Search

This article discusses a case of metastatic basal cell carcinoma (BCC) with neuroendocrine differentiation (BCCNE) and its response to the immunotherapy drug cemiplimab. BCC is a common type of cancer, but metastatic BCC is rare and has a high mortality rate. The patient in this case had an mBCC with neuroendocrine differentiation, which is a rare histological variant. Previous treatments did not work, but the patient responded well to cemiplimab, leading to complete remission of the cancer. The article suggests that cemiplimab could be a suitable therapeutic option for patients with metastatic BCCNE. [Extracted from the article]

AI image classification algorithms have shown promising results when applied to skin cancer detection. Most public skin cancer image datasets are comprised of dermoscopic photos and are limited by selection bias, lack of standardization, and lend themselves to development of algorithms that can only be used by skilled clinicians. The SLICE-3D ("Skin Lesion Image Crops Extracted from 3D TBP") dataset described here addresses those concerns and contains images of over 400,000 distinct skin lesions from seven dermatologic centers from around the world. De-identified images were systematically extracted from sensitive 3D Total Body Photographs and are comparable in optical resolution to smartphone images. Algorithms trained on lower quality images could improve clinical workflows and detect skin cancers earlier if deployed in primary care or non-clinical settings, where photos are captured by non-expert physicians or patients. Such a tool could prompt individuals to visit a specialized dermatologist. This dataset circumvents many inherent limitations of prior datasets and may be used to build upon previous applications of skin imaging for cancer detection., (© 2024. The Author(s).)