Risk of Residual Axillary Lymph Node Macrometastasis in Early Breast Cancer PATIENTS with One Positive Macrometastasis Sentinel Lymph Node.

IF 2.7 4区 医学 Q3 BIOTECHNOLOGY & APPLIED MICROBIOLOGY
OncoTargets and therapy Pub Date : 2025-05-12 eCollection Date: 2025-01-01 DOI:10.2147/OTT.S506778
Dao-Yong Liu, Yun Zhu, Qiang Xie, Jun Deng, Bang-Ling Chen
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引用次数: 0

Abstract

Objective: To investigate the risk factors for residual axillary lymph node macro-metastasis in early-stage breast cancer patients with a single macrometastasis sentinel lymph node (SLN).

Methods: We retrospectively analyzed the clinical data of 119 breast cancer patients diagnosed between January 2018 and September 2023, each with one positive SLN stained with methylene blue, who subsequently underwent axillary lymph node dissection. The patients were divided into two groups based on the total number of SLNs identified: fewer than three and more than three. Fisher's exact test was used for statistical analysis between groups.

Results: Among the 119 patients evaluated, 30 patients had a total of 2 sentinel lymph nodes, with 15 testing positive for residual axillary lymph nodes, yielding a positivity rate of 50.0%. Another 30 patients had 3 sentinel lymph nodes, with a positivity rate of 33.3%. An additional 32 patients each had 4 sentinel lymph nodes, with a positivity rate of 3.13%. Finally, 27 patients had 5 sentinel lymph nodes, with a 0% positivity rate. The positivity rate of axillary lymph nodes was significantly higher in the group with ≤ 3 sentinel lymph nodes (less SLN group) compared to the group with > 4 sentinel lymph nodes (more SLN group). Binary logistic regression analysis confirmed that the number of SLNs was the only significant predictor of residual lymph node macrometastasis.

Conclusion: The number of sentinel lymph nodes (SLNs) is a key factor influencing the risk of residual axillary lymph node macrometastasis in early-stage breast cancer patients with one positive SLN. Identifying a higher number of SLNs (≥4) significantly lowers the risk of residual metastasis, supporting the use of thorough SLN mapping in these cases to improve patient outcomes.

一个前哨淋巴结阳性的早期乳腺癌患者腋窝淋巴结残余大转移的风险。
目的:探讨单发大转移前哨淋巴结(SLN)的早期乳腺癌患者腋窝残余淋巴结大转移的危险因素。方法:回顾性分析2018年1月至2023年9月诊断的119例乳腺癌患者的临床资料,每例患者均有1例亚甲基蓝染色的SLN阳性,随后行腋窝淋巴结清扫术。根据确定的sln总数将患者分为两组:少于3组和多于3组。组间统计分析采用Fisher精确检验。结果:119例患者中,30例患者共有2个前哨淋巴结,其中腋窝淋巴结残留阳性15例,阳性率为50.0%。3个前哨淋巴结30例,阳性率33.3%。另有32例前哨淋巴结4个,阳性率为3.13%。27例有5个前哨淋巴结,阳性率为0%。前哨淋巴结≤3个组(SLN较少组)腋窝淋巴结阳性率明显高于前哨淋巴结≤4个组(SLN较多组)。二元logistic回归分析证实,sln的数量是残余淋巴结大转移的唯一显著预测因子。结论:前哨淋巴结(SLN)数目是影响早期乳腺癌伴1个阳性腋窝淋巴结残留大转移的关键因素。识别更多的SLN(≥4)显著降低残留转移的风险,支持在这些病例中使用彻底的SLN定位来改善患者的预后。
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来源期刊
OncoTargets and therapy
OncoTargets and therapy BIOTECHNOLOGY & APPLIED MICROBIOLOGY-ONCOLOGY
CiteScore
9.70
自引率
0.00%
发文量
221
审稿时长
1 months
期刊介绍: OncoTargets and Therapy is an international, peer-reviewed journal focusing on molecular aspects of cancer research, that is, the molecular diagnosis of and targeted molecular or precision therapy for all types of cancer. The journal is characterized by the rapid reporting of high-quality original research, basic science, reviews and evaluations, expert opinion and commentary that shed novel insight on a cancer or cancer subtype. Specific topics covered by the journal include: -Novel therapeutic targets and innovative agents -Novel therapeutic regimens for improved benefit and/or decreased side effects -Early stage clinical trials Further considerations when submitting to OncoTargets and Therapy: -Studies containing in vivo animal model data will be considered favorably. -Tissue microarray analyses will not be considered except in cases where they are supported by comprehensive biological studies involving multiple cell lines. -Biomarker association studies will be considered only when validated by comprehensive in vitro data and analysis of human tissue samples. -Studies utilizing publicly available data (e.g. GWAS/TCGA/GEO etc.) should add to the body of knowledge about a specific disease or relevant phenotype and must be validated using the authors’ own data through replication in an independent sample set and functional follow-up. -Bioinformatics studies must be validated using the authors’ own data through replication in an independent sample set and functional follow-up. -Single nucleotide polymorphism (SNP) studies will not be considered.
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