scholarly journals Acute Truncal Lymphedema Secondary to Axillary Metastatic Melanoma Presenting Like Cellulitis

2017 ◽  
Vol 2017 ◽  
pp. 1-3 ◽  
Author(s):  
Shelley J. E. Hwang ◽  
Benjamin Y. Kong ◽  
Shaun Chou ◽  
Deepal Wakade ◽  
Matteo S. Carlino ◽  
...  

There are reported cases of diphencyprone used in treating cutaneous metastases of melanoma. Here, we report a patient with previous primary melanoma on his left back treated with surgical excision and lymphadenectomy, followed by radiotherapy for the recurrent tumor on the primary site. Despite radiotherapy and treatment with dabrafenib and trametinib, in-transit metastases have developed and topical diphencyprone was applied to these metastases. Six weeks later, the patient developed fever and a spreading erythematous tender indurated plaque covering the left side of the body including axillae, back, and flank, clinically suggestive of cellulitis. Systemic antibiotic therapy did not improve the condition and a biopsy showed sparse lymphocytic infiltrate. With the diagnosis of possible acute lymphedema, a CT scan was requested that showed significant axillary lymph node metastasis. The fever was considered secondary to dabrafenib and trametinib therapy. This case highlights that, in patients with lymphadenectomy, atypical forms of lymphedema on the body may appear. Truncal lymphedema is an infrequent event.

Cancers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 757
Author(s):  
Sanaz Samiei ◽  
Renée W. Y. Granzier ◽  
Abdalla Ibrahim ◽  
Sergey Primakov ◽  
Marc B. I. Lobbes ◽  
...  

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.


2004 ◽  
Vol 87 (2) ◽  
pp. 75-79 ◽  
Author(s):  
Osamu Watanabe ◽  
Tadao Shimizu ◽  
Hiroshi Imamura ◽  
Jun Kinoshita ◽  
Yoshihito Utada ◽  
...  

Breast Cancer ◽  
2009 ◽  
Vol 19 (4) ◽  
pp. 365-368 ◽  
Author(s):  
Dimitrios M. Dragoumis ◽  
Anthoula S. Assimaki ◽  
Aris P. Tsiftsoglou

2019 ◽  
Vol 120 (2) ◽  
pp. 160-163
Author(s):  
S. Alessandro ◽  
M. Magremanne ◽  
E. Marbaix ◽  
H. Reychler ◽  
P. Mahy

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