Background: The diagnosis of most cancers is made by a board-certified pathologist based on a tissue biopsy under the microscope. Recent research reveals a high discordance between individual pathologists. For melanoma, the literature reports on 25e26% of discordance for classifying a benign nevus versus malignant melanoma. A recent study indicated the potential of deep learning to lower these discordances. However, the performance of deep learning in classifying histopathologic melanoma images was never compared directly to human experts. The aim of this study is to perform such a first direct comparison. Methods: A total of 695 lesions were classified by an expert histopathologist in accordance with current guidelines (350 nevi/345 melanoma). Only the haematoxylin & eosin (H&E) slides
Background: Multiple studies have compared the performance of artificial intelligence (AI)ebased models for automated skin cancer classification to human experts, thus setting the cornerstone for a successful translation of AI-based tools into clinicopathological practice.
The term "hidradenitis suppurativa" is firmly entrenched in the dermatological literature although it refers to a false pathogenetic concept. The term was historically coined based merely on the characteristic distribution of the apocrine glands and the anatomical coincidence with the disease process. At center stage is not a suppurative inflammation of the apocrine sweat glands but an occlusion of the hair follicles, comparable to acne vulgaris. Reviewing the literature on this subject, we were astonished to find that even articles that concluded that the entity represents a form of follicular occlusion still referred to it as hidradenitis suppurativa. The disorder shares histopathological and clinical aspects with acne vulgaris modified under the special circumstances of anatomical regions rich in apocrine glands. It is acne inversa because, in contrast to acne vulgaris, the disease involves intertriginous localizations and not the regions classically affected by acne. We suggest that the term "hidradenitis suppurativa" for this disease should (finally) be abandoned in favour of "acne inversa".
Background: The diagnosis of most cancers is made by a board-certified pathologist based on a tissue biopsy under the microscope. Recent research reveals a high discordance between individual pathologists. For melanoma, the literature reports 25e26% of discordance for classifying a benign nevus versus malignant melanoma. Deep learning was successfully implemented to enhance the precision of lung and breast cancer diagnoses. The aim of this study is to illustrate the potential of deep learning to assist human assessment for a histopathologic melanoma diagnosis. Methods: Six hundred ninety-five lesions were classified by an expert histopathologist in accordance with current guidelines (350 nevi and 345 melanomas). Only the haematoxylin and eosin stained (H&E) slides of these lesions were digitalised using a slide scanner and then randomly cropped. Five hundred ninety-five of the resulting images were used for the training of a convolutional neural network (CNN). The additional 100 H&E image sections were used to test the results of the CNN in comparison with the original class labels.
The hair follicle bulge marker PHLDA1 differentiates between desmoplastic trichoepitheliomas and nonulcerated examples of morphoeic BCCs. We suggest incorporating PHLDA1 in the diagnostic work-up of difficult to differentiate basaloid tumours.
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