102例前哨淋巴结阳性乳腺癌患者腋窝非前哨淋巴结转移相关因素分析

目的:回顾性研究我中心前哨淋巴结(sentinel lymph node,SLN)阳性并续行腋窝淋巴结清扫术(axillary lymph node dissection,ALND)早期乳腺癌患者的临床病理资料,分析腋窝非前哨淋巴结(non-sentinel lymph node,NSLN)转移的相关危险因素,为建立符合本地区的预测模型提供依据。方法:收集温州市人民医院2009年1月—2016年12月102例前哨淋巴结活检阳性并进一步接受腋窝淋巴结清扫术的早期乳腺癌患者临床及病理资料,采用单因素分析及多因素Logistic回归分析方法研究这些临床病理因素与NSLN转移的关系。结果:本研究中共有102例SLN阳性乳腺癌患者进一步接受了ALND,其中36例NSLN发现有转移,NSLN转移检出率是35.3%(36/102)。根据单因素分析结果显示:组织学分级(χ~2=8.8214,P=0.0030)、SLN转移率≥0.5(χ~2=5.2377,P=0.0221)、SLN转移灶最大径 2 mm (χ~2=4.3290,P=0.0370)是NSLN转移的危险因素。多因素Logistic回归分析结果显示:SLN转移率≥0.5(OR=1.63,95%CI:1.29-2.10,P=0.001)、SLN转移灶最大径 2 mm(OR=1.34,95%CI:1.02-2.12,P=0.032)是NSLN转移的独立预测因素。结论:SLN转移率≥0.5、SLN转移灶最大径 2 mm是预测乳腺癌NSLN转移的危险因素,可以作为预测因素,进一步用来构建符合本地区的预测模型。     

前哨淋巴结阳性乳腺癌患者非前哨淋巴结转移影响因素分析

目的探讨前哨淋巴结(sentinel lymph node, SLN)阳性乳腺癌患者非前哨淋巴结(non-sentinel lymph node, NSLN)转移的影响因素。方法回顾性分析2015年1月～2018年6月我科行SLNB提示SLN阳性并行腋窝淋巴结清扫(axillary lymph node dissection, ALND)的女性乳腺癌患者69例,其中NSLN阴性33例(47.8%),阳性36例(52.2%)。采用单因素和多因素Logistic分析NSLN转移的影响因素。结果单因素分析结果显示:乳腺癌患者NSLN转移与SLN总数(P=0.021)和SLN+/总SLN(P=0.003)有关,与患者年龄(P=0.805)、月经状态(P=0.627)、肿块大小(P=0.110)、是否有脉管癌栓(P=0.088)、SLN转移数目(P=0.102)以及ER(P=0.847)、PR(P=0.453)、HER2(P=0.071)、Ki67(P=0.623)不相关(P0.05);多因素Logistic回归分析显示,SLN阳性比例(SLN+/总SLN)为乳腺癌NSLN转移的独立危险因素(P=0.005)。结论 SLN阳性的乳腺浸润性导管癌患者存在较高的NSLN阳性风险,SLN阳性比例为影响NSLN转移的独立危险因素。SLN+/SLN比值≥0.5时,NSLN转移率显著增高,在免除ALND时应慎重考虑。     

前哨淋巴结1～2枚转移乳腺癌病人非前哨淋巴结转移的预测

目的 :探讨前哨淋巴结(SLN)活检1~2枚转移乳腺癌病人非前哨淋巴结(NSLN)转移的预测因素。方法 :收集2011年1月至2017年6月本院乳腺疾病诊治中心cT_(1~2)N_0女性乳腺癌病例,SLN活检提示SLN 1~2枚转移,进一步行腋窝淋巴结清扫(ALND)共263例。回顾性分析NSLN转移的预测因素。结果:单因素分析结果显示,肿块大小(P=0.024)、脉管癌栓(P=0.038)、SLN阳性个数/与SLN活检个数比值(SLN+/SLN,P0.001)和术前超声ALN异常状态(P=0.020)是NSLN转移的预测因素。多因素Logistic回归分析结果显示,肿块最大径2 cm(OR=1.97,95%CI:1.08~3.60,P=0.028)、SLN+/SLN比值≥0.5(OR=3.00,95%CI:1.65~5.48,P0.001),术前超声ALN异常(OR=1.93,95%CI:1.03~3.63,P=0.041)是NSLN转移的独立预测因素。具有0、1、2或3项独立预测因素的病人,NSLN转移率分别为10.1%、21.3%、36.5%和58.3%。结论:对于cT_(1~2)N_0、SLN 1~2枚转移的乳腺癌病人,肿块大小、SLN+/SLN比值和术前超声ALN异常状态是NSLN转移的独立预测因素。同时具有≥2项独立预测因素的病人,NSLN转移率较高,在免除ALND时应慎重考虑。     

早期乳腺癌患者1～2枚前哨淋巴结阳性时非前哨淋巴结转移的相关危险因素分析

目的 探究1～2枚前哨淋巴结（sentinel lymph node,SLN）阳性的早期乳腺癌患者非前哨淋巴结（non-sentinel lymph node,NSLN）转移的相关因素,寻找部分SLN阳性患者豁免腋窝淋巴结清扫的依据。方法选取2019年1月至2023年4月期间于西南医科大学附属医院就诊的SLN活检阳性并行腋窝淋巴结清扫术的早期乳腺癌患者299例,采用单因素分析其临床病理资料,多因素logistic回归分析SLN阳性的早期乳腺癌患者腋窝NSLN转移的相关因素;采用GraphPad Prim 9.0绘制受试者工作特征（receiver operating characteristic,ROC）曲线,并计算ROC曲线下面积（area under curve,AUC）评估风险因素的预测价值。结果 299例1～2枚SLN阳性的乳腺癌患者中,101例（33.78%） NSLN阳性,198例（66.22%）NSLN阴性。单因素分析结果显示,患者SLN阳性数目、临床T分期及淋巴脉管侵犯与NSLN转移相关（P<0.001）。多因素logistic回归分析结果显示,SLN阳性数为2枚...     

前哨淋巴结1～2个阳性乳腺癌患者腋窝非前哨淋巴结转移情况及危险因素分析

目的 探讨前哨淋巴结（sentinal lymph node,SLN）1～2个阳性乳腺癌患者腋窝非前哨淋巴结（non-sentinel lymph node metastasis,NSLN）转移情况和危险因素，为该类患者豁免腋窝淋巴结清扫（axillary lymph node dissection,ALND）的可行性提供理论依据。方法 回顾性分析铜陵市人民医院甲状腺和乳腺外科2018年1月至2023年4月期间收治的确诊为乳腺癌患者行前哨淋巴结活检（sentinel lymph node biopsy,SLNB）证实有1～2个SLN阳性且行规范化ALND的54例患者的临床病理资料，根据NSLN是否有转移分为NSLN转移组（17例）和NSLN非转移组（37例），采用卡方检验比较2组患者的基本情况和临床病理特征，采用多因素二元logistic回归模型分析筛选出腋窝NSLN发生转移的独立危险因素，并采用受试者工作特征（receiver operating characteristic,ROC）曲线评估独立危险因素联合预测腋窝NSLN转移的预测价值。结果 SLN有1～2个转移者有54例，腋窝N...     

乳腺癌前哨淋巴结活检的初步体会

目的 初步探讨乳腺癌前哨淋巴结（sentinel lymph node,SLN)检测的可行性,SLN预测腋窝淋巴结状态的可靠性。方法 对我院普外科收治的15例T1-2期、腋窝未扪及肿大淋巴结的乳腺癌病人,使用^99mTc右旋糖酐或^99mTc硫胶体为示踪剂,γ探测仪探测SLN,然后进行活检和腋窝淋巴结清扫（axillary lymph node dissection,ALND)。两处标本同时送病理检查,以此来评价SLN的病理组织学结果能否准确地反映腋窝淋巴结状态。结果 在10例病人中发现SLN,发现率为66.6%(10/15),SLN的数量为1-3枚/例,平均2.1枚/例,非前哨淋巴结（non-sentinel lymph node,NSLN)5-16枚/例,平均10.3枚/例,发现SLN的10例病人中1例（10%）SLN有癌转移,其他腋窝淋巴结未见转移;2例SLN未发现癌转移而NSLN有癌转移,假阴性率20%（2/10）,准确性80%（8/10）,结论 乳腺癌前哨淋巴结定位和活检技术以及预测腋窝淋巴结状态的可靠性方面有待进一步积累经验,提高准确性,降低假阴性率。     

前哨淋巴结阳性乳腺癌非前哨淋巴结转移的情况分析

目的探讨前哨淋巴结（SLN）阳性乳腺癌患者的临床病理特征与非前哨淋巴结（NSLN）转移的关系。 方法回顾性分析2010年1月至2016年1月中山大学附属第一医院500例行前哨淋巴结活检（SLNB）的临床分期为T_1-2N₀M₀期乳腺癌患者资料,其中病理检查确诊SLN阳性、随后行腋窝淋巴结清扫（ALND）的乳腺癌患者共89例,总结其临床、病理因素的特征及其对腋窝NSLN转移的影响因素进行单因素及多因素Logistic分析。 结果SLN阳性率为17.8%（89/500）,49.4%（44/89）出现NSLN转移。单因素分析显示,NSLN转移与原发肿瘤分期、脉管浸润、SLN阳性数、SLN阳性率相关（χ²=4.062、36.084、7.003、10.889,P=0.044、<0.001、0.030、0.004）。进一步多因素Logistic回归分析显示,脉管浸润、SLN阳性率是NSLN转移的独立预测因子（OR=46.142,95%CI：11.821~258.472,P<0.000 1;OR=10.482,95%CI：2.564~51.312,P=0.002）。 结论SLN阳性的乳腺癌患者,其原发肿瘤分期、肿瘤是否多发、脉管浸润、SLN阳性数、SLN转移率与腋窝NSLN转移相关。其中,脉管浸润及SLN阳性率≥0.5是SLN阳性乳腺癌患者腋窝NSLN转移的独立预测因子。     

前哨淋巴结宏转移的早期乳腺癌非前哨淋巴结转移危险因素分析

目的:探讨前哨淋巴结(SLN)宏转移的早期乳腺癌非前哨淋巴结(NSLN)转移的危险因素。方法:回顾性收集2014年1月—2016年12月诊治的196例SLN宏转移临床早期乳腺癌患者临床资料,分析各临床病理指标与NSLN转移的关系。结果:196例患者中,NSLN转移患者53例(25.5%)。单因素分析显示NSLN转移与年龄、月经状况、原发肿瘤位置、组织学分级、脉管瘤栓、Ki-67表达、HER-2表达、免疫组化分型无明显关系(均P0.05),而与原发肿瘤大小、阳性SLN个数明显有关(均P0.05)。多因素回归分析显示阳性SLN个数是NSLN转移的独立危险因素(P=0.000,OR=2.355)。结论:原发肿瘤大小、阳性SLN个数是SLN宏转移的临床早期乳腺癌患者NSLN转移重要因素,对于原发肿瘤2cm,尤其阳性SLN数超过2枚的患者,建议行腋窝淋巴结清扫。     

乳腺癌非前哨淋巴结转移的危险因素研究

目的探讨腋窝前哨淋巴结(SLN)阳性的乳腺癌患者非前哨淋巴结(NSLN)转移的影响因素,并对美国纪念斯隆-凯特琳癌症中心(MSKCC)预测模型的临床应用价值进行验证。方法收集山西白求恩医院乳腺外科2012年3月至2019年9月142例SLN阳性临床早期乳腺癌,Logistic回归分析NSLN阳性组和阴性组间各临床病理指标与NSLN转移的关系,使用MSKCC预测模型计算每例患者腋窝NSLN转移风险,利用校正曲线和受试者操作特性曲线(ROC)下面积(AUC)评估该模型预测的准确性。结果142例患者中,NSLN转移患者仅54例(38%)。单因素Logistic分析NSLN转移可能的影响因素包括:年龄>60岁组NSLN转移低于≤40岁组[比值比(OR)0.242,95%可信区间(CI)0.082~1.720,P<0.05],差异有统计学意义、肿块>2 cm组NSLN转移高于肿块≤2 cm组(OR 2.062,95%CI 1.036~1.126,P<0.05),差异有统计学意义、脉管浸润组NSLN转移高于无脉管浸润组(OR 2.242,95%CI 1.126~1.482,P<0.05),差异有统计学意义、SLN转移2个组NSLN转移高于SLN阴性组(OR 0.332,95%CI 0.120~1.957,P<0.05),差异有统计学意义、SLN转移≥3个组NSLN转移高于SLN阴性组(OR 0.342,95%CI 0.132~1.902,P<0.05),差异有统计学意义。多因素logistic回归分析示年龄41~60岁组NSLN转移低于≤40岁组(OR 0.324,95%CI 0.127~0.827,P<0.05),差异有统计学意义、年龄>60岁组NSLN转移低于≤40岁组(OR 0.178,95%CI 0.055~0.572,P<0.05),差异有统计学意义、脉管浸润组NSLN转移高于无脉管浸润组(OR 2.713,95%CI 1.254~5.873,P<0.05),差异有统计学意义,以及SLN转移组NSLN转移高于非SLN转移组(OR 1.022,95%CI 1.009~1.036,P<0.05),差异有统计学意义,三者均是NSLN转移的独立预测因素,并对137例SLN阳性的中国乳腺癌患者验证MSKCC模型的可行性(AUC=0.695,95%CI 0.599~0.791)。结论年龄、脉管浸润以及SLN转移率可作为预测早期乳腺癌NSLN转移的重要指标。     

1～2枚前哨淋巴结阳性乳腺癌患者中非前哨淋巴结转移的影响因素分析

目的分析1~2枚前哨淋巴结(SLN)阳性乳腺癌患者的临床病理因素与非前哨淋巴结(nSLN)转移的关系。方法回顾性分析2012年1月至2015年12月期间广州医科大学附属第二医院乳腺外科1~2枚SLN阳性且同时行腋窝淋巴结清扫的118例早期浸润性乳腺癌患者临床病理资料,分别应用x~2检验及Logistic回归进行单因素及多因素分析。结果 nSLN未转移(nSLN-)组患者64例,nSLN转移(nSLN+)组患者54例。单因素分析显示nSLN转移与肿瘤大小、脉管癌栓、SLN转移比例有关(P=0.001,P=0.030,P=0.002),但与年龄、绝经状态、病理类型、肿瘤位置、核分级、SLN转移数目、ER、PR、C-erBb-2、Ki67无关(均P0.05)。多因素分析显示肿瘤大小、脉管癌栓、SLN转移比例均为nSLN转移的独立影响因素(OR分别为3.159,2.425,2.258,均P0.05)。结论肿瘤大小≥2 cm、脉管癌栓、SLN转移比例≥2、3均为1~2枚SLN阳性乳腺癌患者nSLN转移的独立不良影响因素。     

Total tumor load assessed by one-step nucleic acid amplification assay as an intraoperative predictor for non-sentinel lymph node metastasis in breast cancer

BackgroundThis study aimed to determine the relationship between CK19 mRNA copy number in sentinel lymph nodes (SLN) assessed by one-step nucleic acid amplification (OSNA) technique, and non-sentinel lymph nodes (NSLN) metastization in invasive breast cancer. A model using total tumor load (TTL) obtained by OSNA technique was also constructed to evaluate its predictability.MethodsWe conducted an observational retrospective study including 598 patients with clinically T1-T3 and node negative invasive breast cancer. Of the 88 patients with positive SLN, 58 patients fulfill the inclusion criteria.ResultsIn the analyzed group 25.86% had at least one positive NSLN in axillary lymph node dissection. Univariate analysis showed that tumor size, TTL and number of SLN macrometastases were predictive factors for NSLN metastases. In multivariate analysis just the TTL was predictive for positive NSLN (OR 2.67; 95% CI 1.06–6.70; P = 0.036). The ROC curve for the model using TTL alone was obtained and an AUC of 0.805 (95% CI 0.69–0.92) was achieved. For TTL >1.9 × 10⁵ copies/μL we got 73.3% sensitivity, 74.4% specificity and 88.9% negative predictive value to predict NSLN metastases.ConclusionWhen using OSNA technique to evaluate SLN, NSLN metastases can be predicted intraoperatively. This prediction tool could help in decision for axillary lymph node dissection.     

乳腺癌非前哨淋巴结转移的预测模型构建

目的探讨乳腺癌非前哨淋巴结(NSLN)转移的危险因素并构建非前哨淋巴结转移的预测模型。 方法回顾性分析2016年1月至2019年6月接受前哨淋巴结活检(SLNB)且确诊前哨淋巴结(SLN)阳性,行腋窝淋巴清扫术(ALND)的95例乳腺癌患者的临床资料。应用SPSS 20.0软件对数据进行处理,计数资料用[例(%)]描述,其中连续变量使用秩和检验,分类变量使用χ²检验。对与NSLN转移相关的临床病理因素进行多因素Logistic回归分析,根据Logistic回归分析各变量的回归系数建立非前哨淋巴结转移风险预测模型计算每例患者NSLN转移的预测概率,通过描绘受试者工作特征曲线(ROC)并计算曲线下面积(AUC)从而来评估模型的预测能力。 结果通过单因素分析显示乳腺癌患者NSLN转移与肿瘤大小、肿瘤位置、淋巴血管是否受侵犯、SLN转移灶大小及SLN阳性率相关(P<0.05);将相关因素纳入多因素Logistic回归分析中,结果显示,肿瘤大小、淋巴血管是否受侵犯、SLN阳性率为乳腺癌患者NSLN转移的独立危险因素。根据Logistic回归分析建立NSLN转移风险预测模型,绘制研究对象的ROC曲线,计算AUC为0.792(95%CI为0.651～0.934);Hosmer-Lemeshow拟合优度检验P＝0.603。 结论肿瘤大小、淋巴血管是否受侵犯、SLN阳性率为乳腺癌患者非前哨淋巴结转移的独立危险因素,NSLN转移风险预测模型对NSLN转移患者具有较高的预测价值,可辅助临床医师术前判断,选择合理的术式。     

Predicting residual lymph node basin disease in melanoma patients with sentinel lymph node metastases

BACKGROUND: The incidence of residual occult disease in nonsentinel lymph nodes (NSLN) after a positive sentinel lymph node (SLN) biopsy in patients with melanoma is relatively low. The purpose of this study is to identify factors that may be predictive of occult NSLN metastases after positive SLN biopsy. METHODS: Fifty-six consecutive melanoma patients with positive sentinel nodes who subsequently underwent complete lymph node dissection (CLND) were evaluated. RESULTS: Only the number of positive SLN predicted the status on NSLN by univariate (P = 0.008) and multivariate (P = 0.028) analyses. None of the other variables (characteristics of SLN metastases, number of draining nodal basins, age, sex, thickness, Clark level, ulceration, number of mitoses/mm(2), histological subtype, and location of the primary) significantly predicted CLND results. CONCLUSIONS: Identifying patients with residual lymph node basin disease remains difficult. Thus, lymph node dissection should be performed in all patients after positive sentinel node biopsy.     

Prediction of non-sentinel lymph node involvement in breast cancer patients with a positive sentinel lymph node

Completion axillary lymph node dissection (cALND) is the golden standard if breast cancer involves the sentinel lymph node (SLN). However, most non-sentinel lymph nodes (NSLN) are not involved, cALND has a considerable complication rate and does not improve outcome. We here present and validate our predictive model for positive NSLNs in the cALND if the SLN is positive.Consecutive early breast cancer patients from one center undergoing cALND for a positive SLN were included. We assessed demographic and clinicopathological variables for NSLN involvement. Uni- and multivariate analysis was performed. A predictive model was built and validated in two external centers.21.9% of 470 patients had at least one involved NSLN. In univariate analysis, seven variables were significantly correlated with NSLN involvement: tumor size, grade, lymphovascular invasion (LVI), number of positive and negative SLNs, size of SLN metastasis and intraoperative positive SLN. In multivariate analysis, LVI, number of negative SLNs, size of SLN metastasis and intraoperative positive pathological evaluation were independent predictors for NSLN involvement. The calculated risk resulted in an AUC of 0.76. Applied to the external data, the model was accurate and discriminating for one (AUC = 0.75) and less for the other center (AUC = 0.58).A discriminative predictive model was constructed to calculate the risk of NSLN involvement in case of a positive SLN. External validation of our model reveals differences in performance when applied to data from other institutions concluding that such a predictive model requires validation prior to use.     

乳腺癌非前哨淋巴结转移的相关危险因素分析

目的：探讨前哨淋巴结活检（SLNB）阳性乳腺癌患者非前哨淋巴结（NSLN）转移的危险因素。 方法：收集2009年7月—2013年10月新疆医科大学附属肿瘤医院收治的138例SLNB阳性的乳腺癌患者临床资料,采用单因素及多因素Logistic回归分析方法研究各项临床病理因素与NSLN转移的关系。 结果：单因素分析显示,原发肿瘤直径、组织学分级、前哨淋巴结转移率、前哨淋巴结转移灶最大径及脉管浸润与NSLN转移有关（均P<0.05）;多因素Logistic回归分析发现,原发肿瘤直径（OR=2.263,P=0.005）、前哨淋巴结转移率（OR=1.919,P=0.002）、前哨淋巴结转移灶最大径（OR=8.479,P=0.000）、脉管浸润（OR=4.518,P=0.029）是NSLN转移的独立危险因素。 结论：原发肿瘤直径、前哨淋巴结转移率、前哨淋巴结转移灶最大径及脉管浸润可作为预测乳腺癌NSLN转移的独立性指标。     

Predictors of nonsentinel lymph node positivity in patients with a positive sentinel node for melanoma

BACKGROUND: Patients found to harbor melanoma micrometastases in the sentinel lymph node (SLN) are recommended to proceed to complete lymph node dissection (CLND), although the majority of patients will have no additional disease identified in the nonsentinel lymph nodes (NSLNs). We sought to assess predictive factors associated with finding positive NSLNs, and identify a subset of patients with low likelihood of finding additional disease on CLND. STUDY DESIGN: We queried our prospective melanoma database for patients from January 1996 to August 2003 with a positive SLN. Univariable logistic regression models were fit for multiple factors and a positive NSLN. To derive a probabilistic model for occurrence of one or more positive NSLN(s), a multivariable logistic model was fit using a stepwise variable selection method. RESULTS: Of 980 patients who underwent SLN biopsy for cutaneous melanoma, 232 (24%) had a positive SLN; 221 (23%) followed by CLND. Of these patients, 34 (15%) had one or more positive NSLN(s). In multivariable analysis, male gender (odds ratio [OR] 3.6 [95% CI 1.33, 9.71]; p = 0.01), Breslow thickness (OR 4.58 [95% CI 1.28, 16.36]; p = 0.019), extranodal extension (OR 3.2 [95% CI 1.0, 10.5]; p = 0.05), and three or more positive sentinel nodes (OR 65.81 [95% CI 5.2, 825.7]; p = 0.001) were all associated with the likelihood of finding additional positive nodes on CLND. Of 47 patients with minimal tumor burden in the SLN, only 1 (2%) had additional disease in the NSLN. CONCLUSIONS: These results provide additional data to plan clinical trials to answer the question of who can safely avoid CLND after a positive SLN. Patients with minimal tumor burden in the SLN might be the most likely group, although defining "minimal tumor burden" must be standardized. Serial sectioning and immunohistochemistry on the NSLN in any "low-risk" group must be performed in a clinical trial to confirm that residual disease is unlikely before avoiding CLND can be recommended.     

原发乳腺癌前哨淋巴结阳性患者非前哨淋巴结转移的预测因子

【摘要】〓目的〓分析前哨淋巴结(SLN)阳性原发乳腺癌患者腋窝非前哨淋巴结(NSLN)转移的预测因子。方法〓回顾性分析212例SLN阳性并接受腋窝淋巴结清扫术的患者资料。获取的SLN均按示踪剂浓度排序并测量浸润灶大小。对各种临床及病理组织学因素数据进行单因素分析,纳入单因素分析有意义(P<0.05)的预测因素进行logistic多因素分析。结果〓多因素分析提示NSLN转移与SLN转移率(P<0.001)、SLN阴性个数(P=0.02)、染色剂浓度最小SLN转移(P=0.02)、SLN最大转移灶大小(P<0.001)有关。全部SLN转移者NSLN转移可能性较非全部SLN转移者大;随SLN阴性个数增加,NSLN转移率下降;染色剂浓度越大SLN转移机会越大,浓度最小SLN转移者较浓度最小SLN无转移者SLN ,其NSLN转移几率更高;最大转移灶>2 mm者出现NSLN转移可能性比SLN最大转移灶≤2 mm者大。结论〓SLN转移率(P<0.001)、SLN阴性个数(P=0.02)、染色剂浓度最小SLN转移(P=0.02)、SLN最大转移灶大小(P<0.001)是NSLN转移的独立预测因子。