Cutaneous metastases from thyroid cancers are rare. We report the case of an otherwise asymptomatic 81-year-old woman with an enlarging scalp lesion. Her solitary skin metastasis was the presenting feature of thyroid carcinoma. Routine histopathology of the lesion was notable for an atypical clear cell neoplasm. Immunohistochemistry was positive for thyroglobulin. Subsequent resection of the thyroid gland identified separate foci (< 1 cm) for both papillary and follicular carcinoma. Although such immunohistochemical staining has been used previously, it has never been reported to provide the definitive diagnosis for a solitary cutaneous metastasis from the thyroid. Previous tumors had anatomic features in a clinical context that permitted identification by routine light microscopy. Clear cell features found in the follicular focus of carcinoma in the thyroid suggest that it is the primary. A worldwide literature review reveals that follicular carcinoma has a greater preponderance than papillary carcinoma for cutaneous metastasis and that the majority of skin metastases from either papillary or follicular thyroid cancer are localized to the head and neck.

Background:Artificial intelligence is advancing at an accelerated pace into clinical applications, providing opportunities for increased efficiency, improved accuracy, and cost savings through computer-aided diagnostics. Dermatopathology, with emphasis on pattern recognition, offers a unique opportunity for testing deep learning algorithms.Aims:This study aims to determine the accuracy of deep learning algorithms to diagnose three common dermatopathology diagnoses.Methods:Whole slide images (WSI) of previously diagnosed nodular basal cell carcinomas (BCCs), dermal nevi, and seborrheic keratoses were annotated for areas of distinct morphology. Unannotated WSIs, consisting of five distractor diagnoses of common neoplastic and inflammatory diagnoses, were included in each training set. A proprietary fully convolutional neural network was developed to train algorithms to classify test images as positive or negative relative to ground truth diagnosis.Results:Artificial intelligence system accurately classified 123/124 (99.45%) BCCs (nodular), 113/114 (99.4%) dermal nevi, and 123/123 (100%) seborrheic keratoses.Conclusions:Artificial intelligence using deep learning algorithms is a potential adjunct to diagnosis and may result in improved workflow efficiencies for dermatopathologists and laboratories.

represents a transformative technology that impacts dermatologists and dermatopathologists from residency to academic and private practice. Two concerns are accuracy of interpretation from whole-slide images (WSI) and effect on workflow. Studies of considerably large series involving single-organ systems are lacking.OBJECTIVE To evaluate whether diagnosis from WSI on a digital microscope is inferior to diagnosis of glass slides from traditional microscopy (TM) in a large cohort of dermatopathology cases with attention on image resolution, specifically eosinophils in inflammatory cases and mitotic figures in melanomas, and to measure the workflow efficiency of WSI compared with TM. DESIGN, SETTING, AND PARTICIPANTS Three dermatopathologists established interobserver ground truth consensus (GTC) diagnosis for 499 previously diagnosed cases proportionally representing the spectrum of diagnoses seen in the laboratory. Cases were distributed to 3 different dermatopathologists who diagnosed by WSI and TM with a minimum 30-day washout between methodologies. Intraobserver WSI/TM diagnoses were compared, followed by interobserver comparison with GTC. Concordance, major discrepancies, and minor discrepancies were calculated and analyzed by paired noninferiority testing. We also measured pathologists' read rates to evaluate workflow efficiency between WSI and TM. This retrospective study was caried out in an independent, national, university-affiliated dermatopathology laboratory. MAIN OUTCOMES AND MEASURESIntraobserver concordance of diagnoses between WSI and TM methods and interobserver variance from GTC, following College of American Pathology guidelines.RESULTS Mean intraobserver concordance between WSI and TM was 94%. Mean interobserver concordance was 94% for WSI and GTC and 94% for TM and GTC. Mean interobserver concordance between WSI, TM, and GTC was 91%. Diagnoses from WSI were noninferior to those from TM. Whole-slide image read rates were commensurate with WSI experience, achieving parity with TM by the most experienced user.CONCLUSIONS AND RELEVANCE Diagnosis from WSI was found equivalent to diagnosis from glass slides using TM in this statistically powerful study of 499 dermatopathology cases. This study supports the viability of WSI for primary diagnosis in the clinical setting.

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