Solitary fibrous tumor of deep parotid gland preoperatively classified as salivary gland neoplasm of uncertain malignant potential by the Milan system for reporting salivary gland cytopathology: A common diagnosis for a rare entity.

Solitary fibrous tumor (SFT) is a rare fibroblastic tumor with spindle cell morphology, which is characterized by a prominent branching vasculature and a NAB2-STAT6 gene rearrangement. SFT may occur in any anatomical site and may involve salivary glands, including the parotid gland. We present a young female with a primary parotid SFT diagnosed as "neoplasm-Salivary gland neoplasm of uncertain malignant potential (SUMP)" per the Milan system for reporting salivary gland cytopathology by fine-needle aspiration (FNA) with surgical pathology follow-up. Cytomorphology of SFT is diverse and overlaps with more common entities causing a diagnostic challenge. Non-diagnostic FNA results are not uncommon. Thankfully, the majority of SFTs involving the salivary gland can be identified as "neoplasm" on FNA. The Neoplasm-SUMP subcategory is considered for the majority of cases, which would warrant a diagnostic excision with clear surgical margins, which is also curative in most cases. The Neoplasm-SUMP also perfectly encompasses the neoplastic behavior of SFT, which runs on a scale from indolent to malignant.

Succinate dehydrogenase-deficient gastrointestinal stromal tumor of stomach diagnosed by endoscopic ultrasound-guided fine-needle biopsy: Report of a distinct subtype in cytology.

Succinate dehydrogenase (SDH)-deficient gastrointestinal stromal tumors (GISTs) are characterized by the lack of mutations in KIT receptor tyrosine kinase complex and platelet derived growth factor receptor-alpha (PDGFRA) that are commonly found in the majority of GISTs. SDH-deficient GISTs comprise approximately 5%-10% of all GISTs. This subset may be associated with Carney Triad and Carney-Stratakis syndrome. SDH-deficient GISTs show unique demographic, radiologic, morphologic findings, clinical behavior, and treatment response. To our knowledge, the identification and characterization of this subset of GISTs have not yet been described in the cytopathology literature. By understanding the clinical as well as the other unique features of this tumor, in addition to the rapidly evolving identification of specific molecular alterations and targeted therapies, cytopathologists may play an important role in the diagnosis and work-up of these patients to allow clinicians to better manage and treat them. We present a young female with gastric SDH-deficient GIST diagnosed by fine-needle biopsy with supporting surgical pathology follow-up and molecular confirmation. This report suggests that the diagnosis of SDH-deficient GIST can be made on cytology in the appropriate clinical setting by using cytomorphologic features and demonstrating SDH loss by IHC on the cell block. In addition, molecular testing may be possible on the cytology cell block or supernatant to confirm the diagnosis.

A multi-resolution textural approach to diagnostic neuropathology reporting.

We present a computer aided diagnostic workflow focusing on two diagnostic branch points in neuropathology (intraoperative consultation and p53 status in tumor biopsy specimens) by means of texture analysis via discrete wavelet frames decomposition. For intraoperative consultation, our methodology is capable of classifying glioblastoma versus metastatic cancer by extracting textural features from the non-nuclei region of cytologic preparations based on the imaging characteristics of glial processes, which appear as anisotropic thin linear structures. For metastasis, these are homogeneous in appearance, thus suitable and extractable texture features distinguish the two tissue types. Experiments on 53 images (29 glioblastomas and 24 metastases) resulted in average accuracy as high as 89.7 % for glioblastoma, 87.5 % for metastasis and 88.7 % overall. For p53 interpretation, we detect and classify p53 status by classifying staining intensity into strong, moderate, weak and negative sub-classes. We achieved this by developing a novel adaptive thresholding for detection, a two-step rule based on weighted color and intensity for the classification of positively and negatively stained nuclei, followed by texture classification to classify the positively stained nuclei into the strong, moderate and weak intensity sub-classes. Our detection method is able to correctly locate and distinguish the four types of cells, at 85 % average precision and 88 % average sensitivity rate. These classification methods on the other hand recorded 81 % accuracy in classifying the positive and negative cells, and 60 % accuracy in further classifying the positive cells into the three intensity groups, which is comparable with neuropathologists' markings.