Introduction Past studies have shown mixed results about the accuracy of store-and-forward (SAF) teledermatology in the evaluation of skin lesions. The objective of this study is to determine the accuracy of SAF teledermatology in the diagnosis of skin lesions and biopsy decision compared to in-person clinical evaluation. Methods Histories and photographs of skin lesions gathered at clinic visits were sent as SAF consults to teledermatologists, whose diagnoses and biopsy decisions were recorded and compared statistically to the clinic data. Results and Discussion: We enrolled 206 patients with 308 lesions in the study. The study population was composed of 50% males ( n = 104), and most patients were white ( n = 179, 87%) and not Hispanic/Latino ( n = 167, 81%). There was good concordance for biopsy decision between the clinic dermatologist (CD) and teledermatologist (TD) (Cohen’s kappa (κ) = 0.51), which did not significantly differ when melanocytic lesions were excluded (κ = 0.54). The sensitivity and specificity of teledermatology based on biopsy decision was 0.71 and 0.85, respectively. Overall concordance in first diagnosis between the CD and TD was good (κ = 0.60). While there was no difference between CD and TD in proportion of correct diagnoses compared to histopathology, two skin cancers presentations were missed by TD. Study limitations included sample size, enrolment bias and differing amounts of teledermatologist case experience. Teledermatology has good concordance in diagnosis and biopsy decision when compared to clinic dermatology. Teledermatology may be utilized in the evaluation of skin lesions to expand access to dermatologic care.

ohs micrographic surgery (MMS) is considered a standard of care treatment modality for nonmelanoma skin cancer (NMSC) of the head and neck. However, access to dermatologic care and MMS may be limited by wait times, cost, and the relative shortage of dermatologists in rural areas. 1 Insurance type is associated with increased stage at presentation, differences in treatment, and delays in care for patients with melanoma 2-4 and other noncutaneous cancers, including lung, prostate, breast, and colon cancer. [5][6][7][8][9][10] The objective of this study was to assess whether tumor and treatment characteristics differ based on insurance type among patients undergoing MMS for NMSC because there has been little research investigating this association.

A two-year-old girl presented for evaluation of asymptomatic congenital white patches in addition to new white patches that had appeared over the past few months. She had bilateral congenital sensorineural hearing loss and mild gross motor delays. Family history was negative for similar lesions. Physical examination demonstrated depigmented patches on the lower extremities with poorly circumscribed, feathered edges (Figs 1 and 2). Eye examination was significant for heterochromia iridis and normal placement of the inner canthi (Fig 3). There were no other abnormalities on examination.

Determining early-stage prognostic markers and stratifying patients for effective treatment are two key challenges for improving outcomes for melanoma patients. Previous studies have used tumour transcriptome data to stratify patients into immune subgroups, which were associated with differential melanoma specific survival and potential treatment strategies. However, acquiring transcriptome data is a time-consuming and costly process. Moreover, it is not routinely used in the current clinical workflow. Here we attempt to overcome this by developing deep learning models to classify gigapixel H&E stained pathology slides, which are well established in clinical workflows, into these immune subgroups. Previous subtyping approaches have employed supervised learning which requires fully annotated data, or have only examined single genetic mutations in melanoma patients. We leverage a multiple-instance learning approach, which only requires slide-level labels and uses an attention mechanism to highlight regions of high importance to the classification. Moreover, we show that pathology-specific self-supervised models generate better representations compared to pathology-agnostic models for improving our model performance, achieving a mean AUC of 0.76 for classifying histopathology images as high or low immune subgroups. We anticipate that this method may allow us to find new biomarkers of high importance and could act as a tool for clinicians to infer the immune landscape of tumours and stratify patients, without needing to carry out additional expensive genetic tests.

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