Mediastinal Epithelioid Angiosarcoma, New Insights into an Uncommon Diagnosis: A Case Report and Literature Review.

Angiosarcoma is an uncommon malignant mesenchymal neoplasm, accounting for 1-2% of all sarcomas. More than half are cutaneous, with the remainder arising in the deep soft tissue, breast, bone or viscera, particularly the liver, spleen and heart. Mediastinal angiosarcomas are exceedingly uncommon. While epithelioid morphology is sometimes a minor component in conventional angiosarcoma, tumors with a predominance of epithelioid morphologic features are designated as epithelioid angiosarcoma (EAS). This is a report of a 58-year-old woman presenting with severe chest pain, accompanied by worsening dyspnea and dysphagia. Chest computed tomography (CT) revealed a large pericardial effusion and a bulky mediastinal mass. Biopsy revealed a malignant neoplasm with vascular differentiation consistent with high-grade EAS. By immunohistochemistry, epithelioid angiosarcomas express endothelial cell markers, such as CD31, CD34, ERG and FLI-1. A variable proportion express low molecular weight cytokeratin (CK), epithelial membrane antigen (EMA) and CD30. The use of molecular techniques has proven useful in the diagnosis of this rare neoplasm. Targeted next generation sequencing showed aberrations in multiple genes including NRAS, KRAS, MYC and TP53.

HER2 in uterine serous carcinoma: Current state and clinical perspectives.

OBJECTIVES: Uterine cancer has the highest incidence and the second-highest mortality rate among gynecologic malignancies in the United States. Although uterine serous carcinoma (USC) represents less than 10% of endometrial carcinomas, it accounts for a disproportionate 50% of tumor relapses and 40% of endometrial cancer deaths. Over the past decade, clinical trials have focused on finding better treatments for this aggressive subtype of endometrial cancer, especially HER2-targeted therapy. METHODS: We conducted a literature search in PubMed to expand the understanding of HER2 in USC. RESULTS: HER2 has been established as an important biomarker with prognostic and therapeutic implications in USC. Intratumoral heterogeneity and lateral/basolateral membranous staining of HER2 as well as high discordance between HER2 immunohistochemistry and in situ hybridization are more common in USC than in breast carcinoma. Therefore, a universal HER2 testing and scoring system more suitable to endometrial cancer is needed and currently under investigation. CONCLUSIONS: This review discusses the clinical perspective of HER2 overexpression/gene amplification in USC, the distinct HER2 staining pattern and the evaluation of HER2 in USC, the resistance mechanisms of HER2-targeted therapy in HER2-positive cancers, and likely areas of future investigation.

The potential of using artificial intelligence to improve skin cancer diagnoses in Hawai'i's multiethnic population.

Skin cancer remains the most commonly diagnosed cancer in the USA with more than 1 million new cases each year. Melanomas account for about 1% of all skin cancers and most skin cancer deaths. Multiethnic individuals whose skin is pigmented underestimate their risk for skin cancers and melanomas and may delay seeking a diagnosis. The use of artificial intelligence may help improve the diagnostic precision of dermatologists/physicians to identify malignant lesions. To validate our artificial intelligence's efficiency in distinguishing between images, we utilized 50 images obtained from our International Skin Imaging Collaboration dataset (n = 25) and pathologically confirmed lesions (n = 25). We compared the ability of our artificial intelligence to visually diagnose these 50 skin cancer lesions with a panel of three dermatologists. The artificial intelligence model better differentiated between melanoma vs. nonmelanoma with an area under the curve of 0.948. The three-panel member dermatologists correctly diagnosed a similar number of images (n = 35) as the artificial intelligence program (n = 34). Fleiss' kappa (ĸ) score for the raters and artificial intelligence indicated fair (0.247) agreement. However, the combined result of the dermatologists panel with the artificial intelligence assessments correctly identified 100% of the images from the test data set. Our artificial intelligence platform was able to utilize visual images to discriminate melanoma from nonmelanoma, using de-identified images. The combined results of the artificial intelligence with those of the dermatologists support the use of artificial intelligence as an efficient lesion assessment strategy to reduce time and expense in diagnoses to reduce delays in treatment.