Pancreatic ductal adenocarcinoma (PDAC) is one of the most fatal human cancers due to its complicated genomic instability. PDAC frequently presents at an advanced stage with extensive metastasis, which portends a poor prognosis. The known risk factors associated with PDAC include advanced age, smoking, long-standing chronic pancreatitis, obesity, and diabetes. Its association with genomic and somatic mutations is the most important factor for its aggressiveness. The most common gene mutations associated with PDAC include KRas2, p16, TP53, and Smad4. Among these, Smad4 mutation is relatively specific and its inactivation is found in more than 50% of invasive pancreatic adenocarcinomas. Smad4 is a member of the Smad family of signal transducers and acts as a central mediator of transforming growth factor beta (TGF-β) signaling pathways. The TGF-β signaling pathway promotes many physiological processes, including cell growth, differentiation, proliferation, fibrosis, and scar formation. It also plays a major role in the development of tumors through induction of angiogenesis and immune suppression. In this review, we will discuss the molecular mechanism of TGF-β/Smad4 signaling in the pathogenesis of pancreatic adenocarcinoma and its clinical implication, particularly potential as a prognostic factor and a therapeutic target.

Combined hepatocellular-cholangiocarcinoma (CHC) is a rare tumor with poor prognosis, with incidence ranging from 1.0%-4.7% of all primary hepatic tumors. This entity will be soon renamed as hepato-cholangiocarcinoma. The known risk factors for hepatocellular carcinoma (HCC) have been implicated for CHC including viral hepatitis and cirrhosis. It is difficult to diagnose this tumor pre-operatively. The predominant histologic component within the tumor largely determines the predominant radiographic features making it a difficult distinction. Heterogeneous and overlapping imaging features of HCC and cholangiocarcinoma should raise the suspicion for CHC and multiple core biopsies (from different areas of tumor) are recommended before administering treatment. Serum tumor markers CA19-9 and alpha-fetoprotein can aid in the diagnosis, but it remains a challenging diagnosis prior to resection. There is sufficient data to support bipotent hepatic progenitor cells as the cell of origin for CHC. The current World Health Organization classification categorizes two main types of CHC based on histo-morphological features: Classical type and CHC with stem cell features. Liver transplant is one of the available treatment modalities with other management options including transarterial chemoembolization, radiofrequency ablation, and percutaneous ethanol injection. We present a review paper on CHC highlighting the risk factors, origin, histological classification and therapeutic modalities.

Blastic plasmacytoid dendritic cell neoplasm (BPDCN) derived from precursors of plasmacytoid dendritic cells is a very rare, unique, and highly aggressive immature hematopoietic malignancy, more frequently occurring among healthy elderly adults. BPDCN can be characterized by a striking predilection for cutaneous involvement, which is often detected incidentally by dermatologists and is difficult to clinically distinguish it from other primary skin lesions and histologically from leukemia/lymphoma cutis. Thus, histological diagnosis of cutaneous biopsies is crucial to correctly classify this entity. Most patients eventually progress to acute myeloid leukemia and are generally not curable. Here, we present 2 cases of classic BPDCN and discuss the origin of tumor and literature-based characteristic clinical and morphological features, evolving immunomarkers, and molecular genetic aspects of this neoplasm.

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Background:Recent studies show various cytomorphologic features that can assist in the differentiation of classic papillary thyroid carcinoma (cPTC) from noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP). Differentiating these two entities changes the clinical management significantly. We evaluated the performance of support vector machine (SVM), a machine learning algorithm, in differentiating cases of NIFTP and encapsulated follicular variant of papillary thyroid carcinoma with no capsular or lymphovascular invasion (EFVPTC) from cases of cPTC with the use of microscopic descriptions. SVM is a supervised learning algorithm used in classification problems. It assigns the input data to one of two categories by building a model based on a set of training examples (learning) and then using that learned model to classify new examples.Methods:Surgical pathology cases with the diagnosis of cPTC, NIFTP, and EFVPTC, were obtained from the laboratory information system. Only cases with existing fine-needle aspiration matching the tumor and available microscopic description were included. NIFTP cases with ipsilateral micro-PTC were excluded. The final cohort consisted of 59 cases (29 cPTCs and 30 NIFTP/EFVPTCs).Results:SVM successfully differentiated cPTC from NIFTP/EFVPTC 76.05 ± 0.96% of times (above chance, P < 0.05) with the sensitivity of 72.6% and specificity of 81.6% in detecting cPTC.Conclusions:This machine learning algorithm was successful in distinguishing NIFTP/EFVPTC from cPTC. Our results are compatible with the prior studies, which show cytologic features are helpful in differentiating these two entities. Furthermore, this study shows the power and potential of this approach for clinical use and in developing data-driven scoring systems, which can guide cytopathology and surgical pathology diagnosis.