:Primary sinonasal mucosal melanoma (SNMM) is an aggressive tumor with high metastatic potential and poor outcomes. Presenting symptoms are nonspecific, and the nasal cavity is the most common site of origin followed by the maxillary and ethmoid sinuses. Histopathologically, SNMMs are pleomorphic and predominantly composed of epithelioid cell type. Identifying these tumors requires a high index of suspicion for melanoma and the use of a panel of immunohistochemical markers when typical histopathological features are missing. Not infrequently, these tumors are undifferentiated and/or amelanotic. Currently, SNMM falls into 2 different staging systems proposed by the American Joint Committee on Cancer, one for carcinoma of the nasal cavity and sinuses and the other for head and neck melanoma. Although therapeutic standards do not exist, surgical resection with adjuvant radiotherapy and/or systemic therapy may offer the best outcome. Lymphadenectomy including possible parotidectomy and neck dissection should be considered in patients with regional lymph node metastasis. However, the role of elective lymph node dissection is controversial. Genetic profiling has identified a number of recurrent gene mutations that may prove useful in providing targets for novel, emerging biological treatments. In this article, we provide an update on clinicopathological features, staging, molecular discoveries, and treatment options for SNMM.

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Context.— Clinical decision support (CDS) systems could assist less experienced pathologists with certain diagnostic tasks for which subspecialty training or extensive experience is typically needed. The effect of decision support on pathologist performance for such diagnostic tasks has not been examined. Objective.— To examine the impact of a CDS tool for the classification of ovarian carcinoma subtypes by pathology trainees in a pilot observer study using digital pathology. Design.— Histologic review on 90 whole slide images from 75 ovarian cancer patients was conducted by 6 pathology residents using: (1) unaided review of whole slide images, and (2) aided review, where in addition to whole slide images observers used a CDS tool that provided information about the presence of 8 histologic features important for subtype classification that were identified previously by an expert in gynecologic pathology. The reference standard of ovarian subtype consisted of majority consensus from a panel of 3 gynecologic pathology experts. Results.— Aided review improved pairwise concordance with the reference standard for 5 of 6 observers by 3.3% to 17.8% (for 2 observers, increase was statistically significant) and mean interobserver agreement by 9.2% (not statistically significant). Observers benefited the most when the CDS tool prompted them to look for missed histologic features that were definitive for a certain subtype. Observer performance varied widely across cases with unanimous and nonunanimous reference classification, supporting the need for balancing data sets in terms of case difficulty. Conclusions.— Findings showed the potential of CDS systems to close the knowledge gap between pathologists for complex diagnostic tasks.