Detection of Nonpalpable Tiny Axillary Lymph Nodes Surrounded by Adipose Tissue Using a Near-Infrared Camera

AbstractRecent evidence suggests that a loss of expression of caveolin in the stromal compartment (sCav-1) of human invasive breast carcinoma (IBC) may be a predictor of disease recurrence, metastasis and poor outcome. At present, there is little knowledge regarding the expression of sCav-1 at the metastatic sites. We therefore studied sCav-1 expression in IBCs and in their axillary lymph nodes to seek a correlation with cancer metastasis. 189 consecutive invasive IBCs (53 with axillary lymph node metastases and 136 without) were studied by immunohistochemistry, using a rabbit polyclonal anti-Cav-1 antibody. In IBCs sCav-1 was evaluated in fibroblasts scattered in the tumor stroma whereas in lymph nodes sCav-1 was assessed in fibroblast-like stromal cells. For the first time, we observed a statistically significant progressive loss of sCav-1 from normal/reactive axillary lymph nodes of tumors limited to the breast to metastatic axillary lymph nodes, through normal/reactive axillary lymph nodes of tumors with axillary metastatic spread. These data indicate that Cav-1 expressed by the stromal compartment of lymph nodes, somehow, may possibly contribute to metastatic spread in IBC.

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Development of MRI‐based Axillary Numerical Models and Estimation of Axillary Lymph Nodes Dielectric Properties for Microwave Imaging

Medical Physics ◽

10.1002/mp.15143 ◽

2021 ◽

Author(s):

Daniela M. Godinho ◽

João M. Felício ◽

Tiago Castela ◽

Nuno A. Silva ◽

M. Lurdes Orvalho ◽

...

Keyword(s):

Dielectric Properties ◽

Lymph Nodes ◽

Numerical Models ◽

Microwave Imaging ◽

Axillary Lymph Nodes ◽

Axillary Lymph

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Intra-mammary lymph nodes, an overlooked breast cancer prognostic tool?

World Journal of Surgical Oncology ◽

10.1186/s12957-021-02219-0 ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Tarek Hashem ◽

Ahmed Abdelmoez ◽

Ahmed Mohamed Rozeka ◽

Hazem Abdelazeem

Keyword(s):

Breast Cancer ◽

Lymph Nodes ◽

Ductal Carcinoma ◽

Percutaneous Biopsy ◽

Preoperative Imaging ◽

Axillary Lymph Nodes ◽

Axillary Lymph ◽

Pathological Features ◽

Breast Cancer Patients ◽

Surgical Data

Abstract Background Due to the high variability of incidence and prevalence of intra-mammary lymph nodes (IMLNs), they might be overlooked during clinical and radiological examinations. Properly characterizing pathological IMLNs and detecting the factors that might influence their prevalence in different stages of breast cancer might aid in proper therapeutic decision-making and could be of possible prognostic value. Methods Medical records were reviewed for all breast cancer patients treated at the National Cancer Institute of Cairo University between 2013 and 2019. Radiological, pathological, and surgical data were studied. Results Intra-mammary lymph nodes were described in the final pathology reports of 100 patients. Five cases had benign breast lesion. Three cases had phyllodes tumors and two cases had ductal carcinoma in situ (DCIS). All ten cases were excluded. The remaining 90 cases all had invasive breast cancer and were divided into two groups: one group for patients with malignant IMLNs (48) and another for patients with benign IMLNs (42). Pathological features of the malignant IMLN group included larger mean tumor size in pathology (4.7 cm), larger mean size of the IMLN in pathology (1.7 cm), higher incidence of lympho-vascular invasion (65.9%), and higher rate of extracapsular extension in axillary lymph nodes (57.4%). In addition, the pathological N stage was significantly higher in the malignant IMLN group. Conclusion Clinicians frequently overlook intra-mammary lymph nodes. More effort should be performed to detect them during preoperative imaging and during pathological processing of specimens. A suspicious IMLN should undergo a percutaneous biopsy. Malignant IMLNs are associated with advanced pathological features and should be removed during surgery.

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

Cancers ◽

10.3390/cancers13040757 ◽

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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