BackgroundMany prior institutional and multi‐institutional studies have applied the Milan System for Reporting Salivary Gland Cytopathology (MSRSGC) retrospectively to their specimens to determine the risk of malignancy (ROM) of each category. Most of these studies focused on general assessment of the system and risk classification. However, there seems to be less focus on the category of atypia of undetermined significance (AUS) that could be attributed to the low number of cases that could fit into this category. Herein, we present a bi‐institutional experience with this category.MethodsA computerized search of the databases was performed to identify all salivary gland fine‐needle aspiration (FNA) in two institutions over a period of 12 years. The final diagnosis of each case was reclassified based on MSRSGC, and histology follow‐up was retrieved.ResultsSixty AUS cases (out of 1560 salivary gland FNA) were identified with a rate of 3.8%. Forty cases (66%) had a subsequent tissue material. Correlation with histology revealed that the estimated ROM is 37.5% (15/40) and the overall ROM is 25% (15/60). Fifty percent of the cases had a prominent lymphoid component and most commonly represented lymphomas, reactive lymph node or sialadenitis.ConclusionThe AUS category is a heterogeneous group of lesions with predominant lymphoid‐rich entities. Some variability exists between institutions with most having higher ROM than the suggested 20% by the MSRSGC atlas.

Advances in computational algorithms and tools have made the prediction of cancer patient outcomes using computational pathology feasible. However, predicting clinical outcomes from pre-treatment histopathologic images remains a challenging task, limited by the poor understanding of tumor immune micro-environments. In this study, an automatic, accurate, comprehensive, interpretable, and reproducible whole slide image (WSI) feature extraction pipeline known as, IMage-based Pathological REgistration and Segmentation Statistics (IMPRESS), is described. We used both H&E and multiplex IHC (PD-L1, CD8+, and CD163+) images, investigated whether artificial intelligence (AI)-based algorithms using automatic feature extraction methods can predict neoadjuvant chemotherapy (NAC) outcomes in HER2-positive (HER2+) and triple-negative breast cancer (TNBC) patients. Features are derived from tumor immune micro-environment and clinical data and used to train machine learning models to accurately predict the response to NAC in breast cancer patients (HER2+ AUC = 0.8975; TNBC AUC = 0.7674). The results demonstrate that this method outperforms the results trained from features that were manually generated by pathologists. The developed image features and algorithms were further externally validated by independent cohorts, yielding encouraging results, especially for the HER2+ subtype.

Hepatic angiomyolipoma (HAML) is a rare mesenchymal neoplasm that belongs to the perivascular epithelioid tumor family. Though it is characteristically, a triphasic tumor composed of smooth muscle, blood vessels, and adipocytes, the smooth muscle cells are often epithelioid and can represent the near‐entirety of the tumor. A HAML composed predominantly of epithelioid smooth muscle cells occurring in the liver presents significant diagnostic challenges as many liver tumors are composed of large epithelioid cells. Furthermore, even if the tumor is not composed predominantly of epithelioid smooth muscle cells, this may be the only component present in a fine‐needle aspiration (FNA) or core needle biopsy. A 38‐year‐old female with a 3 month history of abdominal pain, nausea, and diarrhea was found to have a 12 cm right hepatic lobe mass. FNA biopsy revealed a moderately cellular specimen composed of plump epithelioid cells with indistinct cell borders, low N:C ratio, round to oval nuclei, fine chromatin, occasional nucleoli, and abundant vacuolated to fibrillary cytoplasm. Rare intranuclear inclusions and occasional foamy macrophages were noted. Concurrent core biopsy revealed large polygonal cells with eccentric nuclei and clear, vacuolated to granular, eosinophilic cytoplasm that stained strongly for HMB45, confirming the diagnosis of HAML. Because HAML is a rare tumor, this diagnosis can be easily overlooked; cognizance of the typical cytologic, histologic, and immunophenotypic findings is crucial to establishing a diagnosis.

Tumor necrosis factor-α (TNF-α)-inhibiting agents are a standard therapy for moderate-to-severe inflammatory bowel disease (IBD). IgA nephropathy in the setting of prolonged exposure to TNF-α inhibitors is a rare, clinically significant adverse event often overlooked by gastroenterologists but well documented in the rheumatologic literature. We present a case series of 3 patients with IBD on TNF-α inhibitors who developed biopsy-proven IgA nephropathy. Clinicians prescribing TNF-α inhibitors to patients with IBD need to be aware of this potential side effect. Therapies with alternative mechanisms of action should instead be considered.

BCL6 corepressor; BCL6 corepressor loss; CD30 e lymphoma; CD30 e T-cell lymphoma; CD30 e cutaneous T-cell lymphoma; CD20; cutaneous T-cell lymphoma, cutaneous T-cell lymphoma, not otherwise specified; mammalian target of rapamycin; mammalian target of rapamycin mutation; mammalian target of rapamycin inhibitor; peripheral T-cell lymphoma; peripheral T-cell lymphoma, unspecified; peripheral T-cell lymphoma, not otherwise specified; primary cutaneous lymphoma; primary cutaneous peripheral T-cell lymphoma; primary cutaneous peripheral T cell lymphoma, not otherwise specified; rituximab; T-cell lymphoma; TAK-228.