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多参数MRI联合免疫炎症指标预测乳腺癌腋窝淋巴结转移的价值研究
戴兴伟 申云霞 黄奕巧 杨春燕 王秀荣

Cite this article as: Dai XW, Shen YX, Huang YQ, et al. Value of multi-parameter MRI combined with immune inflammatory markers in predicting axillary lymph node metastasis of breast cancer[J]. Chin J Magn Reson Imaging, 2022, 13(7): 116-120, 128.本文引用格式:戴兴伟, 申云霞, 黄奕巧, 等. 多参数MRI联合免疫炎症指标预测乳腺癌腋窝淋巴结转移的价值研究[J]. 磁共振成像, 2022, 13(7): 1116-120, 128. DOI:10.12015/issn.1674-8034.2022.07.021.


[摘要] 目的 探讨多参数MRI联合免疫炎症指标预测乳腺癌腋窝淋巴结转移(axillary lymph node metastasis, ALNM)的价值。材料与方法 回顾性分析52例经手术病理证实为乳腺癌的患者临床资料,根据病理结果将其分为淋巴结转移组与无转移组,单因素分析评估两组患者的临床病理特征、免疫炎症指标及多参数MRI特征与ALNM的关系。采用多因素logistic回归筛选临床和MRI危险因素建立临床预测模型、MRI预测模型及联合模型。Spearman等级相关方法分析免疫炎症指标与免疫组化因子、多参数MRI特征之间的相关性。通过绘制受试者工作特征(receiver operating characteristic, ROC)曲线和校准曲线评估模型效能,Delong检验和决策曲线(decision curve analysis, DCA)比较和验证不同模型的预测性能。结果 logistic回归分析显示乳腺癌淋巴结转移组与无转移组间的Ki-67表达、血小板/淋巴细胞比值(platelet-lymphocyte ratio, PLR)、肿瘤长径、瘤周最大表观扩散系数(peritumoral maximum apparent diffusion coefficient, ADCpmax)、瘤周-肿瘤ADC比值(ratio of peritumoral-tumor ADC, ADCratio)及MRI淋巴结特征具有统计学意义(P<0.05);PLR与ADCpmax、ADCratio呈正相关(P<0.05);临床预测模型(Ki-67+PLR)曲线下面积(area under the curve, AUC)为0.722,多参数MRI预测模型(肿瘤长径+ADCpmax+ADCratio+MRI淋巴结特征)AUC为0.898,联合预测模型AUC为0.914。联合预测模型的临床价值高于临床预测模型。结论 多参数MRI联合免疫炎症指标PLR可用于术前无创性预测乳腺癌患者腋窝淋巴结状态,为临床诊断及预后评估提供参考。
[Abstract] Objective To investigate the value of multi-parameter MRI combined with immune inflammatory markers in axillary lymph node metastasis (ALNM) of breast cancer.Materials and Methods In this retrospective analysis, 52 breast cancer patients were divided into lymph node metastasis group and non-metastasis group according to the pathological results. The relationship between clinical, pathological, immune inflammatory markers, multi-parameter MRI features and axillary lymph node metastasis was evaluated by univariate analysis. Multivariate logistic regression was used to screen clinical and MRI risk factors to establish clinical prediction model, MRI prediction model and combined model. The correlation between immune inflammatory markers, immunohistochemical factor expression and multi-parameter MRI features were analyzed by spearman rank correlation analysis. Evaluate model effectiveness by drawing receiver operating characteristic (ROC) curve and calibration curve. The predictive performance of different models was compared and verified by the Delong test and decision curve analysis (DCA).Results Logistic regression analysis showed that Ki-67 expression, platelet-lymphocyte ratio (PLR), tumor size, the peritumoral maximum apparent diffusion coefficient (ADCpmax), the ratio of peritumoral tumor ADC (ADCratio) and MRI lymph node characteristics were statistically significant (P<0.05). PLR was positively correlated with ADCpmax and ADCratio (P<0.05). The area under the curve (AUC) of the clinical prediction model (Ki-67+PLR) was 0.722, the AUC of the multi-parameter MRI prediction model (tumor length+ADCpmax+ADCratio+MRI lymph node characteristics) was 0.898, and the AUC of the combined prediction model was 0.914. DCA showed that the clinical value of the combined model was higher than that of the clinical prediction model.Conclusions Multi-parameter MRI combined with immune inflammatory index PLR can be used to predict the status of axillary lymph nodes in breast cancer patients non-invasively before surgery, and provide a reference for clinical diagnosis and prognosis evaluation.
[关键词] 乳腺癌;磁共振成像;表观扩散系数;炎症指标;淋巴结转移
[Keywords] breast cancer;magnetic resonance imaging;apparent diffusion coefficient;inflammatory markers;lymph node metastasis

戴兴伟 1, 2   申云霞 2*   黄奕巧 2   杨春燕 3   王秀荣 2  

1 广州中医药大学深圳临床医学院,深圳 518116

2 深圳市龙岗中心医院影像科,深圳 518116

3 深圳市龙岗中心医院妇产科,深圳 518116

申云霞,E-mail:yunxiashen@sina.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: 深圳市龙岗区科技创新局基金项目 LGKCYLWS2020005
收稿日期:2022-03-15
接受日期:2022-07-01
中图分类号:R445.2  R737.9 
文献标识码:A
DOI: 10.12015/issn.1674-8034.2022.07.021
本文引用格式:戴兴伟, 申云霞, 黄奕巧, 等. 多参数MRI联合免疫炎症指标预测乳腺癌腋窝淋巴结转移的价值研究[J]. 磁共振成像, 2022, 13(7): 1116-120, 128. DOI:10.12015/issn.1674-8034.2022.07.021

       乳腺癌发病率、死亡率逐年增加,截至2020年,乳腺癌发病率首次超过肺癌,占癌症相关死亡率15.5%[1]。腋窝淋巴结状态及阳性淋巴结数目决定乳腺癌TNM分期,准确评估腋窝淋巴结性质对临床治疗方案选择及患者预后均有重要意义。临床上最常采用前哨淋巴结活检或腋窝淋巴结清除术来评估淋巴结良恶性,然而其属于侵入性检查,可造成肩部功能障碍、神经损伤、上肢麻木及淋巴水肿等严重并发症。超声是检查乳腺癌腋窝淋巴结转移(axillary lymph node metastasis, ALNM)的一种常见影像手段,也有通过超声造影来鉴别腋窝淋巴结是否转移的研究,但结果可能因操作者的手法及经验不同而存在主观差异[2]。正电子发射计算机断层显像(positron emission tomography-computed tomography, PET-CT)作为一种全身性的影像学检查,空间分辨率较低,诊断淋巴结转移的灵敏度较低[3]。相比之下,MRI可以多参数多序列成像,具有较高的软组织分辨力,能够通过定量评估反映肿瘤微观结构,如细胞膜完整性、血管通透性及组织细胞密度[4]

       外周血免疫相关炎症指标的动态变化能够反映机体抗肿瘤免疫与炎症反应的微观环境,与肿瘤的预后密切相关[5]。有学者发现乳腺癌ALNM与外周血免疫相关炎症指标有关[6, 7],同时发现中性粒细胞/淋巴细胞比值(neutrophil-lymphocyte ratio, NLR)、血小板/淋巴细胞比值(platelet-lymphocyte ratio, PLR)、单核细胞/淋巴细胞比值(monocyte-lymphocyte ratio, MLR)与食管癌、前列腺癌、膀胱癌及肝癌等多种癌症预后相关[8, 9, 10, 11]。但关于炎症指标与乳腺癌多参数MRI特征之间的相关性未见报道。因此本研究旨在探讨多参数MRI与免疫组化因子、免疫相关炎症指标在ALNM中的预测价值及相关性,为临床诊断及预后评估提供参考。

1 材料与方法

1.1 研究对象

       回顾性分析2019年1月至2021年12月在深圳市龙岗中心医院就诊,经手术病理证实为乳腺癌的患者资料。纳入标准:(1)病理证实为乳腺癌;(2)治疗(包括药物治疗和手术)前一周内同时行动态增强磁共振成像(dynamic contrast enhanced-magnetic resonance imaging, DCE-MRI)、扩散加权成像(diffusion-weighted imaging, DWI)检查,数据完整,图像清晰;(3)MRI检查前未行腋窝穿刺、放化疗和手术治疗;(4)治疗前一周内行血常规检查;(5)所有患者均进行穿刺活检或者肿块切除并取得病理结果。排除标准:(1)肿瘤过小无法进行数据测量者;(2)临床资料不齐者;(3)假体植入史;(4)血常规检查前一周有感染症状;(5)血常规检查前接受过影响血液学指标的治疗,如免疫抑制治疗或者长期口服阿司匹林、华法林和其他抗凝血药物等。本研究经过深圳市龙岗中心医院医学伦理委员会批准,免除受试者知情同意(批准文号:2021ECPJ014)。

1.2 检查方法

1.2.1 仪器设备

       采用德国西门子Prisma 3.0 T磁共振扫描仪及16通道乳腺专用相控阵线圈。被检患者取俯卧位,双乳自然悬垂于线圈内。所有患者行平扫、动态增强及ZOOMit序列扫描。扫描参数如下:

       平扫T1WI扫描参数如下:TR 6.0 ms,TE 2.46 ms,FOV 300 mm×300 mm,层厚1.5 mm,矩阵326×384;轴位T2WI脂肪抑制序列扫描参数如下:TR 6460 ms,TE 71 ms,FOV 300 mm×300 mm,层厚4.0 mm,矩阵326×384。动态增强扫描参数如下:TR 4.50 ms,TE 1.58 ms,FOV 300 mm×300 mm,矩阵307×384,层厚1.5 mm,平扫1期后注射对比剂钆喷酸葡胺(拜耳先灵医药公司,德国),剂量0.2 mmol/kg,随后以流速1.5 mL/s注射生理盐水20 mL。共扫描9期,每期30 s,3 min后扫描延迟期。DWI扫描参数如下:采用单次激发平面回波成像技术,b值为50、800 s/mm2。TR 3500 ms,TE 56.0 ms,FOV 173 mm×340 mm,矩阵78×200,层厚5.0 mm。

1.2.2 图像分析

       由2名经验丰富的放射科医师(具有3年MRI诊断经验的医师和20年MRI诊断经验的副主任医师)在西门子后处理工作站Syngo.via(20A.HF06版本)上采用双盲法独立分析MRI图像,意见不一致时协商解决。在MRI图像上观察腋窝淋巴结特征。MRI诊断淋巴结转移的依据:(1)短径/长径>0.6;(2)脂肪门消失;(3)淋巴结强化明显;(4)皮髓质界限不清。如果出现以上四项中的一项,则认为MRI淋巴结阳性。最大密度投影(maximal intensity projection, MIP)图上观察肿瘤周围血管征象,如果瘤周血管增多增粗则为周围血管征阳性。在肿瘤最大层面强化最明显处勾画感兴趣区(region of interest, ROI),避开囊变坏死及出血区域。ROI取值控制在20 mm2,自动生成时间信号强度曲线(time-signal intensity curves, TIC)。DWI图像经工作站后处理获得表观扩散系数(apparent diffusion coefficient, ADC)图,定位肿瘤最大层面,勾画一个ROI,尽量避开囊变坏死及出血区域,得到肿瘤ADC平均值(mean values of tumor ADC, ADCtmean),然后选择肿瘤周围2 mm内临近乳腺实质组织ADC值最高处(视觉感知)勾画三个ROI,测量其ADC值,记录瘤周最大ADC值(peritumoral maximum ADC, ADCpmax)。计算肿瘤ADC值差值(difference of tumor ADC, ADCtdiff)和瘤周-肿瘤ADC比值(ratio of peritumoral-tumor ADC, ADCratio)。计算公式如下:ADCtdiff=肿瘤最大ADC值-肿瘤最小ADC值;ADCratio=ADCpmax/ADCtmean(图1)。所有数据测量三次,取其平均值。

图1  女,52岁,右乳肿块,病理证实为非特殊类型浸润性导管癌Ⅱ级,右侧腋窝淋巴结转移,肿块长径为32.9 mm。1A:扩散加权成像显示病灶高信号;1B:表观扩散系数(ADC)图显示病灶低信号,红色线为勾画整个肿瘤区域,获得肿瘤ADC平均值和差值分别为0.83×10-3 mm2/s、0.63×10-3 mm2/s。蓝色圈为勾画瘤周区域,勾画三次,获得瘤周ADC最大值为1.99×10-3 mm2/s。1C:MRI动态增强扫描第三期,肿瘤呈不均匀强化;1D:右侧腋窝淋巴结转移。

1.2.3 病理分析

       所有标本均由外科医师获得,由病理医师采用免疫组织化学染色方法分析生物因子表达。根据指南[12],雌激素受体(estrogen receptor, ER)与孕激素受体(progesterone receptor, PR)阳性表达细胞<1%为阴性,≥1%为阳性。人表皮生长因子受体2(human epidermal growth factor receptor 2, HER-2)染色结果(-)(+)为阴性,(+++)为阳性,(++)则采用荧光原位杂交法(fluorescence in situ hybridization, FISH)确定HER-2状态。参照2013年St.Gallen国际专家共识[13],增殖细胞核抗原(Ki-67)阳性表达细胞<20%为低表达,≥20%为高表达。

1.2.4 外周血免疫相关炎症指标分析

       登录病历资料系统,提取患者接受治疗前一周内血常规的检查结果,包括中性粒细胞绝对值(absolute neutrophil count, N, ×109/L)、淋巴细胞绝对值(absolute lymphocyte count, L, ×109/L)、单核细胞绝对值(absolute monocyte count, M, ×109/L)及血小板计数(platelet count, P, ×109/L)。计算NLR、PLR、MLR,计算公式如下:NLR=N/L;PLR=P/L;MLR=M/L。

1.3 统计学方法

       应用SPSS 26.0软件、R软件(3.5.1版本,http://www.r-project.org/)和Medcalc 20.1.0软件进行数据处理。连续变量满足正态分布采用平均数±标准差表示,独立样本t检验进行比较;偏态分布采用中位数和四分位距表示,Mann-Whitney U检验进行比较。分类变量采用数量和百分比表示,χ2检验或Fisher精确检验进行比较。Spearman等级相关方法分析外周血免疫炎症指标与免疫组化因子、多参数MRI特征之间的相关性。将单因素分析有意义的指标纳入多因素logistic回归分析,对各参数及联合参数模型绘制受试者工作特征曲线(receiver operating characteristic, ROC),计算曲线下面积(area under the curve, AUC)。使用Delong检验比较ROC曲线,决策曲线(decision curve analysis, DCA)及校准曲线对模型进行评价。组间相关系数(interclass correlation coefficients, ICC)用于评价ADC观察者间一致性,ICC>0.75认为一致性良好。P<0.05为差异具有统计学意义。

2 结果

       根据纳入标准初步筛选出85份病例。根据排除标准,肿瘤过小无法进行数据测量者8例、临床资料不齐者12例、假体植入者1例、血常规检查前一周有感染症状者7例,血常规检查前接受过影响血液学指标的治疗者5例,最终入组52例乳腺癌病例。患者均为女性,年龄29~65(45.02±7.71)岁。32例患者腋窝淋巴结转移,20例无转移;其中浸润性导管癌43例,导管内原位癌7例、浸润性小叶癌2例。

2.1 观察者间一致性

       两名医师独立测量ADCtmean、ADCtdiff、ADCpmax和ADCratio具有良好的一致性。ICC范围为0.779~0.932(>0.75)。选择第一位医师ADC测量值进行分析。

2.2 淋巴结转移组与无转移组间临床特征、病理特征、免疫炎症指标和多参数MRI特征比较

       对乳腺癌淋巴结转移组与无转移组间临床特征、病理特征、免疫炎症指标及多参数MRI特征进行单因素分析。根据Youden指数计算NLR、PLR和MLR最佳临界值分别为2.437、218.788和0.228。单因素分析表明,Ki-67表达、PLR、肿瘤长径、周围血管征、内部强化方式、ADCtdiff、ADCpmax、ADCratio及MRI淋巴结特征差异具有统计学意义(P<0.05),见表1。将单因素分析有意义的结果进一步采用多因素logistic回归向前步进法分析,结果显示Ki-67、PLR、肿瘤长径、ADCpmax和ADCratio、MRI淋巴结特征是预测乳腺癌腋窝淋巴结转移的独立影响因素(P<0.05)(表2)。

图2  临床模型、MRI模型及联合模型预测乳腺癌腋窝淋巴结转移的受试者工作特征(ROC)曲线图以及决策曲线图和校正曲线图。2A:临床预测模型ROC曲线;2B:MRI预测模型ROC曲线;2C:联合预测模型ROC曲线。
表1  淋巴结转移组与无转移组乳腺癌患者的特征比较
表2  单因素分析显著变量的多因素logistic分析

2.3 免疫炎症指标与免疫组化因子、多参数MRI特征的相关性分析

       免疫炎症指标PLR与ADCpmax、ADCratio呈正相关,差异具有统计学意义(r=0.278,P=0.042;r=0.303,P=0.025)。免疫炎症指标NLR、PLR和MLR与其他免疫组化因子及多参数MRI特征无明显相关性(P>0.05)。

2.4 ALNM预测模型建立与评估

       通过多因素logistic回归分析表明,Ki-67、PLR、肿瘤长径、ADCpmax、ADCratio及MRI淋巴结特征是乳腺癌ALNM的危险因素,以此构建临床预测模型(PLR+Ki-67)、MRI预测模型(肿瘤长径+ADCpmax+ADCratio+淋巴结MRI特征)及联合预测模型(临床预测模型+MRI预测模型)。结果表明,临床预测模型AUC为0.722;MRI预测模型AUC为0.898,其中,ADCpmax的AUC最大,为0.845,敏感度为71.90%,特异度为95.00%;联合Ki-67、PLR及MRI预测模型可以将AUC提高至0.914,见表3图2

       Delong结果显示,联合预测模型的AUC值高于临床预测模型(P=0.002),但是与MRI预测模型的AUC差异不显著(P=0.200)。MRI预测模型的AUC高于临床预测模型(P=0.010)。DCA表明联合预测模型具有良好的临床效益,校准曲线表明联合预测模型评估ALNM效能良好(图3)。

图3  决策曲线和校准曲线。3A:临床模型、MRI模型和联合模型决策曲线;3B:联合模型校准曲线。
表3  Ki-67、PLR及多参数MRI预测ALNM的效能

3 讨论

       本研究旨在探讨多参数MRI联合免疫炎症指标预测乳腺癌ALNM的临床价值。研究结果表明淋巴结转移组PLR水平比无转移组高,且首次证实PLR与ADCpmax和ADCratio成正相关。此外,联合Ki-67、PLR、肿瘤长径、ADCpmax、ADCratio及淋巴结MRI特征构建联合预测模型,可以提高ALNM诊断效能,为预测乳腺癌ALNM提供临床依据。

3.1 乳腺癌患者免疫炎症指标与多参数MRI、免疫组化因子的相关性分析及与ALNM的关系

       据我们所知,目前没有研究分析乳腺癌患者外周血免疫炎症指标与肿瘤ADC值的相关性。Liang等[14]的研究表明,宫颈癌患者NLR和肿瘤ADC值之间没有显著相关性,但该研究并未考虑PLR和LMR。本研究首次证实乳腺癌患者PLR与ADCpmax和ADCratio呈正相关。根据先前的研究,肿瘤炎症反应通过诱导组织损伤,刺激巨核细胞分化、促进血小板生成素产生,导致外周血小板增多、活化与聚集[15, 16]。此外,血小板能够分泌细胞生长因子,包括集落刺激因子1、血管内皮生长因子和血小板衍生生长因子受体,促进肿瘤血管生成和上皮-间质转化[17, 18, 19]。而细胞因子的释放会导致肿瘤新生血管增多及血管通透性增加,进而导致瘤周水肿[20, 21]。Igarashi等[22]与Choi等[23]认为瘤周水肿和血管密度增加与ADCpmax和ADCratio密切相关。因此,本研究推断PLR高水平可能会通过影响血管通透性和新生血管密度,导致瘤周微观环境发生变化,进而介导肿瘤细胞转移。此外,梅章懿等[24]研究表明NLR、PLR与乳腺癌淋巴结转移相关,且NLR是淋巴结转移的独立预测因子。Cho等[7]的研究发现淋巴结转移与LMR高水平相关,与NLR、PLR相关性不明显。与Morkavuk等[25]的研究结果一致,本研究证实淋巴结转移组PLR高于淋巴结无转移组。提示乳腺癌ALNM与血小板存在相关性,且最新研究也证实血小板促进循环肿瘤细胞(circulating tumor cell, CTC)外渗,诱导其增殖,在血小板丰富的微环境中肿瘤细胞更容易转移扩散[26, 27]

       本研究结果表明免疫炎症指标与免疫组化因子之间无相关性,与Yang等[6]研究结果基本一致。Xu等[28]却认为ER阳性与NLP、PLR低水平相关。结论的差异可能是由于NLR、PLR的截断值差异造成的。此外,研究表明NLR高水平与死亡率增加显著相关[29]。多项研究证实,在乳腺癌患者中,低水平NLR患者的无病生存期明显长于高水平NLR患者[30, 31]。换言之,中性粒细胞能够分泌细胞因子和肿瘤生长促进因子,淋巴细胞分泌一些与控制肿瘤生长有关的细胞因子,如IFN-γ、TNF-α等,NLR表达水平越低,则表示肿瘤侵袭力较弱,其总生存率和无病生存率较长[32]

3.2 乳腺癌多参数MRI特征与ALNM的关系

       淋巴结转移组与无转移组间肿瘤周围血管征和强化方式差异具有统计学意义(P<0.05)。研究表明,肿瘤内部不均匀强化或者边缘强化是前哨淋巴结和远处转移的独立预后因素[18]。肿瘤边缘强化与肿瘤周围的高微血管密度及纤维化程度较高有关。当肿瘤细胞增殖活跃,组织微环境缺氧,肿瘤相关巨噬细胞分泌大量的生长因子及趋化因子在肿瘤边缘形成反应带,促进肿瘤细胞增殖、血管生成及周围纤维组织增生[17, 33]。但多因素logistic回归分析并未将周围血管征、强化方式纳入MRI联合预测模型。究其原因可能与样本量较小有关。此外,本研究表明肿瘤大小是评估ALNM的独立预测因素,与Xue等[34]的研究结果一致。众所周知,肿瘤直径增大,与周围的淋巴管网接触增多,癌细胞易发生远处转移[35]。然而,必须强调的是,小肿瘤也可能是由活跃的癌细胞在短时间内突然发展起来的。因此,考虑ALNM时,必须结合肿瘤及腋窝淋巴结其他参数特征。

       DWI是一种基于组织间水分子扩散运动检测细胞密度、膜完整性和肿瘤微观结构的功能MRI技术。已有研究证实ADC值在区分良恶性肿瘤方面是可行的,也是预测肿瘤预后的一个强有力指标[36]。乳腺癌作为一种高度异质性肿瘤,病灶内部细胞密度、组织成分不尽相同,单独采用肿瘤ADC值评估其侵袭力不够全面[37]。ADCtdiff、ADCpmax及ADCratio可以尽量避免相关混杂因素的影响,反映肿瘤异质性和瘤周微观结构[37, 38]。Choi等[23]的研究也证实了ADCpmax、ADCratio是ALNM的独立预测因子,与肿瘤侵袭力密切相关,与本研究结果一致。

3.3 诊断效能

       研究结果显示,MRI预测模型AUC为0.898,高于临床预测模型(AUC=0.722),联合Ki-67、PLR和MRI预测模型可将AUC提高至0.914。Yang等[6]研究表明PLR预测ALNM的AUC为0.685,与本研究结果(AUC=0.653)基本接近,但在联合预测模型中占的比重较少,提示单独应用Ki-67和PLR预测ALNM存在一定局限性,联合MRI技术可以提供更加精确、全面的诊断信息。Choi等[23]联合肿瘤长径、ADCratio和肿瘤强化模式的MRI预测模型AUC为0.80,表明MRI特征在预测ALNM时具有一定临床参考价值,但该研究AUC小于本研究,可能是因为本研究MRI预测模型除了纳入肿瘤长径和ADCratio,还包括ADCpmax,因此提高MRI联合模型的诊断效能。

3.4 局限性

       本研究存在以下局限性:(1)本研究为单中心研究且样本量相对较小。因此,在后续的研究中有必要进行多中心研究进一步验证;(2)本研究采用的是二维ROI测量方式,基于ADC值的三维直方图分析与免疫炎症指标及ALNM的相关性是否更显著值得进一步探讨;(3)本研究没有根据乳腺癌不同分子分型及病理分型分类讨论免疫炎症指标与ALNM的相关性,有待以后扩大研究样本量进一步分析。

       综上所述,免疫炎症指标PLR高水平与乳腺癌ALNM密切相关,且与ADCpmax、ADCratio呈正相关,间接反映肿瘤侵袭力,进一步证实肿瘤细胞可能更容易在富含血小板的微环境中扩散。联合Ki-67、PLR及多参数MRI可用于术前无创性预测乳腺癌患者腋窝淋巴结状态,为临床诊断及预后评估提供参考。

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