Efficacy and safety comparison between axillary lymph node dissection with no axillary surgery in patients with sentinel node-positive breast cancer: a systematic review and meta-analysis

Yang, Fan; Li, Jincheng; Zhu, Demiao; Zhu, Hailong; Zhao, Yang; Zhu, Xin-Bing; Wu, Gang; Bai, Tingting

doi:10.1186/s12893-023-02101-8

Cited by 4 publications

(1 citation statement)

References 57 publications

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“…In clinical practice, axillary node dissection is typically recommended for effective local treatment of patients with positive sentinel nodes 44 . Nevertheless, since over 40% of patients with positive sentinel nodes have no other nodal metastases, predicting non-sentinel node involvement has been conducive to avoiding unnecessary axillary node dissection 45 . Consequently, the prediction of non-sentinel node involvement associated with the presence of LVI might allow for appropriate treatment (sentinel node biopsy or axillary node dissection) of patients.…”

Section: Discussionmentioning

confidence: 99%

Predictive value of MRI-based deep learning model for lymphovascular invasion status in node-negative invasive breast cancer

Liang,

Li,

Yao

et al. 2024

Sci Rep

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To retrospectively assess the effectiveness of deep learning (DL) model, based on breast magnetic resonance imaging (MRI), in predicting preoperative lymphovascular invasion (LVI) status in patients diagnosed with invasive breast cancer who have negative axillary lymph nodes (LNs). Data was gathered from 280 patients, including 148 with LVI-positive and 141 with LVI-negative lesions. These patients had undergone preoperative breast MRI and were histopathologically confirmed to have invasive breast cancer without axillary LN metastasis. The cohort was randomly split into training and validation groups in a 7:3 ratio. Radiomics features for each lesion were extracted from the first post-contrast dynamic contrast-enhanced (DCE)-MRI. The Least Absolute Shrinkage and Selection Operator (LASSO) regression method and logistic regression analyses were employed to identify significant radiomic features and clinicoradiological variables. These models were established using four machine learning (ML) algorithms and one DL algorithm. The predictive performance of the models (radiomics, clinicoradiological, and combination) was assessed through discrimination and compared using the DeLong test. Four clinicoradiological parameters and 10 radiomic features were selected by LASSO for model development. The Multilayer Perceptron (MLP) model, constructed using both radiomic and clinicoradiological features, demonstrated excellent performance in predicting LVI, achieving a high area under the curve (AUC) of 0.835 for validation. The DL model (MLP-radiomic) achieved the highest accuracy (AUC = 0.896), followed by DL model (MLP-combination) with an AUC of 0.835. Both DL models were significantly superior to the ML model (RF-clinical) with an AUC of 0.720. The DL model (MLP), which integrates radiomic features with clinicoradiological information, effectively aids in the preoperative determination of LVI status in patients with invasive breast cancer and negative axillary LNs. This is beneficial for making informed clinical decisions.

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Section: Discussionmentioning

confidence: 99%

Predictive value of MRI-based deep learning model for lymphovascular invasion status in node-negative invasive breast cancer

Liang,

Li,

Yao

et al. 2024

Sci Rep

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Omitting axillary lymph node dissection is associated with an increased risk of regional recurrence in early stage breast cancer: a systematic review and meta-analysis of randomized clinical trials

Cardoso,

de Lara,

Sobreira

et al. 2024

Clinical Breast Cancer

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Prediction of the number of positive axillary lymph nodes according to sentinel lymph node involvement and biological subtypes in patients receiving neoadjuvant chemotherapy

Yilmaz,

Kus,

Aytekin

et al. 2024

BMC Surg

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Background Sentinel lymph node biopsy (SLNB) has replaced axillary lymph node dissection (ALND) for assessing axillary lymph node status in clinically node-negative breast cancer patients. However, the approach to axillary surgery after neoadjuvant treatment is still controversial. In the present study, our objective was to predict the pathological nodal stage based on SLNB results and the clinicopathological characteristics of patients who initially presented with clinical N1 positivity but whose disease status was converted to clinical N0 after neoadjuvant chemotherapy (NAC). Materials and methods After NAC, 150 clinically node-negative patients were included. The relationships between clinicopathologic parameters and the number of positive lymph nodes in SLNBs and ALNDs were assessed through binary/multivariate logistic regression analysis. Results Among 150 patients, 78 patients had negative SLNBs, and 72 patients had positive SLNBs. According to the ALND data of 21 patients with SLNB1+, there was no additional node involvement (80.8%), 1–2 lymph nodes were positive in 5 patients (19.2%), and no patient had ≥ 3 lymph nodes involved. Following the detection of SLNB1 + positivity, the rate of negative non-sentinel nodes were 75% in the luminal A/B subgroup, 100% in the HER-2-positive subgroup, and 100% in the triple-negative subgroup. Patients with a lower T stage (T1-3 vs. T4), fewer than 4 clinical nodes before NAC (< 4 vs. ≥4), and a decreased postoperative Ki-67 index (< 10% vs. stable/increase) were included. According to both univariate and multivariate analyses, being in the triple-negative or HER2-positive subgroup, compared to the luminal A/B subgroup (luminal A/B vs. HER2-positive/triple-negative), was found to be predictive of complete lymph node response. Conclusion The number of SLNB-positive nodes, tumor-related parameters, and response to treatment may predict no additional nodes to be positive at ALND.

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Efficacy and safety comparison between axillary lymph node dissection with no axillary surgery in patients with sentinel node-positive breast cancer: a systematic review and meta-analysis

Cited by 4 publications

References 57 publications

Predictive value of MRI-based deep learning model for lymphovascular invasion status in node-negative invasive breast cancer

Predictive value of MRI-based deep learning model for lymphovascular invasion status in node-negative invasive breast cancer

Omitting axillary lymph node dissection is associated with an increased risk of regional recurrence in early stage breast cancer: a systematic review and meta-analysis of randomized clinical trials

Prediction of the number of positive axillary lymph nodes according to sentinel lymph node involvement and biological subtypes in patients receiving neoadjuvant chemotherapy

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