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Sentinel Lymph Node Biopsy in Cutaneous Melanoma - Analysis of 227 Cases

100%

Spychała A. , Murawa D. , Połom K. , Gracz A. , Murawa P.

Polish Journal of Surgery

2008

tom 80

nr 5

241-247

The aim of the study was to evaluate the experience of the centre in carrying out and assessing the use of sentinel node biopsy in skin melanoma.Material and methods. From 2000 to 2004, a sentinel node biopsy was carried out on 227 patients being treated for skin melanoma. In all the patients, the sentinel node was subjected to a standard histological evaluation with the application of H+E staining. If no melanoma cells were visualised within the sentinel node, the material was subjected to an immunohistochemical investigation. The patients whose sentinel node included metastases found in the H+E examination or micrometastases identified by means of the immunohistochemical investigation were subjected to a supplementary lymphadenectomy.Results. The sentinel node was identified in all the patients. In a group of 28 patients (12.3%), the presence of metastases within the sentinel node was visualised by means of H+E examination. A group of 199 patients (87.66%) with a metastases-negative sentinel node (H+E staining) was subjected to immunohistochemical evaluation, which revealed the presence of micrometastases in 45 patients (19.82%). Supplementary lymphadenectomy was carried out in patients who screened positive, and metastases were identified in other regional lymph nodes in 11 (4.8%) patients.Conclusions. 1. After finishing the learning curve, the sentinel node biopsy is a simple and effective method, enabling precise assessment of the lymphatic system in patients with skin melanoma. 2. The application of immunohistochemical investigation enabled the identification of micrometastases in 19.8% of the patients, where these were not found in the H+E examination. 3. The application of sentinel node biopsy allowed lymphadenectomy to be avoided in 154 (67.8%) patients.

An automated skin melanoma detection system with melanoma-index based on entropy features

51%

Cheong K. H. , Tang K. J. W. , Zhao X. , Koh J. E. W. , Faust O. , Gururajan R. , Ciaccio E. J. , Rajinikanth V. , Acharya U. R.

Biocybernetics and Biomedical Engineering

2021

tom Vol. 41, no. 3

997--1012

Skin melanoma is a potentially life-threatening cancer. Once it has metastasized, it may cause severe disability and death. Therefore, early diagnosis is important to improve the conditions and outcomes for patients. The disease can be diagnosed based on Digital-Dermoscopy (DD) images. In this study, we propose an original and novel Automated Skin-Melanoma Detection (ASMD) system with Melanoma-Index (MI). The system incorporates image pre-processing, Bi-dimensional Empirical Mode Decomposition (BEMD), image texture enhancement, entropy and energy feature mining, as well as binary classification. The system design has been guided by feature ranking, with Student’s t-test and other statistical methods used for quality assessment. The proposed ASMD was employed to examine 600 benign and 600 DD malignant images from benchmark databases. Our classification performance assessment indicates that the combination of Support Vector Machine (SVM) and Radial Basis Function (RBF) offers a classification accuracy of greater than 97.50%. Motivated by these classification results, we also formulated a clinically relevant MI using the dominant entropy features. Our proposed index can assist dermatologists to track multiple information-bearing features, thereby increasing the confidence with which a diagnosis is given.