有明显出血、梗阻、穿孔等其他情况;(2)转化治疗结束后原发灶实施根治切除,术后病理标本满足病理肿瘤退缩分级(pathological tumor regression grade,pTRG)评估标准[4];(3)既往无盆腔及直肠手术史;(4)基线行盆腔MRI平扫、增强及扩散加权成像(diffusion weighted imaging,DWI)检查,影像资料完整、清晰,满足评估要求。本研究经过广西医科大学附属肿瘤医院医学伦理委员会批准(编号:LW2021085),免除受试者知情同意。

1.1.2 分组

       依据美国国立综合癌症网络(National Comprehensive Cancer Network,NCCN) 2020年版直肠癌治疗指南疗效评估pTRG标准,参考相关文献[5],将患者分为敏感组(pTRG分级0~2级)和不敏感组(pTRG 3级)。

1.1.3 转化治疗

1.1.3.1 FOLFOX6方案

       奥沙利铂85 mg/m2静脉输注,第1天用量400 mg/m2静脉输注,5-FU第1天用量400 mg/m2静脉推注,之后1200 mg/m2/d×2 d持续静脉输注(总量2400 mg/m2,输注时间46~48 h),每2周重复。

1.1.3.2 FOLFIRI方案

       奥沙利铂130 mg/m2静脉输注,第1天用量400 mg/m2静脉输注,5-FU第1天用量 1200 mg/m2/d×2 d持续静脉输注(总量2400 mg/m2,输注46~48 h),每2周重复化疗药物输注。转化治疗结束后8-11周原发灶实施根治性手术切除。

1.2 检查方法

1.2.1 扫描参数

       采用美国GE公司Discovery MR 750W 3.0 T磁共振成像仪、体部相控阵16通道线圈。检查前一天进流质饮食,检查当天须空腹。上机检查前15 min肌肉注射山莨菪碱注射液10~15 mg (北京北陆药业有限公司,10 mg/支)抑制胃肠道蠕动,注射完毕10~15 min后开始上机检查。MRI扫描序列参数:(1)盆腔常规扫描,T1WI序列:TR/TE=499/80 ms,层厚/层间距=4 mm/2 mm,间隔=2 mm,视野=320 mm×320 mm,矩阵=320×256,NEX=2次;T2WI序列:TR/TE=8404/102 ms,层厚/层间距=3 mm/0.3 mm,间隔=0.3 mm,视野=200 mm×220 mm,矩阵=288×256,NEX=2次;(2)直肠高分辨斜轴位T2WI序列扫描:TR/TE=6848/102 ms,层厚/层间距=3 mm/0.3 mm,间隔=0.3 mm,视野=200 mm×220 mm,矩阵=288×256,NEX=2次;(3) DWI扫描:扩散敏感系数b值分别为0、800 s/mm2,TR/TE=2800/71 ms,层厚=1 mm,层间隔=1 mm,视野=340 mm×320 mm,矩阵=128×128;(4)动态增强扫描:运用3D VIBE T1WI脂肪抑制序列,范围包括全盆腔,TR/TE=5.9 ms,层厚/层间距=4 mm/0.9 mm,间隔=0.9 mm,视野=320 mm×320 mm,矩阵=288×192,NEX=1次,无间隔横轴位动态扫描,共重复扫描8个时相。第1时相扫描结束后立即经肘静脉以1.5 mL/s流率团注对比剂钆喷酸葡胺注射液15 mL (德国拜耳医药有限公司,10 mg/支),随后再进行第2~8时相连续扫描,每个时相扫描时间持续20 s,无间隔横轴位动态扫描结束后再行冠矢状扫描。

1.2.2 图像纹理分析

       在未知晓病理报告的情况下,由两名结直肠肿瘤影像诊断经验丰富的副主任医师独立阅片,不一致者经讨论达成统一。勾画时须尽量避开病灶液化坏死区、肠腔及运动伪影,选择平扫直肠壁明显不规则增厚,T1WI稍低、T2WI高信号,DWI高信号,ADC图低信号,增强扫描明显强化的病灶实性部分最大层面,在直肠高分辨率T2WI序列图像上以手动方式勾画感兴趣区(region of interest,ROI),并将其视为目标图像,将该图像(图1)导入MaZdaver.4.6纹理分析软件进行分析,分别运用4种运算方法[Fisher、分类错误概率联合平均相关系数(probability of classification error+average correction coefficient,POE+ACC)、交互信息(mutual infoemation,MI)及分类树方法(classification tree test method,CTM)]对ROI进行纹理特征提取、降维、筛选,分别选出4组纹理特征参数。应用B11统计软件,根据以上4组对晚期直肠癌原发灶转化治疗疗效预测结果进行评估[3种方法:线性判别分析(linear discriminant analysis,LDA)、非线性判别分析(nonlinear discriminant analysis,NDA)、主要成分分析(principal component analysis,PCA)],评估结果以错判率表示[错判数/总例数(%)],错判率越小,说明该特征组合的诊断价值越高,本研究将以错判率最低的组合作为最优纹理提取方法。

图1  MaZda软件勾画晚期直肠癌患者T2WI图像肿瘤实性最大层面ROI示意图
图2  Perc 1%、S (5,0) InvDfMom预测晚期直肠癌转化治疗不敏感的曲线下面积为0.812,敏感度为92.90%,特异度为60.80%。
Fig. 1  MaZda software outlines the ROI diagram of the largest parenchymal level of the tumor in patients with advanced rectal cancer on the T2WI map.
Fig. 2  Perc 1%, S (5, 0) InvDfMom predicts that the area under the curve of advanced rectal cancer insensitivity to transformation therapy is 0.812, the sensitivity is 92.90%, and the specificity is 60.80%.

1.3 病理评估

       获取直肠肿瘤完整大体标本后,先用10%甲醛溶液进行固定,尽量取肿瘤实性部分进行编号、脱水、包埋、制片,经HE染色后镜下观察。选取镜下细胞成分较多的蜡块再次切片后行免疫组化检查。所有切片均由两名病理科高年资主治医师共同阅片,评估意见不一致时,以讨论后的意见作为最终诊断结论。

       疗效评估标准采用NCCN指南2020年版直肠癌治疗指南[4]pTRG标准,以肿瘤细胞数减少情况以及纤维化程度评估原发灶转化治疗疗效[4]:(1) pTRG 0级,无活性癌细胞残留;(2) pTRG 1级,单个或小簇癌细胞残留;(3) pTRG 2级,较多残留癌灶,部分间质纤维化;(4) pTRG 3级,仅少数或未见癌细胞消退表现,大量肿瘤细胞残留。

1.4 统计学处理

       采用SPSS 22.0软件进行统计分析。计量资料以数字表示,先运用Kolmogorov-Smirnov进行正态性分布检验,符合正态分布的数据以均数±标准差表示,非正态分布的数据以中位数±四分位间距表示,采用两独立样本t检验;计数资料以例表示,采用卡方检验。先将晚期直肠癌患者转化治疗后原发灶不同疗效组别间形态学表现、T2WI图像纹理特征进行单因素分析,再把单因素分析差异有统计学意义的指标纳入二元Logistic回归模型进行多因素分析,获取独立预测因子并构建预测模型,用ROC曲线分析预测不敏感组的诊断效能,以AUC表示,同时计算敏感度、特异度,并用Hosmer-Lemeshow检验评价模型的拟合优度。P<0.05为差异有统计学意义。

2 结果

2.1 不同疗效组别原发灶术后病理及一般资料比较

       66例晚期直肠癌患者原发灶转化治疗并术后病理示:pTRG 0级9例,pTRG 1级8例,pTRG 2级35例,pTRG 3级14例。依据文献[5]将晚期直肠癌患者转化治疗原发灶不同疗效分为敏感组(pTRG 0~2级)52例和不敏感组14例(pTRG 3级),两组患者临床资料差异无统计学意义(P均>0.05),组间比较存在可比性,具体结果见表1

表1  晚期直肠癌转化治疗后原发灶不敏感组与敏感组间一般资料比较(x¯±s)
Tab. 1  General data analysis between insensitive positive and sensitive groups for advanced rectal cancer (x¯±s)

2.2 不同疗效组别原发灶基线形态学表现比较

       两组患者原发灶累及肠段、与腹膜反折关系、纵向累及肠管长度、占肠腔环周比例、斜轴位最大厚度、肿瘤下缘距肛缘距离均没有统计学差异(P均>0.05),具体结果见表2

表2  晚期直肠癌不同疗效组别转化治疗前后原发灶形态学表现比较(n%)
Tab. 2  Comparison of primary tumor morphology before and after transformation treatment in different therapeutic groups of advanced rectal cancer (n%)

2.3 基于T2WI图像病灶纹理特征不同提取及分类统计方法的误判率比较

       分别运用PCA、LDA和NDA提取方法进行判别分类,筛选最优纹理提取方法。结果显示:以POE+ACC纹理特征提取方法下的LDA分类方法对应之误判率最高,而Fisher纹理特征提取方法下的NDA分类方法误判率最低。

表3  基于T2WI图像病灶纹理特征不同提取及分类统计方法的误判率(n%)
Tab. 3  The misjudgment rate based on different extraction and classification statistical methods of lesion texture features in T2WI images (n%)

2.4 T2WI图像基于Fisher纹理提取法在不同组别原发灶纹理特征单因素分析

       将纹理参数第一百分位数(Percentile,Perc 1%)、S (2,0) DifEntrp、S (3,0) InvDfMom、S (3,-3) SumAverg、S (4,0) InvDfMom、S (4,-4) SumAverg、S (5,0) InvDfMom、S (5,-5) SumAverg、S (2,2) SumVarnc、S (3,0) DifEntrp进行单因素分析显示:除S (2,2) SumVarnc、S (3,0) DifEntrp差异无统计学意义以外(P=0.05、0.052),余各项指标差异均有统计学意义(P均<0.05),具体结果见表4

表4  T2WI图像Fisher纹理提取法在不同组别原发灶各纹理特征单因素分析(x¯±s)
Tab. 4  T2WI image Fisher texture extraction method in the single factor analysis of each texture feature of primary lesions in different groups (x¯±s)

2.5 T2WI图像基于Fisher纹理提取法在不同组别原发灶纹理特征多因素分析

       将单因素分析中,差异有统计学意义的纹理参数导入Logistic回归模型进行多因素分析。回归结果显示:Perc 1%、S (5,0) InvDfMom为预测晚期直肠癌转化治疗不敏感的独立预测因子,以上述参数建立模型,预测晚期直肠癌原发灶转化治疗不敏感的AUC为0.812,敏感度为92.90%,特异度为60.80% (图2)。

表5  T2WI图像基于Fisher纹理提取法在不同组别原发灶纹理特征多因素分析(x¯±s)
Tab. 5  Multivariate analysis of texture features of primary lesions in different groups based on Fisher texture extractionon T2WI images (x¯±s)

3 讨论

       本研究提取66例晚期直肠癌患者基线磁共振T2WI图像纹理特征,与转化治疗后原发灶根治性切除术后病理进行对照分析,研究结果显示:Perc 1%、S (5,0) InvDfMom为晚期直肠癌原发灶转化治疗不敏感的独立预测因子,基于以上构建晚期直肠癌原发灶转化治疗不敏感预测模型曲线下面积(area under the curve,AUC)为0.812,敏感度为92.90%,特异度为60.80%,既往国内外研究中,MRI纹理分析多应用在直肠癌疗前分期、无创评价肿瘤生物学标志物表达状态及预后等方面[6, 7, 8]。MRI纹理分析预测疗效也多运用于局部晚期直肠癌[9, 10]。若针对晚期直肠癌患者,基线检查就能提供转化治疗相关的决策性信息,不仅能使更多患者获得根治性手术的机会,还能尽量减少不良治疗事件的发生,最大程度实现治疗获益。本研究正是着眼于上述研究现状及临床需要,将晚期直肠癌患者基线MRI图像进行纹理分析,其研究结果表明:基线磁共振T2WI图像纹理特征有望为晚期直肠癌患者提供更多决策性的参考信息。

3.1 晚期直肠癌转化治疗后原发灶T2WI图像纹理不同提取法比较

       长期以来,人工影像评估的准确性、稳定性及一致性难以把握,而运用计算机及合理的提取方法进行图像纹理分析,有助于避免主观因素影响,最大限度提取到图像纹理分布频率及空间位置关系特征,实现精准诊断。有学者[11]在常规MRI图像上对肝血管瘤和原发性肝癌运用不同纹理提取方法进行比较,其中基于T2WI图像运用不同纹理特征提取方法的NDA分类方法,均取得了零误判的结果。笔者运用T2WI加权序列图像基于Fisher纹理特征提取方法下的NDA分类方法误判率最低,主要因为该方法不仅能够最大限度地提取图像内像素或体素的灰度频率,还能最大限度反映各像素及体素的空间位置关系,即使原发灶转化治疗过程中形态学尚未发生变化,也能提供诸多肉眼无法识别的病灶内部纹理信息,间接反映其生物学特性,有助于疗前预测晚期直肠癌患者转化治疗原发灶疗效[12]

3.2 基于Fisher纹理提取法T2WI纹理分析参数在晚期直肠癌转化治疗原发灶不同组别比较

       晚期直肠癌转化治疗后原发灶疗效不同,敏感组内部微循环较为丰富,物质交换及转运较为便利,转化治疗药物也多富集于此,发挥抗肿瘤作用。同时因相对富氧,组织乏氧程度相对较轻,细胞药物反应更好。本次研究一级纹理特征中,Perc 1%在晚期直肠癌转化治疗后原发灶敏感组与不敏感组两组之间差异存在显著统计学意义,不敏感组(23.500±7.500)显著低于敏感组(31.157±9.105)。由此可见,Perc 1%能较为敏感地反映肿瘤局部血供情况。晚期直肠癌原发灶Perc 1%值较高的区域越多,提示病灶局部微循环较为丰富,治疗反应较为敏感。此外,也能反映病灶乏氧程度更轻,对转化治疗的反应更好,该数值为预测晚期直肠癌转化治疗不敏感的独立预测因子。

       另有学者[13]研究显示:IVIM-DWI检查的四个参数(ADC、D、D*及f)对直肠癌新辅助放化疗后病理完全缓解(pathological complete response,pCR)状态均无法体现预测价值,尽管上述参数能反映组织小分子物质细胞扩散及灌注状态,但反映局部血供情况却并非理想的检查模式。而有学者[14]则认为:较IVIM-DWI而言,局部晚期直肠癌患者新辅助放化疗反应组与无反应组基线ADC图一级纹理特征值无显著差别,但二级纹理参数Kurtosis值有助于疗前预测新辅助治疗后pCR状态。不同的样本量纳入、纳排标准、MRI检查、图像质控及评价标准等均会影响使其出现不同的研究结果,但不可否认的是MRI图像纹理分析在直肠癌诊疗环节具有深入研究的价值。

       在二级纹理特征参数中:对比度(Contrast)、差分方差(DifVarnc)和逆差矩(InvDfMom)是能够微观反映肿瘤异质性,Contrast或DifVarnc值越高,肿瘤异质性越强,而InvDfMom则相反。相关度(Correlat)和平均数(SumAverg)与肿瘤组织异质性无直接关系。本次研究敏感组的Contrast和DifVarnc值较低,而InvDfMom值则较高,S (5,0) InvDfMom为晚期直肠癌原发灶转化治疗不敏感的独立预测因子,由此说明,原发灶的异质性与转化治疗结果有着密不可分的关系,肿瘤异质性越大,对转化治疗的反应越差。有学者[15]也认为以局部晚期直肠癌经过新辅助放化疗后降期组基线ADC图的InvDfMom值明显高于未降期的患者。而局部晚期直肠癌患者新辅助治疗术后pTRG 3~4级的CT增强图像中,病灶纹理参数Entropy值显著低于TRG 0~2级的直肠癌[16]。由此可见,InvDfMom值越高提示肿瘤更均质,异质性更小,治疗反应更好。

3.3 基于Fisher纹理提取法T2WI纹理分析参数在晚期直肠癌转化治疗后原发灶不敏感的预测价值

       本研究基于Fisher纹理提取法T2WI纹理分析参数,预测晚期直肠癌转化治疗原发灶疗效预测效能较好(AUC 0.812),与其他研究者[17, 18]的研究结论近似,他们研究结果显示ADC图像纹理分析预测局部晚期直肠癌新辅助治疗耐药的AUC值分别为0.889和0.840。运用S (0,1) Contrast纹理特征预测模型的AUC分别为0.814及0.848,两者预测直肠癌新辅助化疗不敏感的效能间差异无统计学意义,但纹理特征模型的约登指数较大(0.591)。而Yang等[19]运用分段读出平面回波成像(readout-segmented echo-planar Imaging,rs-EPI)从高分辨率DWI计算放化疗后平均ADC值,能有效选择术前放化疗后实现pCR的局部晚期直肠癌(Locally advanced rectal cancer,LARC)患者,尽管它们不能显著提高诊断效能,但T2WI加权成像的一级纹理参数也能反映肿瘤异质性来疗前筛选出新辅助治疗pCR患者。而Shayesteh等[20]认为:运用机器学习模型构建局部晚期直肠癌患者新辅助治疗反应疗效预测模型准确性有待提高,选择合理算法对模型预测效能有影响。Crimì等[10]表示:将非病理完全应答者组和非良好反应者组相比,基于纹理分析的MRI T2WI图像难以预测局部晚期直肠癌新辅助放化疗病理完全反应。Zou等[21]纳入15例病理完全反应者(16.9%)和21例反应良好者(25.3%)患者。肿瘤的体积、平均K-转移、熵和相关性显著降低,能量值显著增加。Δ相关性(Δ相关性=后相关-预相关)是疗前筛选pCR病理完全反应组/良好反应组患者的一个有价值的参数(AUC 0.895,敏感度86.7%,特异度81.8%)。综合分析后笔者认为可能与下列因素有关:(1)本研究所选择的图像及算法能更准确地识别病灶,从而提取反映病灶微观生物学特征的纹理信息;(2)第一、二级纹理参数联合预测,能够更有效地反映肿瘤分子生物学信息及血流动力学信息,疗前准确地筛选晚期直肠癌患者转化治疗原发灶局部获益程度及相关的差别,T2WI图像纹理特征模型预测晚期直肠癌转化治疗原发灶疗效有着其独特优势及价值。

3.4 本研究不足与展望

       本研究还有以下方面值得继续探讨:(1)纳入的样本量较小,可能存在选择性偏倚,日后可继续扩大样本例数,研究方向拓展至预测不同病理类型、不同转化治疗方案的晚期直肠癌转化治疗疗效,探讨更多因素对治疗结果的影响;(2)本研究只提取了T2WI图像纹理参数进行研究,日后可探讨其他序列或序列联合预测晚期直肠癌转化治疗疗效的应用价值;(3)本研究为单中心研究,有待于多中心、多学科联合研究,使研究结论更有临床应用及推广价值。

       综上所述,基于T2WI参数图的纹理特征,在预测晚期直肠癌原发灶新辅助放化疗的病理反应方面有一定的应用价值,能为患者个体化治疗决策的制订提供有价值的参考依据。

[1]
Park SH, Cho SH, Choi SH, et al. MRI Assessment of Complete Response to Preoperative Chemoradiation Therapy for Rectal Cancer: 2020 Guide for Practice from the Korean Society of Abdominal Radiology[J]. Korean J Radiol, 2020, 21(7): 812-828. DOI: 10.3348/kjr.2020.0483.
[2]
刘启志, 张杭, 郝立强, 等. 中低位直肠癌新辅助放化疗后病理完全缓解的影响因素分析[J]. 中华胃肠外科杂志, 2020, 23(12): 1159-1163. DOI: 10.3760/cma.j.cn.441530-20200106-00009
Liu QZ, Zhang H, Hao LQ, et al. Analysis of influencing factors of pathological complete remission after neoadjuvant radiotherapy and chemotherapy for middle and low rectal cancer[J]. Chin J Gastrointestinal Surg, 2020, 23(12): 1159-1163. DOI: 10.3760/cma.j.cn.441530-20200106-00009.
[3]
Bulens P, Couwenberg A, Intven M, et al. Predicting the tumor response to chemoradiotherapy for rectal cancer: Model development and external validation using MRI radiomics[J]. Radiother Oncol, 2020, 142: 246-252. DOI: 10.1016/j.radonc.2019.07.033.
[4]
Oh JE, Kim MJ, Lee J, et al. Magnetic Resonance-Based Texture Analysis Differentiating KRAS Mutation Status in Rectal Cancer[J]. Cancer Res Treat, 2020, 52(1): 51-59. DOI: 10.4143/crt.2019.050.
[5]
Antunes J, Viswanath S, Brady JT, et al. Coregistration of Preoperative MRI with Ex Vivo Mesorectal Pathology Specimens to Spatially Map Post-treatment Changes in Rectal Cancer Onto In Vivo Imaging: Preliminary Findings[J]. Acad Radiol, 2018, 25(7): 833-841. DOI: 10.1016/j.acra.2017.12.006.
[6]
Yin JD, Song LR, Lu HC, et al. Prediction of different stages of rectal cancer: Texture analysis based on diffusion-weighted images and apparent diffusion coefficient maps[J]. World J Gastroenterol, 2020, 26(17): 2082-2096. DOI: 10.3748/wjg.v26.i17.2082.
[7]
刘宇卉, 陈安良, 武敬君, 等. 动态对比增强磁共振成像纹理多参数联合分析预测P53表达状态对直肠癌鉴别诊断的价值[J]. 磁共振成像, 2021, 12(8): 33-37,74. DOI: 10.12015/issn.1674-8034.2021.08.007.
Liu YH, Chen AL, Wu JJ, et al. The value of dynamic contrast-enhanced magnetic resonance imaging texture multi-parameter analysis to predict the expression of P53 in the differential diagnosis of rectal cancer[J]. Chin J Magn Reson Imaging, 2021, 12(8): 33-37, 74. DOI: 10.12015/issn.1674-8034.2021.08.007.
[8]
Atre ID, Eurboonyanun K, Noda Y, et al. Utility of texture analysis on T2-weighted MR for differentiating tumor deposits from mesorectal nodes in rectal cancer patients, in a retrospective cohort[J]. Abdom Radiol (NY), 2021, 46(2): 459-468. DOI: 10.1007/s00261-020-02653-w.
[9]
Park H, Kim KA, Jung JH, et al. MRI features and texture analysis for the early prediction of therapeutic response to neoadjuvant chemoradiotherapy and tumor recurrence of locally advanced rectal cancer[J]. Eur Radiol, 2020, 30(8): 4201-4211. DOI: 10.1007/s00330-020-06835-4.
[10]
Crimì F, Capelli G, Spolverato G, et al. MRI T2-weighted sequences-based texture analysis (TA) as a predictor of response to neoadjuvant chemo-radiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC)[J]. Radiol Med, 2020, 125(12): 1216-1224. DOI: 10.1007/s11547-020-01215-w.
[11]
Lu HC, Wang F, Yin JD. Texture Analysis Based on Sagittal Fat-Suppression and Transverse T2-Weighted Magnetic Resonance Imaging for Determining Local Invasion of Rectal Cancer[J]. Front Oncol, 2020, 10: 1476. DOI: 10.3389/fonc.2020.01476.
[12]
Song LR, Yin JD. Application of Texture Analysis Based on Sagittal Fat-Suppression and Oblique Axial T2-Weighted Magnetic Resonance Imaging to Identify Lymph Node Invasion Status of Rectal Cancer[J]. Front Oncol, 2020, 10: 1364. DOI: 10.3389/fonc.2020.01364.
[13]
Yin JD, Song LR, Lu HC, et al. Prediction of different stages of rectal cancer: Texture analysis based on diffusion-weighted images and apparent diffusion coefficient maps[J]. World J Gastroenterol, 2020, 26(17): 2082-2096. DOI: 10.3748/wjg.v26.i17.2082.
[14]
Nardone V, Reginelli A, Scala F, et al. Magnetic-Resonance-Imaging Texture Analysis Predicts Early Progression in Rectal Cancer Patients Undergoing Neoadjuvant Chemoradiation[J]. Gastroenterol Res Pract, 2019, 2019: 8505798. DOI: 10.1155/2019/8505798.
[15]
Horvat N, Veeraraghavan H, Pelossof RA, et al. Radiogenomics of rectal adenocarcinoma in the era of precision medicine: A pilot study of associations between qualitative and quantitative MRI imaging features and genetic mutations[J]. Eur J Radiol, 2019, 113: 174-181. DOI: 10.1016/j.ejrad.2019.02.022.
[16]
Petresc B, Lebovici A, Caraiani C, et al. Pre-Treatment T2-WI Based Radiomics Features for Prediction of Locally Advanced Rectal Cancer Non-Response to Neoadjuvant Chemoradiotherapy: A Preliminary Study[J]. Cancers (Basel), 2020, 12(7): 1894. DOI: 10.3390/cancers12071894.
[17]
Meng YK, Zhang CD, Zou SM, et al. MRI texture analysis in predicting treatment response to neoadjuvant chemoradiotherapy in rectal cancer[J]. Oncotarget, 2017, 9(15): 11999-12008. DOI: 10.18632/oncotarget.23813.
[18]
Shayesteh SP, Alikhassi A, Farhan F, et al. Prediction of Response to Neoadjuvant Chemoradiotherapy by MRI-Based Machine Learning Texture Analysis in Rectal Cancer Patients[J]. J Gastrointest Cancer, 2020, 51(2): 601-609. DOI: 10.1007/s12029-019-00291-0.
[19]
Yang LQ, Qiu M, Xia CC, et al. Value of High-Resolution DWI in Combination With Texture Analysis for the Evaluation of Tumor Response After Preoperative Chemoradiotherapy for Locally Advanced Rectal Cancer[J]. AJR Am J Roentgenol, 2019,1-8. DOI: 10.2214/AJR.18.20689.
[20]
Shayesteh SP, Alikhassi A, Farhan F, et al. Prediction of Response to Neoadjuvant Chemoradiotherapy by MRI-Based Machine Learning Texture Analysis in Rectal Cancer Patients[J]. J Gastrointest Cancer, 2020, 51(2): 601-609. DOI: 10.1007/s12029-019-00291-0.
[21]
Zou HH, Yu J, Wei Y, et al. Response to neoadjuvant chemoradiotherapy for locally advanced rectum cancer: Texture analysis of dynamic contrast-enhanced MRI[J]. J Magn Reson Imaging, 2019, 49(3): 885-893. DOI: 10.1002/jmri.26254.

上一篇 钆塞酸二钠增强MRI中基于肝叶的信号强度参数与白蛋白-胆红素分级关系的研究
下一篇 磁共振扩散加权成像定量评估克罗恩病活动性的临床应用价值
  
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