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. To date, these methods have found only limited application in routine diagnostics. The aim of this study was to establish an AI-based model for automated BCC detection. PATIENTS AND METHODS: In three dermatopathological centers, daily routine practice BCC cases were digitalized. The diagnosis was made both conventionally by analog microscope and digitally through an AI-supported algorithm based on a U-Net architecture neural network. RESULTS: In routine practice, the model achieved a sensitivity of 98.23% (center 1) and a specificity of 98.51%. The model generalized successfully without additional training to samples from the other centers, achieving similarly high accuracies in BCC detection (sensitivities of 97.67% and 98.57% and specificities of 96.77% and 98.73% in centers 2 and 3, respectively). In addition, automated AI-based basal cell carcinoma subtyping and tumor thickness measurement were established. CONCLUSIONS: AI-based methods can detect BCC with high accuracy in a routine clinical setting and significantly support dermatopathological work.

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. The study includes 650 WSI with 3443 tissue sections in total. Two clinical dermatopathologists annotated the data, marking tumor tissues' exact location on 100 WSI. The rest of the data, with ground-truth sectionwise labels, are used to further validate and test the models. We analyze two different encoders for the first part of the UNet network and two additional training strategies: (a) deep supervision, (b) linear combination of decoder outputs, and obtain some interpretations about what the network's decoder does in each case. The best model achieves over 96%, accuracy, sensitivity, and specificity on the Test set.

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. Yet, an effective dermatologic management of cutaneous adverse effects can be achieved. Whereas various case reports, case series or expert opinions on the management of EGFR-inhibitor (EGFRI) induced rashes have been published, data on systematic management studies are sparse. METHODS: Here, we present a retrospective, uncontrolled, comparative study in 49 patients on three established regimens for the management of EGFRI-associated rashes. RESULTS: Strikingly, patients' rash severity improved significantly over three weeks of treatment with topical mometason furoate cream, topical prednicarbate cream plus nadifloxacin cream, as well as topical prednicarbate cream plus nadifloxacin cream plus systemic isotretinoin. CONCLUSIONS: In summary our results demonstrate that EGFRI-associated rashes can be effectively managed by specific dermatologic interventions. Whereas mild to moderate rashes should be treated with basic measures in combination with topical glucocorticosteroids or combined regiments using glucocorticosteroids and antiseptics/antibiotics, more severe or therapy-resistant rashes are likely to respond with the addition of systemic retinoids.