Applying an artificial intelligence deep learning approach to routine dermatopathological diagnosis of basal cell carcinoma.

Background: Institutes of dermatopathology are faced with considerable challenges including a continuously rising numbers of submitted specimens and a shortage of specialized health care practitioners. Basal cell carcinoma (BCC) is one of the most common tumors in the fair-skinned western population and represents a major part of samples submitted for histological evaluation. Digitalizing glass slides has enabled the application of artificial intelligence (AI)-based procedures.

Deeply Supervised UNet for Semantic Segmentation to Assist Dermatopathological Assessment of Basal Cell Carcinoma.

Accurate and fast assessment of resection margins is an essential part of a dermatopathologist's clinical routine. In this work, we successfully develop a deep learning method to assist the dermatopathologists by marking critical regions that have a high probability of exhibiting pathological features in whole slide images (WSI). We focus on detecting basal cell carcinoma (BCC) through semantic segmentation using several models based on the UNet architecture.

Retrospective analysis of the frequency of centrofacial telangiectasia in systemic sclerosis patients treated with bosentan or ilomedin.

Background: Bosentan is a dual endothelin receptor antagonist initially introduced for the treatment of pulmonary arterial hypertension and recently approved for the treatment of digital ulcers in patients with systemic sclerosis (SSc). Our clinical observations indicate that bosentan therapy may be associated with an increased frequency of centrofacial telangiectasia (TAE). Here, we sought to analyze the frequency of TAE in patients with SSc who were treated with either bosentan or the prostacyclin analog iloprost.

Management of EGFR-inhibitor associated rash: a retrospective study in 49 patients.

Background: In recent years inhibitors directed against the epidermal growth factor receptor (EGFR) have evolved as effective targeting cancer drugs. Characteristic papulopustular exanthemas, often described as acneiform rashes, are the most frequent adverse effect associated with this class of novel cancer drugs and develop in > 90% of patients. Notably, the rash may significantly compromise the patients' quality of life, thereby potentially leading to incompliance as well as dose reduction or even termination of the anti-EGFR therapy.