Dermatologie (Heidelberg, Germany)最新文献

Primary cutaneous B‑cell lymphomas (PCBCL) belong to the group of extranodal non-Hodgkin lymphomas and by definition only manifest on the skin at the time of diagnosis. These lymphomas can be broadly classified into indolent and aggressive subtypes. Indolent PCBCLs include primary cutaneous marginal zone lymphoma, primary cutaneous follicular B‑cell lymphoma and Epstein-Barr virus-positive mucocutaneous ulcer, which present solitary or multifocal lesions and are associated with a favorable prognosis. Treatment usually involves excision or radiotherapy, although a watch-and-wait concept can also be discussed. Conversely, aggressive PCBCLs, such as primary cutaneous diffuse large B‑cell lymphoma, leg type, and primary cutaneous intravascular large B‑cell lymphoma, carry a high risk of systemic dissemination. Consequently, they require systemic treatment. The standard first-line therapy is typically the R‑CHOP regimen, which may be supplemented with radiotherapy.

Reflectance confocal microscopy (RCM) is a noninvasive imaging technique offering high lateral resolution and is increasingly used in dermatology for differentiating benign from malignant melanocytic skin lesions such as nevi, melanoma, and lentigo maligna. By directing focused laser light into the skin and capturing reflected signals through a confocal aperture, horizontal optical sections of the epidermis and upper dermis can be generated with a resolution of 1-3 µm. Hallmarks of malignancy-such as pagetoid cells, nonedged papillae, and irregular nests-can be detected in vivo. The penetration depth of the technique is limited to approximately 250-300 µm. One of the key strengths of RCM lies in its ability to delineate tumor margins in lentigo maligna prior to surgical or laser-based therapy. In addition to reflectance imaging, fluorescence-based visualization is possible following topical or intradermal administration of contrast agents. Recent advances integrate artificial intelligence (AI) to assist in the automated analysis of RCM images. Early AI-based systems, such as MED-Net, have demonstrated promising sensitivity and specificity for identifying melanocytic structures. Overall, RCM-especially when combined with validated AI algorithms-represents a valuable extension of diagnostic capabilities in modern dermatologic oncology.

Line-field confocal optical coherence tomography (LC-OCT) is an innovative and noninvasive imaging technique that combines the advantages of reflectance confocal microscopy (RCM) and optical coherence tomography (OCT). Previously used for the diagnosis of basal cell carcinoma and squamous cell carcinomas, recent studies also show promising results in its application in melanocytic tumors. While in LC-OCT benign nevi exhibit a regular epidermal architecture with a honeycomb and cobblestone pattern as well as clearly defined dermoepidermal junctions (DEJ), melanomas, in contrast, exhibit an irregular cellular architecture with pagetoid ascending cells, destruction of the DEJ, and clefting. Therefore, LC-OCT expands the diagnostic spectrum for the differentiated assessment of conspicuous melanocytic lesions.

Ex vivo confocal laser scanning microscopy is a modern diagnostic tool that enables rapid microscopic evaluation of freshly excised skin samples without tissue loss. By combining reflectance and fluorescence imaging with digital staining algorithms, the technique produces section images that closely resemble conventional hematoxylin-eosin (HE) staining. In dermatological practice, it is primarily used for the diagnosis and margin assessment of basal cell carcinomas. The method also shows promise for squamous cell carcinomas, its precursors, and inflammatory dermatoses. Current limitations include the assessment of pigmented lesions and the differentiation of inflammatory cell infiltrates. Future developments such as artificial intelligence (AI)-assisted image analysis or fluorescence-labeled antibodies could further expand its range of applications.

Background: The incidence of malignant melanoma has increased exponentially in recent decades. The combination of total body photography (TBP) and digital dermoscopy (DD) improved the early detection of melanoma in previous studies. Furthermore, the use of artificial intelligence (AI) can improve the diagnostic accuracy of dermatologists.

Objectives: In the present work, the applications of AI in TBP, DD, and high-resolution dermoscopy in clinical practice are evaluated.

Materials and methods: A literature review was conducted of previous studies on the use of TBP, DD, and high-resolution dermoscopy and the integration of AI in these devices for the early detection of melanoma.

Results and conclusion: The use of AI can improve the diagnostic accuracy of TBP, DD, and high-resolution dermoscopy. As most of the studies to date have been conducted in an artificial setting, more studies are needed to evaluate the potential use of AI in the clinical setting. In addition, staff must be trained and informed about the legal framework regarding the use of AI in everyday clinical practice.

Modern imaging techniques such as optical coherence tomography (OCT), lateral-confocal OCT, and confocal laser microscopy (KLM) are now well established in dermatology, enhancing diagnostic precision and therapeutic decisions. Currently, billing is handled via analogous codes in the German fee schedule (GOÄ), leading to legal and reimbursement challenges. Although the upcoming revised GOÄ will include specific codes, this may result in lower compensation. Public health insurance does not yet cover these services. Legal and reimbursement frameworks must be urgently adapted to ensure sustainable integration of these technologies into clinical practice.

Ex vivo confocal laser scanning microscopy (EVCM) is an innovative diagnostic method that allows tissue samples to be visualized digitally, quickly and in high resolution immediately after removal. It provides dermatologists with an overview of possible pathological findings during the surgical procedure. At present, research in the field of EVCM mainly focuses on basal cell carcinomas, but other tumor entities such as squamous cell carcinomas or malignant melanomas are increasingly being investigated. In addition, preliminary studies show promising results in the additional use of artificial intelligence (AI) for the automated detection of skin tumors and assessment of surgical margins. In future, EVCM and AI could be used together to expedite clinical processes and thus enable patients to be treated in a time and resource efficient manner.