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临床研究
Kaiser评分与ADC值对乳腺BI-RADS 4类病变的诊断效能评价
任晓梦 刘晓春 代天姿 张晖 郑国娜 韩丽娜

Cite this article as: Ren XM, Liu XC, Dai TZ, et al. Comparative assessment of MRI BI-RADS 4 breast lesions with Kaiser score and apparent diffusion coefficient value[J]. Chin J Magn Reson Imaging, 2022, 13(9): 25-29, 34.本文引用格式:任晓梦, 刘晓春, 代天姿, 等. Kaiser评分与ADC值对乳腺BI-RADS 4类病变的诊断效能评价[J]. 磁共振成像, 2022, 13(9): 25-29, 34. DOI:10.12015/issn.1674-8034.2022.09.005.


[摘要] 目的 评价Kaiser评分与表观扩散系数(apparent diffusion coefficient, ADC)值对乳腺BI-RADS 4类病变的诊断效能。材料与方法 回顾性分析2020年6月至2022年2月于河北省人民医院行乳腺MRI检查归类为BI-RADS 4类且病理结果明确的患者病例。对病例的每个病灶进行Kaiser评分、ADC值测量,Kaiser评分由两名放射科医生商议决定,ADC值的测量由经验丰富的医师指定感兴趣区域(region of interest, ROI)并测量,使用logistic回归联合Kaiser评分与ADC值后获得一个新的预测指标Kaiser+。应用受试者工作特征(receiver operating characteristic, ROC)曲线评价Kaiser评分和ADC值的诊断效能,应用Delong检验对曲线下面积(area under the curve, AUC)进行比较。结果 128例患者共165个病灶,Kaiser评分的总体诊断效能(AUC=0.882)显著高于ADC(AUC=0.582,P<0.05),Kaiser+与Kaiser评分之间的AUC无显著性差异(P=0.885)。与ADC相比,Kaiser评分诊断效能不受背景实质增强影响。结论 对于乳腺MRI诊断的BI-RADS 4类病变,Kaiser评分具有较高的诊断效能且优于ADC,可以减少不必要的活检;Kaiser评分与ADC值联合相对于单独Kaiser评分的诊断效能没有显著优势。
[Abstract] Objective To investigate the diagnostic performance of the Kaiser score and apparent diffusion coefficient (ADC) to Breast Imaging Reporting and Data System (BI-RADS) Category 4 lesions at dynamic contrast-enhanced MRI (DCE-MRI).Materials and Methods The cases who underwent breast MRI and were classified as BI-RADS category 4 with clear pathological findings in Hebei General Hospital from June 2020 to February 2022 were retrospectively analyzed. The measurement of ADC value was designated by experienced physicians to designate a region of interest (ROI) and measured. Using logistic regression combined Kaiser score and ADC value to obtain the predictor Kaiser+. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of Kaiser score, Kaiser+ and ADC. The area under the curve (AUC) values were calculated and compared by using the Delong test.Results The study involved 128 women with 165 lesions. Overall diagnostic performance for Kaiser score (AUC=0.882) was significantly higher than for ADC (AUC=0.582; P<0.05). There were no significant differences in AUCs between Kaiser score and Kaiser+ (P=0.885). Compared with ADC value, the Kaiser score is independent of background parenchymal enhancement when making a lesion diagnosis.Conclusions For BI-RADS 4 breast lesions, the Kaiser score is superior to ADC mapping and may help to avoid unnecessary biopsies. However, the combination of both indicators did not significantly contribute to breast cancer diagnosis.
[关键词] 乳腺癌;Kaiser评分;磁共振成像;表观扩散系数
[Keywords] breast cancer;Kaiser score;magnetic resonance imaging;apparent diffusion coefficient

任晓梦 1, 2   刘晓春 2, 3   代天姿 1, 2   张晖 2*   郑国娜 4   韩丽娜 5  

1 河北医科大学研究生学院,石家庄 050017

2 河北省人民医院医学影像科,石家庄 050051

3 河北北方学院研究生学院,张家口 075000

4 河北省人民医院病理科,石家庄 050051

5 河北省人民医院神经内科,石家庄 050051

*张晖,E-mail:wszzzhui@163.com

作者利益冲突声明:全体作者均声明无利益冲突。


基金项目: 2019年度河北省人才培养资助项目 A201901017
收稿日期:2022-05-31
接受日期:2022-09-14
中图分类号:R445.2  R737.9 
文献标识码:A
DOI: 10.12015/issn.1674-8034.2022.09.005
本文引用格式:任晓梦, 刘晓春, 代天姿, 等. Kaiser评分与ADC值对乳腺BI-RADS 4类病变的诊断效能评价[J]. 磁共振成像, 2022, 13(9): 25-29, 34. DOI:10.12015/issn.1674-8034.2022.09.005

       乳腺癌是女性最常见的恶性肿瘤,MRI是鉴别乳腺内良恶性病变的重要影像学检查方法,敏感性很高[1]。扩散加权成像(diffusion-weighted imaging, DWI)及其定量参数表观扩散系数(apparent diffusion coefficient, ADC)值可应用于乳腺病变良恶性的评估,已有研究表明恶性病变的ADC值显著低于良性病变[2],可以避免不必要的临床干预[3]。目前DWI与动态对比增强(dynamic contrast enhanced, DCE)两种MRI检查序列联合应用可对乳腺癌进行影像学诊断[4],然而对于恶性概率跨度极大的乳腺影像报告和数据系统(Breast Imaging-Reporting and Data System, BI-RADS)4类病变的诊断价值有限,BI-RADS 4类病变的恶性概率为2%~95%[1,5],这预示部分良性病灶将被误诊为恶性病灶而接受不必要的手术,增加了患者的心理和经济负担。作为一种临床决策依据,纳入五种影像特征的Kaiser评分在评估乳腺病变方面具有极佳的敏感性和特异性[6, 7],分值从1到11,相关文献认为评分≥4建议进行活检[8, 9]。本研究的目的是比较Kaiser评分与ADC值对BI-RADS 4类病变的诊断效能,并获取Kaiser评分诊断良恶性的截断值,以减少不必要的有创活检。

1 材料与方法

1.1 一般资料

       回顾性分析2020年6月至2022年2月于河北省人民医院行术前乳腺3.0 T MRI平扫及动态对比增强检查的女性患者病例共128例,年龄9~83(47±13)岁。纳入标准:诊断报告为BI-RADS 4类的强化病灶;影像资料及病理结果完善、清晰。排除标准:MRI检查前行乳腺穿刺、手术或放化疗。本研究经河北省人民医院伦理委员会批准,免除受试者知情同意,批准文号:2022082。

1.2 设备及参数

       所有病例MRI扫描采用美国GE 3.0 T MRI仪,使用16通道乳腺表面相控阵线圈,取俯卧位,扫描范围包括双侧乳腺及腋窝。轴位快速自旋回波T2WI脂肪抑制成像序列扫描参数:TR 7061 ms,TE 104.3 ms,层厚5 mm,层间距5 mm,FOV 350×350;轴位T1WI序列扫描参数:TR 420 ms,TE 7.6 ms,层厚5 mm,层间距5 mm,FOV 350×350;DWI序列扫描参数:TR 5113 ms,TE 66.6 ms,层厚5 mm,层间距5 mm,FOV 350 mm×210 mm,b值取1000 s/mm2;DCE-MRI扫描参数:TR 5.7 ms,TE 1.8 ms,层厚1.6 mm,层间距0,FOV 350 mm×350 mm,共采集7期图像,对比剂为钆喷酸葡胺,剂量0.2 mmol/kg,流速2 mL/s,注射后用20 mL生理盐水以3 mL/s的速度自动冲洗注射器。

1.3 图像处理与分析

       由两位高年资乳腺诊断医师(诊断医师1为工作经验30年的主任医师;诊断医师2为工作经验15年的副主任医师)根据Kaiser评分系统解读所有检查结果,出现分歧时协商达成一致。该评分系统包括5个独立的诊断标准,即毛刺征、时间-信号强度曲线(time-signal intensity curve, TIC)类型、病变边缘、内部强化模式及瘤周水肿。两位诊断医师均不知道病理结果和BI-RADS 分级,计算并记录每个病变的最终Kaiser评分。Kaiser评分系统的流程图如图1所示。

       所有MRI原始数据传到AW 4.2后处理工作站,应用Functool软件进行后处理,感兴趣区(region of interest, ROI)包括病灶的实性区域,避开坏死、囊变或出血的区域,避开纤维腺体及血管,测量每个ROI的ADC值及TIC,重复测量两次,以平均值作为最终数据。测量ADC值时,于DWI序列上病变明显高信号处勾画ROI。动态对比增强扫描采用乳腺容积成像即VIBRANT技术,第一期为蒙片,在第二或三期病变早期明显强化时勾画ROI测量TIC。背景实质增强(background parenchymal enhancement, BPE)即乳腺MRI图像上正常实质的强化,由两位高年资医生(诊断医师1为工作经验30年的主任医师;诊断医师2为工作经验15年的副主任医师)依据DCE-MRI图像按照BI-RADS系统分为4类:少量、轻度、中度、重度,分别代表<25%、25%~50%、51%~75%、>75%的腺体组织强化。

图1  Kaiser 评分流程。
Fig. 1  The flowchart of Kaiser score.

1.4 统计学分析

       数据分析采用SPSS 25.0及MedCalc 20.0(Medcalc Software)软件处理,服从正态分布的计量资料以(x¯±s)表示,组间比较采用独立样本t检验,应用配对卡方检验比较敏感度、特异度,采用Kappa值评价两种方法是否具有一致性。使用logistic回归联合Kaiser评分与ADC值作为一个新的预测指标Kaiser+。诊断效能采用受试者工作特征(receiver operating characteristic, ROC)曲线评估,应用准确度、敏感度、特异度、阳性预测值和阴性预测值评估诊断价值,应用DeLong检验比较曲线下面积(area under the curve, AUC),P<0.05为差异有统计学意义。

2 结果

2.1 临床资料

       128例患者病例共165个病灶,其中肿块样病灶111个,非肿块样病灶54个;恶性67个,良性98个。恶性病灶中,浸润性导管癌27个,导管原位癌17个,导管内乳头状癌5个,导管原位癌伴浸润性导管癌8个,浸润性小叶癌4个,包裹性乳头状癌1个,浸润性筛状癌1个,浸润性癌伴黏液分化1个,黏液癌2个,浸润性微乳头状癌1个;良性病灶中,纤维腺瘤37个,导管内乳头状瘤25个,腺病17个,肉芽肿性乳腺炎7个,炎症4个,硬化性腺病4个,良性叶状肿瘤3个,良性肌上皮瘤1个。典型病例见图2

图2  女,39 岁,浸润性导管癌Ⅱ级。2A:轴位T1WI 图,右侧乳头后外下方肿块呈低信号;2B:轴位T2WI图,病变呈高低混杂信号;2C:DWI图,病变呈周围高中心低信号;2D:DCE-MRI 第2 期轴位图,病变呈明显不均匀强化;2E:TIC图,各ROI处TIC均为流出型;2F:病理图片(HE ×100)肿瘤细胞中等大小、弥漫分布,可见核分裂象,部分导管结构消失。Kaiser 评分为8 分。DWI:扩散加权成像;DCE:动态对比增强;TIC:时间-信号强度曲线;ROI:感兴趣区。
Fig. 2  A 39-year-old woman with invasive ductal carcinoma grade Ⅱ . 2A-2E show the MRI axial images of the lesion in the posterior and inferior of the right nipple. The lesion presents hypointense on T1WI (2A), hyperintense and hypointense mixed on T2WI (2B) and diffusion-weighted imaging (DWI) (2C). dynamic contrast enhanced MRI (DCE) showed significant inhomogeneous enhancement (2D) and the time-signal intensity curve (TIC) is outflow type (2E). Pathological section shows the tumors cells are medium-sized, diffusely distributed, with nuclear schizophrenia and some of ductal structures disappearing (HE ×100) (2F). The Kaiser score is 8.

2.2 Kaiser评分与ADC值诊断效能分析

       Kaiser评分与ADC值诊断结果见表1。恶性病变的Kaiser评分高于良性病变,ADC值低于良性病变,差异具有统计学意义(P<0.05),结果见表2。对于所有病变、肿块型强化病变,Kaiser评分的AUC均高于ADC值的AUC,差异具有统计学意义(P<0.001),但在非肿块病变中,差异无统计学意义(P=0.152)。Kaiser评分+的AUC在所有病变、肿块及非肿块型强化病变中均高于ADC值的AUC,差异具有统计学意义(P<0.05)。在所有病变、肿块及非肿块型强化病变中,Kaiser评分与Kaiser评分+AUC比较,差异均不具有统计学意义,结果见表3图3。依据BI-RADS系统将BPE分为4类,比较Kaiser、Kaiser+与ADC值的AUC,结果见图4表3。在BPE 1~4类乳腺中,Kaiser评分的AUC均高于ADC值的AUC,这表明Kaiser评分在诊断BI-RADS 4类病变时不受BPE影响。

图3  Kaiser 评分、Kaiser+及ADC 值ROC曲线对比。3A~3C分别为所有类型病变、肿块型病变、非肿块型病变Kaiser 评分、Kaiser+ 与ADC 值ROC 曲线对比。ADC:表观扩散系数;ROC曲线:受试者工作特征曲线。
Fig. 3  Comparison of ROC curves of Kaiser score, Kaiser+ and ADC value。 3A-3C: Comparison of ROC curve of Kaiser+ , Kaiser score and ADC value for all lesions, mass lesions and non-mass lesions. ADC: apparent diffusion coefficient; ROC: receiver operating characteristic.
图4  BPE 1~4 类病变Kaiser 评分、Kaiser+及ADC值ROC曲线对比。4A~4D分别为BPE 1~4 类病变Kaiser 评分、Kaiser+及ADC值ROC曲线图。ADC:表观扩散系数;ROC曲线:受试者工作特征曲线;BPE:背景实质增强。
Fig. 4  Comparison of ROC curves of Kaiser score, Kaiser+ and ADC value for BPE 1-4 lesions. 4A-4D: ROC curves of Kaiser score, Kaiser+ and ADC value for BPE 1-4 lesions. ADC: apparent diffusion coefficient; ROC: receiver operating characteristic; BPE: background parenchymal enhancement.
表1  Kaiser评分、ADC值诊断结果与病理结果对比 单位:例
Tab. 1  Comparison of Kaiser score, ADC value results and pathological findings
表2  良恶性病变不同参数对比
Tab. 2  Comparison of different parameters between benign and malignant lesions
表3  Kaiser评分、Kaiser+及ADC值ROC曲线比较
Tab. 3  Comparison of ROC curves of Kaiser score, Kaiser+, and ADC value

2.3 Kaiser评分与ADC值准确度、敏感度、特异度比较

       对于所有病变,Kaiser评分与ADC值诊断一致性较差(Kappa=0.192,95% CI:0.039~0.345,P=0.013),Kaiser评分的准确度(124/165,75%)高于ADC值的准确度(90/165,55%),但差异无统计学意义(P>0.05)。Kaiser评分的敏感度(65/67,97.0%)、特异度(59/98,60.2%)均高于ADC值的敏感度(53/67,79.1%)、特异度(37/98,37.8%),差异具有统计学意义(P<0.001)。根据ROC曲线确定约登指数,确定Kaiser评分评估BI-RADS 4类病变良恶性的截断值为4,截断值对应的敏感度为97.0%(65/67),特异度为60.2%(59/98),阳性预测值为62.5%(65/104),阴性预测值为96.7%(59/61)。对于所有病变应用Kaiser评分可以减少不必要的活检。

3 讨论

       本研究采用随机对照研究中的配对设计方法评估了Kaiser评分、ADC值和两者联合在乳腺BI-RADS 4类病变诊断中的效能,结果显示在乳腺BI-RADS 4类病变中,Kiser评分诊断效能高于ADC值,可以减少临床不必要的活检,联合应用Kaiser评分和ADC值并不能显著提高诊断效能。本研究为国内首次在BI-RADS 4类病变中同时应用Kaiser评分和ADC值,并对两者的诊断效能做出比较。

3.1 DWI在乳腺病变诊断中的应用

       DWI是一种功能MRI技术,被广泛应用于评估乳腺病变,可以提高MRI的诊断准确性[10, 11, 12],在临床上可以通过其定量参数ADC值评估组织中水分子的扩散程度[4,10]。在恶性病变中,由于肿瘤细胞增殖、细胞外空间受压,扩散受限,导致DWI信号增高,相应的ADC值降低。作为一种定量诊断工具,ADC值在初步评估乳腺病变良恶性方面表现良好,但ADC值容易受到病变成分及扫描参数的影响,并且对于那些表现为T2WI或DWI等信号、非肿块强化或存在广泛坏死的病变,测量其数值困难,导致其诊断效能下降,易出现假阴性结果;另外,某些病变如黏液癌,表现为ADC值升高,与良性病变相似,单独依靠ADC值容易误诊[13]

3.2 Kaiser评分在乳腺病变诊断中的应用

       Kaiser评分是Baltzer等[14]提出的一种病变分类算法,基于常规T2WI压脂序列及DCE-MRI序列,应用了五个独立的形态学及动态增强相关的特征,范围从1到11,提供了病变的恶性可能性,分数≤4时可以在很大程度上排除恶性,评分高则需要活检,评分结果不受设备参数及增强扫描期相影响[15],易于推广。相对于BI-RADS系统仅做到使诊断报告标准化,Kaiser评分可以作为诊断依据辅助评估病变良恶性。Kaiser评分的诊断标准符合BI-RADS[16],观察者间一致性良好,即使不同观察者的个人评分不同,但最终是否需要活检的最终结果是一致的,且重复性高[17, 18, 19]。Kaiser评分敏感性高,在高危女性中,采用4作为病变的阈值可以避免超过45% BI-RADS 4类的病灶进行不必要的活检[9],并且可以对X线表现为可疑恶性的钙化进行风险分层,降低58.3%~65.3%不必要的定向活检[8]。Rong等[20]得出结论,对比增强乳腺X线摄影技术与Kaiser评分结合有助于诊断BI-RADS 4A类病变,可以避免75.8%~82.1%不必要的良性乳腺病变活检。王珊等[21]的研究表明,Kaiser评分对BI-RADS 4类非肿块样强化病变具有较高的敏感性和准确性,与本研究结果一致。对于BI-RADS 3-5类病变中性质不明确的病灶,Kaiser评分也可以辅助诊断,提高诊断准确性,避免45.2%~60.8%的活检[22]

3.3 Kaiser评分、ADC值及两者联合在BI-RADS 4类病变诊断效能分析

       本研究结果显示,Kaiser评分对于BI-RADS 4类病变的诊断效能高于ADC值,而且Kaiser评分不受BPE影响,在每个亚组均表现出良好的鉴别能力。研究证实[23],中重度BPE的女性患者DCE-MRI的敏感度低于轻微及轻度BPE的女性,高BPE可能会导致假阳性或假阴性[24],且高BPE可能是乳腺癌的独立危险因素[25]。Kaiser评分与ADC值两者的结合较单独Kaiser评分诊断效能并未见显著提高。当Kaiser评分<3时,对BI-RADS 4类病变的诊断未出现假阴性结果;当Kaiser评分>9时,对BI-RADS 4类病变的诊断未出现假阳性结果。当评分取截断值4时,敏感度、特异度、阳性预测值、阴性预测值分别为97.0%、60.2%、62.5%、96.7%。对于肿块、非肿块强化病变,Kaiser评分诊断效能均高于ADC值,但诊断非强化病变的特异性较其他研究低[26],这可能是由于本研究中非肿块病变较少,且多数为良性。Kaiser评分诊断的2例假阴性均为非肿块强化,均为导管原位癌,在评估Kaiser评分时TIC为流出型,被评价为4分,这是因为导管原位癌病灶区域基底膜周围有多少不等的新生血管,所以TIC形式多样,其中平台型最常见,且在导管原位癌诊断中TIC类型差异无统计学意义[27]

3.4 不足与展望

       本研究有一定的局限性:第一,由于乳腺病变早期筛查的普及,Kaiser评分为10、11分的病变较少,且由于选择偏倚导致本研究BI-RADS 4类病变恶性率(40.6%)高于文献报道[28],阳性预测值会偏高,后续研究中将严格掌握研究对象的纳入与排除标准,增加样本量。第二,在测量ADC值时没有评估病灶的大小,病灶较小会导致ADC值测量不准确,最近的研究证实Kaiser评分可以用来评估小于5 mm的病灶[29],这可能影响结果,后续研究可以将病灶按照大小进行分层分析。第三,在二维图像上勾画ROI忽略了病变异质性的影响,这可以通过使用ITK-SNAP软件进一步勾画三维容积ROI尽量避免。

       综上所述,Kaiser评分对于BI-RADS 4类病变的诊断效能高于ADC值,有望降低不必要的穿刺活检率。与单纯的Kaiser评分相比,联合应用Kaiser评分和ADC值不能显著提高BI-RADS 4类病变的诊断效能。

[1]
Leithner D, Wengert G, Helbich T, et al. MRI in the assessment of BI-RADS® 4 lesions[J]. Top Magn Reson Imaging, 2017, 26(5): 191-199. DOI: 10.1097/RMR.0000000000000138.
[2]
Bickel H, Pinker K, Polanec S, et al. Diffusion-weighted imaging of breast lesions: region-of-interest placement and different ADC parameters influence apparent diffusion coefficient values[J]. Eur Radiol, 2017, 27(5): 1883-1892. DOI: 10.1007/s00330-016-4564-3.
[3]
Clauser P, Krug B, Bickel H, et al. Diffusion-weighted imaging allows for downgrading MR BI-RADS 4 lesions in contrast-enhanced MRI of the breast to avoid unnecessary biopsy[J]. Clin Cancer Res, 2021, 27(7): 1941-1948. DOI: 10.1158/1078-0432.CCR-20-3037.
[4]
Zhang M, Horvat JV, Bernard-Davila B, et al. Multiparametric MRI model with dynamic contrast-enhanced and diffusion-weighted imaging enables breast cancer diagnosis with high accuracy[J]. J Magn Reson Imaging, 2019, 49(3): 864-874. DOI: 10.1002/jmri.26285.
[5]
Strigel RM, Burnside ES, Elezaby M, et al. Utility of BI-RADS assessment category 4 subdivisions for screening breast MRI[J]. AJR Am J Roentgenol, 2017, 208(6): 1392-1399. DOI: 10.2214/AJR.16.16730.
[6]
安永玉, 刘畅, 张宏霞, 等. Kaiser评分对MRI乳腺影像报告与数据系统4类病灶的诊断价值[J]. 浙江医学, 2021, 43(5): 511-515. DOI: 10.12056/j.issn.1006-2785.2021.43.5.2020-3139.
An YY, Liu C, Zhang HX, et al. Diagnostic value of Kaiser score for BI-RADS 4 lesions on MRI[J]. Zhejiang Med J, 2021, 43(5): 511-515. DOI: 10.12056/j.issn.1006-2785.2021.43.5.2020-3139.
[7]
Grippo C, Jagmohan P, Helbich TH, et al. Correct determination of the enhancement curve is critical to ensure accurate diagnosis using the Kaiser score as a clinical decision rule for breast MRI[J/OL]. Eur J Radiol, 2021 [2022-05-30]. https://doi.org/10.1016/j.ejrad.2021.109630. DOI: 10.1016/j.ejrad.2021.109630.
[8]
Wengert GJ, Pipan F, Almohanna J, et al. Impact of the Kaiser score on clinical decision-making in BI-RADS 4 mammographic calcifications examined with breast MRI[J]. Eur Radiol, 2020, 30(3): 1451-1459. DOI: 10.1007/s00330-019-06444-w.
[9]
Milos RI, Pipan F, Kalovidouri A, et al. The Kaiser score reliably excludes malignancy in benign contrast-enhancing lesions classified as BI-RADS 4 on breast MRI high-risk screening exams[J]. Eur Radiol, 2020, 30(11): 6052-6061. DOI: 10.1007/s00330-020-06945-z.
[10]
Iima M, Honda M, Sigmund EE, et al. Diffusion MRI of the breast: current status and future directions[J]. J Magn Reson Imaging, 2020, 52(1): 70-90. DOI: 10.1002/jmri.26908.
[11]
Dijkstra H, Dorrius MD, Wielema M, et al. Quantitative DWI implemented after DCE-MRI yields increased specificity for BI-RADS 3 and 4 breast lesions[J]. J Magn Reson Imaging, 2016, 44(6): 1642-1649. DOI: 10.1002/jmri.25331.
[12]
Istomin A, Masarwah A, Okuma H, et al. A multiparametric classification system for lesions detected by breast magnetic resonance imaging[J/OL]. Eur J Radiol, 2020 [2022-05-30]. https://doi.org/10.1016/j.ejrad.2020.109322. DOI: 10.1016/j.ejrad.2020.109322.
[13]
Bitencourt AG, Graziano L, Osório CA, et al. MRI features of mucinous cancer of the breast: correlation with pathologic findings and other imaging methods[J]. AJR Am J Roentgenol, 2016, 206(2): 238-246. DOI: 10.2214/AJR.15.14851.
[14]
Baltzer PAT, Dietzel M, Kaiser WA. A simple and robust classification tree for differentiation between benign and malignant lesions in MR-mammography[J]. Eur Radiol, 2013, 23(8): 2051-2060. DOI: 10.1007/s00330-013-2804-3.
[15]
Woitek R, Spick C, Schernthaner M, et al. A simple classification system (the Tree flowchart) for breast MRI can reduce the number of unnecessary biopsies in MRI-only lesions[J]. Eur Radiol, 2017, 27(9): 3799-3809. DOI: 10.1007/s00330-017-4755-6.
[16]
Baltzer P, Krug KB, Dietzel M. Evidence-based and structured diagnosis in breast MRI using the Kaiser score[J/OL]. Rofo, 2022 [2022-05-30]. https://www.thieme-connect.com. DOI: 10.1055/a-1829-5985.
[17]
Dietzel M, Baltzer PAT. How to use the Kaiser score as a clinical decision rule for diagnosis in multiparametric breast MRI: a pictorial essay[J]. Insights Imaging, 2018, 9(3): 325-335. DOI: 10.1007/s13244-018-0611-8.
[18]
Marino MA, Clauser P, Woitek R, et al. A simple scoring system for breast MRI interpretation: does it compensate for reader experience?[J]. Eur Radiol, 2016, 26(8): 2529-2537. DOI: 10.1007/s00330-015-4075-7.
[19]
Istomin A, Masarwah A, Vanninen R, et al. Diagnostic performance of the Kaiser score for characterizing lesions on breast MRI with comparison to a multiparametric classification system[J/OL]. Eur J Radiol, 2021 [2022-05-30]. https://doi.org/10.1016/j.ejrad.2021.109659. DOI: 10.1016/j.ejrad.2021.109659.
[20]
Rong X, Kang Y, Xue J, et al. Value of contrast-enhanced mammography combined with the Kaiser score for clinical decision-making regarding tomosynthesis BI-RADS 4A lesions[J/OL]. Eur Radiol, 2022 [2022-05-30]. https://doi.org/10.1007/s00330-022-08810-7. DOI: 10.1007/s00330-022-08810-7.
[21]
王珊, 李建玉, 郑慧, 等. Kaiser评分对乳腺BI-RADS 4类非肿块样强化病灶的诊断价值分析[J]. 临床放射学杂志, 2021, 40(12): 2282-2286. DOI: 10.13437/j.cnki.jcr.2021.12.010.
Wang S, Li JY, Zheng H, et al. Analysis on Kaiser sore in diagnostic value of BI-RADS 4 types of breast non-masses enhancement[J]. J Clin Radiol, 2021, 40(12): 2282-2286. DOI: 10.13437/j.cnki.jcr.2021.12.010.
[22]
Jajodia A, Sindhwani G, Pasricha S, et al. Application of the Kaiser score to increase diagnostic accuracy in equivocal lesions on diagnostic mammograms referred for MR mammography[J/OL]. Eur J Radiol, 2021[2022-05-30] https://doi.org.10.1016/j.ejrad.2020.109413. DOI: 10.1016/j.ejrad.2020.109413.
[23]
Telegrafo M, Rella L, Stabile Ianora AA, et al. Effect of background parenchymal enhancement on breast cancer detection with magnetic resonance imaging[J]. Diagn Interv Imaging, 2016, 97(3): 315-320. DOI: 10.1016/j.diii.2015.12.006.
[24]
Jung Y, Jeong S, Kim JY, et al. Correlations of female hormone levels with background parenchymal enhancement and apparent diffusion coefficient values in premenopausal breast cancer patients: effects on cancer visibility[J/OL]. Eur J Radiol, 2020 [2022-05-30] https://doi.org/10.1016/j.ejrad.2020.108818. DOI: 10.1016/j.ejrad.2020.108818.
[25]
Zhang H, Guo LL, Tao WJ, et al. Possible breast cancer risk related to background parenchymal enhancement at breast MRI: a meta-analysis study[J]. Nutr Cancer, 2021, 73(8): 1371-1377. DOI: 10.1080/01635581.2020.1795211.
[26]
Zhang B, Feng LL, Wang L, et al. Kaiser score for diagnosis of breast lesions presenting as non-mass enhancement on MRI[J]. Nan Fang Yi Ke Da Xue Xue Bao, 2020, 40(4): 562-566. DOI: 10.12122/j.issn.1673-4254.2020.04.18.
[27]
Nadrljanski MM, Marković BB, Milošević ZČ. Breast ductal carcinoma in situ: morphologic and kinetic MRI findings[J]. Iran J Radiol, 2013, 10(2): 99-102. DOI: 10.5812/iranjradiol.4876.
[28]
Lee JM, Ichikawa L, Valencia E, et al. Performance benchmarks for screening breast MR imaging in community practice[J]. Radiology, 2017, 285(1): 44-52. DOI: 10.1148/radiol.2017162033.
[29]
Dietzel M, Krug B, Clauser P, et al. A multicentric comparison of apparent diffusion coefficient mapping and the Kaiser score in the assessment of breast lesions[J]. Invest Radiol, 2021, 56(5): 274-282. DOI: 10.1097/RLI.0000000000000739.019-06444-w.}

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