1枚前哨淋巴结阳性早期乳腺癌保腋窝的可行性分析

目的探讨1枚前哨淋巴结（sentinel lymph node,SLN）阳性的早期乳腺癌患者保腋窝（omitting axillary dissection,OAD）的可行性。方法用美蓝作为示踪剂先行乳腺癌前哨淋巴结活检术（sentinel lymph node biopsy,SLNB）,根据快速冰冻病理结果分为SLN阴性组与1枚SLN阳性组,随后两组均行常规腋窝淋巴结清扫（axillary lymph node dissection,ALND）以解剖出非前哨淋巴结（non—sentinellymphnode,NSLN）,比较两组间NSLN的阳性率。结果SLN阴性组30例,1例NSLN阳性,阳性率为3．3％,准确性为96．7％（29／30）;1枚SLN阳性组30例,仅3例NSLN阳性,阳性率为10．0％;两组阳性率差异无统计学意义（X^2=1．071,P=0．612）。全组随访1～48个月,均无区域淋巴结复发。结论1枚SLN阳性的早期乳腺癌患者可考虑OAD。     

不同分子分型1～2枚前哨淋巴结阳性乳腺癌免行腋窝淋巴结清扫的多因素分析

目的探讨不同分子分型1～2枚前哨淋巴结（sentinel lymph nodes,SLNs）阳性乳腺癌免行腋窝淋巴结清扫（axillary lymph node dissection,ALND）的临床病理因素,并为临床精准化提供依据。方法回顾性分析2009年6月至2018年6月274例就诊于内蒙古医科大学附属医院和内蒙古医科大学附属人民医院经病理证实的乳腺癌患者的临床病理资料,采用单因素及Logistic多因素分析筛选1～2枚SLN阳性但非前哨淋巴结（nonsentinel lymph node,NSLN）转移率较低的患者,同时明确其与不同分子分型的关系。结果274例1～2枚SLN阳性乳腺癌患者中,NSLN转移率为36.9%（101/274）。HER-2阳性（HR阳性）患者NSLN转移率最高, 占55.3%（21/38）;三阴性乳腺癌（triple negative breast cancer,TNBC）患者中NSLN转移率最低,占18.5%（5/27）。Luminal B型（HER-2阴性）乳腺癌患者的NSLN转移率明显高于Luminal A型（P=0.010）和TNBC患者（P=0.011）;HER-2阳性（HR阳性）乳腺癌患者的NSLN转移率明显高于Luminal A型（P=0.002）和TNBC患者（P=0.003）。 Logistic多因素分析显示,SLN转移数目（OR=4.022, 95%CI为2.348～6.889,P<0.001）,SLN检测（OR=3.846, 95%CI为1.541～9.600,P=0.004）,组织学分级（P<0.001）和分子分型（P=0.004）是1～2枚SLN阳性乳腺癌NSLN转移的独立影响因素。结论Luminal B型（HER-2阴性）和HER-2阳性（HR阳性）患者的NSLN阳性率较高,SLN转移数目、SLN检测、组织学分级和分子分型是NSLN转移的独立影响因素。      

前哨淋巴结阳性乳腺癌患者非前哨淋巴结转移的影响因素分析

目的 前哨淋巴结活检术(sentinel lymph nodes biopsy,SLNB)已经广泛应用于乳腺癌外科治疗,临床发现部分转移淋巴结仅局限于前哨淋巴结.本研究分析前哨淋巴结(sentinel lymph nodes,SLN)阳性乳腺癌患者非前哨淋巴结(non-sentinel lymph nodes,NSLN)转移的影响因素,从而避免不必要的腋窝淋巴结清除(axillary lymph node dissection,ALND).方法 回顾性分析聊城市人民医院乳腺外科2013-07-1-2015-06-30 SLNB阳性行ALND的77例女性乳腺癌患者临床病理资料,分析NSLN转移的影响因素.结果 在SIN清除个数≥4个的情况下,单因素分析发现阳性SLN≥2个(x2=10.109,P=0.01)以及LuminalB型患者(x2=6.442,P=0.02)发生NSLN转移的风险高.Logistic回归进行多因素分析发现,阳性SLN≥2个是影响NSLN转移的独立危险因素(OR=207.833,95％ CI为1.430～30 201.980,P=0.036).结论 阳性SLN数和分子亚型是影响NSLN转移的危险因素,阳性SLN≥2个是影响NSLN转移的独立危险因素.     

腋窝淋巴结阳性乳腺癌新辅助化疗后腋窝治疗选择的研究

  目的  分析腋窝淋巴结（axillary lymph node，ALN）阳性乳腺癌患者新辅助化疗（neoadjuvant chemotherapy，NAC）后经前哨淋巴结活检术（sentinel lymph node biopsy，SLNB）评估ALN状态的可行性，并探讨腋窝的后续治疗选择。  方法  回顾性分析2016年1月至2018年1月天津医科大学肿瘤医院收治的82例ALN阳性乳腺癌患者的临床资料，均NAC后行SLNB，评估其检出率、准确率、假阴性率（false negative rate，FNR）并对可能影响因素进行分层分析。  结果  82例患者腋窝病理完全缓解（pathological com? plete response，pCR）43例、占52.4%，SLNB检出率为97.56%（80/82），准确率为88.75%（71/80），FNR为23.08%（9/39）。前哨淋巴结（sentinel lymph node，SLN）检出数目为1、2枚和数目≥3枚时，FNR分别为20.0%（2/10）、71.4%（5/7）和9.1%（2/22），准确率分别为90.9%（20/22）、66.7%（10/15）和95.3%（41/43），差异具有统计学意义（均P < 0.05）。  结论  ALN阳性乳腺癌患者NAC后行SLNB总体FNR较高，尚未达到临床可接受范围，不能完全取代腋窝淋巴结清扫（axillary lymph node dissection，ALND），SLN检出数目≥3枚时SLNB可准确评估ALN状态。      

早期乳腺癌腋窝前哨淋巴结阳性患者腋窝非前哨淋巴结转移风险预测

目的 分析前哨淋巴结活检(SLNB)1~2个阳性乳腺癌患者中非前哨淋巴结(NSLN)转移的影响因素并构建预测模型。方法 回顾分析2008-2014年中国医学科学院北京协和医学院肿瘤医院未行新辅助化疗前哨淋巴结 1~2个阳性并行腋窝淋巴结清扫的乳腺癌患者的临床病理因素。计数资料组间比较采用χ²检验,多因素分析采用Logistic回归模型。以AUC值和校正曲线对Nomogram预测模型进行评估。结果 共 270例患者纳入研究,87例(32.2%)存在NSLN转移。中位年龄46(21~80)岁,中位SLN送检个数4(1~10)个,中位腋窝淋巴结清扫个数20(10~41)个。单因素分析结果显示病理分级、SLN宏转移、阳性SLN个数和阴性SLN个数是腋窝NSLN转移的影响因素(P=0.001~0.045)。多因素分析结果显示病理分级、阳性SLN个数和阴性SLN个数是NSLN转移的独立影响因素(P=0.000~0.041)。乳腺癌NSLN转移Nomogram预测模型AUC=0.70,当预测患者的NSLN转移率≤15%时,假阴性率仅为10.5%。结论 Nomogram预测模型可作为临床医师进行腋窝处理时的决策参考,对于NSLN转移概率低的患者可以避免行腋窝淋巴结清扫或腋窝放疗。     

术中快速预测乳腺癌非前哨淋巴结转移模型的建立与验证研究

背景与目的:大部分前哨淋巴结(sentinel lymph node,SLN)阳性而接受腋窝淋巴结清扫术(axillary lymph node dissection,ALND)的患者,腋窝非前哨淋巴结(non-sentinel lymph node,nSLN)并没有发生转移,因此准确预测nSLN转移至关重要.该研究将建立基于分子诊断一步核酸扩增法(one-step nucleic acid amplification,OSNA)的术中快速预测乳腺癌nSLN转移的模型,以期有效指导乳腺癌后续治疗.方法:利用2010年OSNA临床试验入组的552例患者中SLN阳性、并接受ALND的103例患者数据,建立基于分子诊断的乳腺癌NSLN转移的预测模型,并利用2015年OSNA临床试验入组的327例患者中61例符合相同条件的患者数据进行验证.结果:原发肿瘤大小、SLN总肿瘤负荷、SLN阳性数及阴性数是NSLN转移的四个独立相关因素,利用这四个因素建立预测列线图,得出建模组患者的受试者工作特征(receiver operating characteristic curve,ROC)曲线的曲线下面积(area under the ROC curve,AUC)为0.814,验证组患者的AUC为0.842.利用验证组61例患者影像学评估的肿瘤大小替代病理大小对本模型进行了验证,得出AUC为0.838,与模型验证性AUC相比差异无统计学意义(P=0.7406).结论:基于分子诊断的乳腺癌预测nSLN转移的模型既可以术中快速预测腋窝淋巴结转移风险,也可以术后常规预测,明显优于其他预测模型,对后续腋窝的处理及放疗靶区勾画具有更好的指导价值.     

前哨淋巴结阳性早期乳腺癌患者非前哨淋巴结阳性预测因素

摘 要：［目的］ 探讨前哨淋巴结( sentinel lymph node,SLN)阳性早期乳腺癌患者非前哨淋巴结转移的预测因素。［方法］ 回顾性分析2010年7月至2017年8月河南省肿瘤医院578例SLN阳性乳腺癌患者的临床病例资料。通过术中印片及术后连续切片HE染色检测SLN。［结果］ 全组女性非前哨淋巴结阳性率为38.4%。单因素分析显示,阳性SLN数目（χ2=70.114,P=0.001）、阴性SLN数目（χ2=49.095,P<0.001）及Ki67表达水平（χ2=6.924,P=0.009）与非前哨淋巴结转移相关。多因素分析显示,阳性SLN数目(OR=2.076,95％CI：1.686~2.556,P<0.001)、阴性SLN数目(OR=0.673,95％CI：0.586~0.773,P<0.001)和Ki67表达水平(OR=1.807,95％CI：1.150~2.840,P=0.010)是非前哨淋巴结转移的独立预测因素。［结论］ 阳性SLN数目、阴性SLN数目和Ki67表达是乳腺癌非前哨淋巴结转移的独立预测因素。     

ACOSOG Z0011试验标准用于中国前哨淋巴结阳性乳腺癌患者以避免腋窝淋巴结清扫的可行性研究

背景与目的:美国外科医师学会肿瘤学组(American College of Surgeons Oncology Group,ACOSOG)Z0011试验的结果改变了乳腺癌前哨淋巴结(sentinel lymph node,SLN)阳性患者的传统治疗模式。本研究的目的在于探讨ACOSOG Z0011试验标准用于中国前哨淋巴结阳性乳腺癌患者以避免腋窝淋巴结清扫(axillary lymph node dissection,ALND)的可行性。方法:连续收集194例SLN阳性的乳腺癌患者,根据Z0011的标准分为可以只做前哨淋巴结活检(sentinel lymph node biopsy,SLNB)组和仍需做ALND组。将SLNB组患者的临床病理学特征与Z0011试验标准的原始入组人群进行比较,再将SLNB组与ALND组患者的临床病理学特征进行比较。结果:194例患者中有77例符合Z0011标准可以只做SLNB,117例患者不符合Z0011标准,需要做ALND;SLNB组患者与Z0011标准原始入组人群比较,T1期肿瘤、ER阳性肿瘤、淋巴结转移数目少的肿瘤、非前哨淋巴结(non-sentinel lymph node,NSLN)阴性的肿瘤都显著多于Z0011标准原始人群,差异有统计学意义(P<0.05)。本研究ALND组患者与SLNB组患者比较,T2、T3期肿瘤较多,但差异无统计学意义(P>0.05)。ALND组腋窝淋巴结转移数目多的患者比例要明显多于SLNB组,NSLN阳性患者比例也高于SLNB组,差异均有统计学意义(P<0.05)。结论:将Z0011试验标准用于SLN阳性乳腺癌患者,能够筛选出较Z0011标准研究中预后更好、更为低危的患者,使得该部分患者可以更为安全的只接受SLNB。     

乳腺癌前哨淋巴结活检术中分子诊断的研究进展

乳腺癌前哨淋巴结（sentinel lymph node,SLN）能准确反映腋窝淋巴结的状况。前哨淋巴结活检术（sentinel lymph node biopsy,SLNB）已成为临床腋窝淋巴结阴性早期乳腺癌患者的标准腋窝处理模式。准确、快速、客观的SLN术中诊断可以使SLN阳性患者通过一次手术完成腋窝淋巴结清除,避免二次手术带来的风险及并发症,为患者和术者节约了时间,降低了手术风险,并减少了二次手术带来的费用负担。近年来,术中分子诊断已成为乳腺癌SLN研究的热点之一。     

临床淋巴结阳性新辅助治疗后腋窝降阶处理的研究进展

在优效系统治疗和精准放疗的时代背景下，乳腺癌新辅助治疗（neoadjuvant treatment,NAT）有助于乳房肿瘤降期实现保乳和腋窝降期，使患者豁免腋窝淋巴结清扫（axillary lymph node dissection,ALND）。目前，在临床腋窝淋巴结阳性的患者中，人表皮生长因子受体2(human epidermal growth factor receptor 2,HER2)阳性和三阴性乳腺癌（triplenegativebreastcancer,TNBC）亚型接受NAT后可达到较高的腋窝病理学完全缓解率（axillarynodalpathologiccomplete response,apCR），有望实现腋窝局部降阶梯处理，相关指南与专家共识推荐初始临床淋巴结阴性（clinicallymphnode negative,cN0）的患者NAT后前哨淋巴结（sentinel lymph node,SLN）阴性可行前哨淋巴结活检（sentinel lymph node biopsy,SLNB）替代ALND,NAT后SLN存在较低肿瘤残留负荷的患者可考虑放疗替代ALND。初始...     

The MSKCC nomogram for prediction the likelihood of non-sentinel node involvement in a German breast cancer population

Objective To assess whether the Memorial Sloan Kettering Cancer Center (MSKCC) nomogram for prediction of NSLN metastasis is useful in a German breast cancer population and whether the characteristics of the breast tumor and the sentinel lymph node (SLN) are able to predict the likelihood of non-sentinel lymph node (NSLN) metastasis. Methods A total of 545 patients with primary breast cancer and SLN examination were evaluated. The MSKCC nomogram was applied to 98 patients with a positive SLN who subsequently had completion axillary lymph node dissection (ALND). Predictive accuracy was assessed by calculating the area under the receiver-operator characteristic (ROC) curve. The collective was evaluated by correlating the prevalence of NSLN and SLN metastasis to pathological features. Results The MSKCC nomogram achieved a ROC of 0.58 indicating a bad accuracy of the nomogram. Tumor size, histology, lymphovascular infiltration, multifocality, Her-2-neu positivity, and nuclear grade correlated with the probability of SLN metastasis. Histology and primary tumor localization correlated significantly with the probability of NSLN metastasis. Conclusions The MSKCC nomogram did not provide a reliable predictive model in our study population. However, the likelihood of SLN metastasis correlated with the presumed risk factors and no obvious differences between the MSKCC population and our population could be seen. In order to achieve interinstitutional reproducibility, standardization of surgical procedure and of the pathological assessment of the SLN is desirable.     

Assessment of the performance of the Stanford Online Calculator for the prediction of nonsentinel lymph node metastasis in sentinel lymph node‐positive breast cancer patients

BACKGROUND:

Several models for the prediction of nonsentinel lymph node (NSLN) metastasis in sentinel lymph node (SLN)‐positive breast cancer patients have been proposed. In this study, the authors evaluate the Stanford Online Calculator (SOC), which was designed to predict the likelihood of NSLN metastasis using only 3 variables: primary tumor size, SLN metastasis size, and angiolymphatic invasion status. They compared it with the Mayo and Memorial Sloan‐Kettering Cancer Center (MSKCC) nomograms.

METHODS:

The SOC was used to calculate the probability of NSLN metastasis in 464 breast cancer patients with SLN metastasis who underwent completion axillary lymph node dissection at the Mayo Clinic. The area under the receiver operating characteristic curve (AUC) was calculated for each model. Mean probabilities of patients with and without NSLN metastasis were compared. Patients with ≤5%, ≤10%, and 100% NSLN metastasis probabilities were examined.

RESULTS:

The AUCs of the Stanford, MSKCC, and Mayo models were 0.72, 0.74, and 0.77, respectively (P = .13). The mean Stanford probabilities for patients with and without NSLN metastasis were 0.75 (range, 0.06‐1.0) and 0.50 (range, 0.05‐1.0), respectively (P < .0001). The false‐negative rates for patients with a Stanford probability of ≤5% and ≤10% were 0% and 13%, respectively. Of the patients with a Stanford probability of 100%, 26% did not have NSLN metastasis.

CONCLUSIONS:

Despite using only 3 variables, the Stanford nomogram appears to perform on a par with, but not better than, the MSKCC and Mayo nomograms. Further validation in other patient populations is needed. Cancer 2009. © 2009 American Cancer Society.     

Validation of Three Breast Cancer Nomograms and a New Formula for Predicting Non-sentinel Lymph Node Status

Background: The aim of the study was to evaluate the available breast nomograms (MSKCC, Stanford, Tenon)to predict non-sentinel lymph node metastasis (NSLNM) and to determine variables for NSLNM in SLN positivebreast cancer patients in our population. Materials and Methods: We retrospectively reviewed 170 patientswho underwent completion axillary lymph node dissection between Jul 2008 and Aug 2010 in our hospital. Wevalidated three nomograms (MSKCC, Stanford, Tenon). The likelihood of having positive NSLNM based onvarious factors was evaluated by use of univariate analysis. Stepwise multivariate analysis was applied to estimatea predictive model for NSLNM. Four factors were found to contribute significantly to the logistic regressionmodel, allowing design of a new formula to predict non-sentinel lymph node metastasis. The AUCs of the ROCswere used to describe the performance of the diagnostic value of MSKCC, Stanford, Tenon nomograms and ournew nomogram. Results: After stepwise multiple logistic regression analysis, multifocality, proportion of positiveSLN to total SLN, LVI, SLN extracapsular extention were found to be statistically significant. AUC results wereMSKCC: 0.713/Tenon: 0.671/Stanford: 0.534/DEU: 0.814. Conclusions: The MSKCC nomogram proved to bea good discriminator of NSLN metastasis in SLN positive BC patients for our population. Stanford and Tenonnomograms were not as predictive of NSLN metastasis. Our newly created formula was the best predictiontool for discriminate of NSLN metastasis in SLN positive BC patients for our population. We recommend thatnomograms be validated before use in specific populations, and more than one validated nomogram may beused together while consulting patients.     

Low performance of the MSKCC nomogram in preoperatively ultrasonically negative axillary lymph node in breast cancer patients

BACKGROUND AND OBJECTIVES: In order to predict the nonsentinel lymph node (NSLN) metastases in sentinel lymph node (SLN) positive patients a nomogram was created at the Memorial Sloan Kettering Cancer Centre (MSKCC). The aim of our study was to validate the MSKCC nomogram in patients grouped by the preoperative ultrasound (US) examination of the axillary lymph nodes. METHODS: The MSKCC nomogram was validated separately in three groups of patients: (US-0) only clinically preoperatively negative axillary lymph nodes (126 patients), (US-1) US negative axillary lymph nodes (109 patients), and (US-2) US suspicious but fine needle aspiration biopsy (FNAB) negative axillary lymph nodes (41 patients). RESULTS: The predicted probability underestimates the actual probability with the mean absolute error equal to 0.116 in the US-0 group (P = 0.003), and overestimates the actual probability (mean absolute error equal to 0.084) in US-1 group (P = 0.033) and US-2 group (mean absolute error is 0.110) (P = 0.275). CONCLUSION: We found that the MSKCC nomogram overestimates the probability of the NSLN metastases in breast cancer patients with (i) preoperatively US negative or (ii) US suspicious, but FNAB negative axillary lymph nodes. We also found that MSKCC nomogram has only limited value in patients with only clinically negative axillary lymph nodes.     

Predicting Non-Sentinel Lymph Node Status After Positive Sentinel Biopsy in Breast Cancer: What Model Performs the Best in a Czech Population?

Several models have previously been proposed to predict the probability of non-sentinel lymph node (NSLN) metastases after a positive sentinel lymph node (SLN) biopsy in breast cancer. The aim of this study was to assess the accuracy of two previously published nomograms (MSKCC, Stanford) and to develop an alternative model with the best predictive accuracy in a Czech population. In the basic population of 330 SLN-positive patients from the Czech Republic, the accuracy of the MSKCC and the Stanford nomograms was tested by the area under the receiver operating characteristics curve (AUC). A new model (MOU nomogram) was proposed according to the results of multivariate analysis of relevant clinicopathologic variables. The new model was validated in an independent test population from Hungary (383 patients). In the basic population, six of 27 patients with isolated tumor cells (ITC) in the SLN harbored additional NSLN metastases. The AUCs of the MSKCC and Stanford nomograms were 0.68 and 0.66, respectively; for the MOU nomogram it reached 0.76. In the test population, the AUC of the MOU nomogram was similar to that of the basic population (0.74). The presence of only ITC in SLN does not preclude further nodal involvement. Additional variables are beneficial when considering the probability of NSLN metastases. In the basic population, the previously published nomograms (MSKCC and Stanford) showed only limited accuracy. The developed MOU nomogram proved more suitable for the basic population, such as for another independent population from a mid-European country.     

乳腺癌患者胸肌间淋巴结转移的影响因素及手术清扫

  目的   分析乳腺癌患者胸肌间淋巴结（IPNs）的检出率、转移率及其影响因素,探讨胸肌间淋巴结清扫的意义和指征。   方法   回顾性分析1 673例接受乳腺癌改良根治术并且胸肌间淋巴结单独送病理检查患者的病理临床资料,记录IPNs的检出率和转移情况,分析IPNs转移与肿瘤大小、腋窝淋巴结、临床分期、新辅助化疗、激素受体、Her-2表达以及乳腺癌分子亚型的关系。   结果   本组病例IPNs检出率、IPNs总体转移率、腋窝淋巴结阳性者IPNs转移率分别为13.39%、4.3%和10.01%。IPNs转移率与腋窝淋巴结转移、肿瘤TNM分期之间具有显著相关性（P < 0.05）,但与激素受体状况、Her-2表达以及乳腺癌分子亚型之间未见相关（P>0.05）;新辅助化疗并未降低肿瘤局部偏晚患者的IPNs转移率;IPNs转移者表现为肿瘤较大、腋窝淋巴结转移多、TNM分期较晚。   结论   IPNs转移多见于肿瘤直径较大、腋窝淋巴结转移、TNM分期较晚、局部晚期以及适合新辅助化疗的乳腺癌患者,这些指征可能意味着需要常规进行IPNs的手术清扫和单独送检。      

MSCT对T1和T2期非小细胞肺癌患者纵隔淋巴结转移的诊断意义

  目的   评估胸部多层CT扫描（MSCT）对T₁和T₂期非小细胞肺癌（NSCLC）纵隔淋巴结转移的指导意义。   方法   选择2004年3月至2012年3月T₁和T₂期NSCLC患者32例，依据病理结果分析术前MSCT对纵隔淋巴结的判断。   结果   以淋巴结短径≥10 mm MSCT评价纵隔淋巴转移的敏感性和特异性分别为82.4%和92.4%；淋巴结大小、原发肿瘤位置及脏胸膜侵犯对纵隔淋巴转移的预测差异均有统计学意义（P < 0.05）。   结论   淋巴结大小可作为评估NSCLC患者纵膈淋巴结转移的依据，原发于右肺的肿瘤及肿瘤伴有脏层胸膜侵犯具有较高的纵隔淋巴结转移风险。      

治疗前哨淋巴结阳性乳腺癌非前哨淋巴结状态的多因素分析

目的探讨影响前哨淋巴结阳性乳腺癌非前哨淋巴结状态的因素,建立判断有否转移的预测模型。方法回顾性分析我院自2003年-2010年共285例前哨淋巴结阳性乳腺癌患者临床病理资料。采用Logistic回归方法分析13种影响前哨淋巴结阳性乳腺癌非前哨淋巴结状态的因素,建立判断有否转移的预测模型,并验证模型的准确度、敏感度、特异性。结果单因素Logistic回归分析结果提示,有6个因素与NSLN转移具有密切相关性,分别为肿瘤大小（OR=1.45,P<0.01）、阳性SLN大小（OR=2 078.49,P<0.01）、阳性SLN数量(OR=2.44,P<0.01)、阴性SLN数量(OR=0.19,P<0.01)、脉管侵犯(OR=11.45,P<0.01)、阳性SLN包膜外扩散(OR=74.34,P<0.01)。Logistic多因素回归分析表明：肿瘤大小、脉管侵犯、阴性SLN数量、阳性SLN大小及阳性SLN包膜外扩散与NSLN转移密切相关(P<0.05)。Logistic回归模型预测前哨淋巴结阳性乳腺癌非前哨淋巴结状态的敏感度为 92.62%（138/149）,特异性为 89.15%（115/129）,总符合率91.01% (253/278)。结论Logistic回归预测模型能较好的判断前哨淋巴结阳性乳腺癌非前哨淋巴结的状态,[JP2]有助于乳腺肿瘤外科医师选择最佳治疗方案。