scholarly journals Histological scoring of immune and stromal features in breast and axillary lymph nodes is prognostic for distant metastasis in lymph node-positive breast cancers

2018 ◽  
Vol 4 (1) ◽  
pp. 39-54 ◽  
Author(s):  
Anita Grigoriadis ◽  
Patrycja Gazinska ◽  
Trupti Pai ◽  
Sheeba Irhsad ◽  
Yin Wu ◽  
...  
Keyword(s):  
Lymph Node ◽  
Lymph Nodes ◽  
Distant Metastasis ◽  
Axillary Lymph Nodes ◽  
Axillary Lymph ◽  
Breast Cancers ◽  
Node Positive ◽  
Lymph Node Positive

Primary Tumor Site, Size, Patients Age and Axillary Lymph Nodes in Breast Cancers

1986 ◽  
Vol 72 (3) ◽  
pp. 259-265 ◽  
Author(s):  
Salvatore Toma ◽  
Stefano Bonassi ◽  
Riccardo Puntoni ◽  
Guido Nicolò
Keyword(s):  
Lymph Node ◽  
Lymph Nodes ◽  
Primary Tumor ◽  
Lymph Node Involvement ◽  
Axillary Lymph Nodes ◽  
Axillary Lymph ◽  
Breast Cancers ◽  
Primary Tumor Site ◽  
Primary Tumors ◽  
Node Involvement

This study considers the correlations between some characteristics of the primary tumor and level of lymph node involvement in 185 primary breast cancers. The average number of lymph nodes was higher in N + women than in N— women. Primary tumors with a diameter of more than 4 cm yielded the highest mean number of lymph nodes (17.5). The risk of developing lymph node metastases was fourfold in tumors with a diameter greater than 2 cm when compared to those with a diameter less than or equal to 2 cm. The most commonly metastasized lymph node level, in both large and small tumors, was the first; however, one-fifth of the patients had simultaneous lymph node metastasis in all three axillary levels. Although the left breast was the most affected (58.9 %), there was no evidence of a different risk of metastasis between the two breasts; 34.1 % of the tumors were multifocal. Lymph node involvement was higher in women under 50 years of age with a primary tumor larger than 2 cm.

Adding carbon nanoparticles to dual-tracers for the sentinel node evaluation after neoadjuvant chemotherapy in patients with pretreatment node-positive breast cancers: The TT-SLNB trial.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 566-566
Author(s):  
Jie Chen ◽  
Jiqiao Yang ◽  
Tao He ◽  
Yunhao Wu ◽  
Xian Jiang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Lymph Node ◽  
Neoadjuvant Chemotherapy ◽  
Lymph Nodes ◽  
Cancer Patients ◽  
Carbon Nanoparticles ◽  
Axillary Lymph Nodes ◽  
Axillary Lymph ◽  
Breast Cancers ◽  
Breast Cancer Patients

566 Background: This study measures the feasibility and accuracy of sentinel lymph node biopsy (SLNB) with triple-tracers (TT-SLNB) which combines carbon nanoparticles (CNS) with dual tracers of radioisotope and blue dye, hoping to achieve an optimized method of SLNB after neoadjuvant chemotherapy (NAC) in ycN0 breast cancer patients with pretreatment positive axillary lymph nodes. Methods: Clinically node-negative invasive breast cancer patients with pre-NAC positive axillary lymph nodes who received surgeries from November 2020 to January 2021 were included. CNS was injected at the peritumoral site the day before surgery. Standard dual-tracer (SD)-SLNs were defined as blue-colored and/or hot nodes, and TT-SLNs were defined as lymph nodes detected by any of hot, blue-stained, black-stained, and/or palpated SLNs. All patients received subsequent axillary lymph node dissection. Detection rate (DR), false-negative rate (FNR), negative predictive value (NPV) and accuracy of SLNB were calculated. Results: Seventy-six of 121 (62.8%) breast cancer patients converted to cN0 after NAC and received TT-SLNB. After NAC, 28.95% (22/76) achieved overall (breast and axilla) pCR. The DR was 94.74% (72/76), 88.16% (67/76) and 96.05% (73/76) for SLNB with single-tracer of CNS (CNS-SLNB), SD-SLNB, and TT-SLNB, respectively. The FNR was 22.86% (8/35) for CNS-SLNB and 10% (3/30) for SD-SLNB. The FNR of TT-SLNB was 5.71% (2/35), which was significantly lower than those of CNS-SLNB and SD-SLNB. The NPV and accuracy was 95.0% and 97.3% for TT-SLNB, respectively. Moreover, a significant relation was seen between the pretreatment clinical T classification and the DR of TT-SLNB (Fisher’s exact test, p= 0.010). Conclusions: TT-SLNB revealed ideal performance in post-NAC ycN0 patients with pretreatment node-positive breast cancers. The application of TT-SLNB reached a better balance between more accurate axillary evaluation and less intervention. Clinical trial information: ChiCTR2000039814. [Table: see text]

scholarly journals Dedicated Axillary MRI-Based Radiomics Analysis for the Prediction of Axillary Lymph Node Metastasis in Breast Cancer

Cancers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 757
Author(s):  
Sanaz Samiei ◽  
Renée W. Y. Granzier ◽  
Abdalla Ibrahim ◽  
Sergey Primakov ◽  
Marc B. I. Lobbes ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Lymph Nodes ◽  
Axillary Lymph Node ◽  
Axillary Lymph Node Metastasis ◽  
Axillary Lymph Nodes ◽  
Axillary Lymph ◽  
Mr Images ◽  
Node Metastasis

Radiomics features may contribute to increased diagnostic performance of MRI in the prediction of axillary lymph node metastasis. The objective of the study was to predict preoperative axillary lymph node metastasis in breast cancer using clinical models and radiomics models based on T2-weighted (T2W) dedicated axillary MRI features with node-by-node analysis. From August 2012 until October 2014, all women who had undergone dedicated axillary 3.0T T2W MRI, followed by axillary surgery, were retrospectively identified, and available clinical data were collected. All axillary lymph nodes were manually delineated on the T2W MR images, and quantitative radiomics features were extracted from the delineated regions. Data were partitioned patient-wise to train 100 models using different splits for the training and validation cohorts to account for multiple lymph nodes per patient and class imbalance. Features were selected in the training cohorts using recursive feature elimination with repeated 5-fold cross-validation, followed by the development of random forest models. The performance of the models was assessed using the area under the curve (AUC). A total of 75 women (median age, 61 years; interquartile range, 51–68 years) with 511 axillary lymph nodes were included. On final pathology, 36 (7%) of the lymph nodes had metastasis. A total of 105 original radiomics features were extracted from the T2W MR images. Each cohort split resulted in a different number of lymph nodes in the training cohorts and a different set of selected features. Performance of the 100 clinical and radiomics models showed a wide range of AUC values between 0.41–0.74 and 0.48–0.89 in the training cohorts, respectively, and between 0.30–0.98 and 0.37–0.99 in the validation cohorts, respectively. With these results, it was not possible to obtain a final prediction model. Clinical characteristics and dedicated axillary MRI-based radiomics with node-by-node analysis did not contribute to the prediction of axillary lymph node metastasis in breast cancer based on data where variations in acquisition and reconstruction parameters were not addressed.

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