Difficulty-aware bi-network with spatial attention constrained graph for axillary lymph node segmentation

SummaryThe aim of this study was to evaluate the diagnostic abilities of 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) compared with those of ultrasonography and magnetic resonance imaging (MRI) for axillary lymph node staging in breast cancer patients. Patients, methods: Pre- operative 18F-FDG PET/non-contrast CT, ultrasonography and MRI were performed in 215 women with breast cancer. Axillary lymph node dissection was performed in all patients and the diagnostic performance of each modality was evaluated using histopathologic assessments as the reference standard. ROC curves were compared to evaluate the diagnostic ability of several imaging modalities (i. e., ultrasonography, MRI and 18F-FDG PET/CT). Results: In total, 132 patients (61.4%) had axillary lymph node metastasis. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy for the detection of axillary lymph node metastasis were 72.3%, 77.3%, 66.7%, 81.6%, 75.3% for ultrasonography, 67.5%, 78.0%, 65.9%, 79.2%, 74.0% for MRI, and 62.7%, 88.6%, 77.6%, 79.1%, 78.6% for 18F-FDG PET/CT, respectively. There was no significant difference in diagnostic ability among the imaging modalities (i.e., ultrasonography, MRI and 18F-FDG PET/CT). The diagnostic ability of 18F-FDG PET/CT was significantly improved by combination with MRI (p = 0.0002) or ultrasonography (p < 0.0001). The combination of 18F-FDG PET/CT with ultrasonography had a similar diagnostic ability to that of all three modalities combined (18F-FDG PET/CT+ultraso- nography+MRI, p = 0.05). Conclusion: The diagnostic performance of 18F-FDG PET/CT for detection of axillary node metastasis was not significantly different from that of ultrasonography or MRI in breast cancer patients. Combining 18F-FDG PET/CT with ultrasonography or MRI could improve the diagnostic performance compared to 18F-FDG PET/CT alone.

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Low TGF-β1 in Wound Exudate Predicts Surgical Site Infection After Axillary Lymph Node Dissection

Journal of Surgical Research ◽

10.1016/j.jss.2021.05.039 ◽

2021 ◽

Vol 267 ◽

pp. 302-308

Author(s):

Ida-Maria Leppäpuska ◽

Eeva H. Rannikko ◽

Mervi Laukka ◽

Emilia Peuhu ◽

Raili Veemaa ◽

...

Keyword(s):

Lymph Node ◽

Surgical Site Infection ◽

Axillary Lymph Node Dissection ◽

Lymph Node Dissection ◽

Axillary Lymph Node ◽

Axillary Lymph ◽

Site Infection ◽

Node Dissection ◽

Wound Exudate ◽

Tgf Β1

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CDX2-positive breast cancer presented with axillary lymph node metastases: a case report

Cancer Treatment and Research Communications ◽

10.1016/j.ctarc.2020.100300 ◽

2020 ◽

pp. 100300

Author(s):

Hideko Hoshina ◽

Hiroyuki Takei ◽

Takashi Sakatani ◽

Zenya Naito

Keyword(s):

Breast Cancer ◽

Case Report ◽

Lymph Node ◽

Lymph Node Metastases ◽

Axillary Lymph Node ◽

Axillary Lymph ◽

Axillary Lymph Node Metastases ◽

Node Metastases ◽

Positive Breast Cancer

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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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