Towards translation of advanced laser scanning microscopy for sentinel lymph node diagnostics focusing on melanoma metastases

Kantere, Despoina; Bagge, Roger Olofsson; Wennberg, Ann‐Marie; Ericson, Marica B.

doi:10.1364/translational.2020.tth3b.4

Cited by 2 publications

(1 citation statement)

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“…Different morphological features were found for SCC and superficial BCC, as the presence of hyperkeratosis and palisading of cancerous cells, respectively. (Paoli et al, 2008) For melanoma lesions, it has recently shown its usefulness for the identification of sentinel lymph nodes (Kantere et al, 2020). When compared to CSLM it has a higher penetration power, though if the lesion is considerably thick, outof-focus fluorescence can interfere with the collected images (an issue non-existent in CSLM due to the use of a pinhole) (Paoli et al, 2009;Multiphoton Excitation and Microscopy, 2006).…”

Section: Emerging Non-invasive Diagnostic Technologies For Skin Cancermentioning

confidence: 99%

Towards an Effective Imaging-Based Decision Support System for Skin Cancer

Vardasca

Magalhães

2022

Advances in Healthcare Information Systems and Administration

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The usage of expert systems to aid in medical decisions has been employed since 1980s in distinct applications. With the high demands of medical care and limited human resources, these technologies are required more than ever. Skin cancer has been one of the pathologies with higher growth, which suffers from lack of dermatology experts in most of the affected geographical areas. A permanent record of examination that can be further analyzed are medical imaging modalities. Most of these modalities were also assessed along with machine learning classification methods. It is the aim of this research to provide background information about skin cancer types, medical imaging modalities, data mining and machine learning methods, and their application on skin cancer imaging, as well as the disclosure of a proposal of a multi-imaging modality decision support system for skin cancer diagnosis and treatment assessment based in the most recent available technology. This is expected to be a reference for further implementation of imaging-based clinical support systems.

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Section: Emerging Non-invasive Diagnostic Technologies For Skin Cancermentioning

confidence: 99%