Faculty Bibliography

BACKGROUND:The aim of this study is to assess the efficacy of cytology in omental or peritoneal lesions. METHODS:A retrospective review of the pathology database for cytology cases of peritoneal or omental nodules over a 3-year period (2016-2018) was conducted. The cases consisted of either FNA only (FO); FNA and Core biopsy (FCB) or Touch prep and core biopsy (TCB). Cases were further divided based on the prior history of carcinoma. Concordance rates of cytologic diagnosis with histologic diagnosis were studied. RESULTS:Out of 104 cytology cases reviewed, 60 (57.7%) had prior history of cancer (PHC) and 44 (42.3%) had no prior history of cancer (NPHC). Of the cases with PHC, 43(71.66%) were recurrence, 10 (16.66%) were second cancer, and 7 (11.66%) were non-neoplastic lesions. Of the cases with NPHC, 38 (86.4%) had a second cancer diagnosis, while 6 (13.6%) were non-neoplastic. For FO only cases, 11 of 35 (31.4%) had follow up and 9 of 11 (81.8%) were concordant. For FCB cases, 6 out of 39 (15.4%) had follow up and 6 (100%) were concordant. For TCB cases, 9 out of 30 (30%) had follow up and 9 (100%) were concordant. A definite diagnosis was reached in 30/35, 39/39, and 29/30 cases in FO, FCB, and TCB, respectively. CONCLUSION/CONCLUSIONS:In summary, cytologic evaluation of omental lesions is an effective tool in providing accurate diagnosis and guiding further management. Also, the results based on our study show that the combined techniques are superior at reaching a definitive diagnosis.

Colorectal cancer (CRC) is the most common malignancy of the gastrointestinal tract. The stroma and the tumoral microenvironment (TME) represent ecosystem-like biological networks and are new frontiers in CRC. The present study demonstrates the use of a novel machine learning-based superpixel approach for whole slide images to unravel this biology. Findings of significance include the association of low proportionated stromal area, high immature stromal percentage, and high myxoid stromal ratio (MSR) with worse prognostic outcomes in CRC. Overall, stromal computational markers outperformed all others at predicting clinical outcomes. MSR may be able to prognosticate patients independent of pathological stage, representing an optimal way to effectively prognosticate CRC patients which circumvents the need for more extensive molecular and/or computational profiling. The superpixel approaches to the TME demonstrated here can be performed by a trained pathologist and recorded during synoptic cancer reporting with appropriate quality assurance. Future clinical trials will have the ultimate say in determining whether we can better tailor the need for adjuvant therapy in patients with CRC.

The role of stromal differentiation (SD), program death-ligand 1 (PD-L1), and v-domain Ig suppressor of T cell activation (VISTA) in gastrointestinal stromal tumor (GIST) is largely unknown. Looking forward, the assessment of SD and immune check point inhibition will become more ubiquitous in surgical pathology. Immature, myxoid stroma has been found to be a poor prognostic signature in many cancer subtypes (colon, breast, cervix, esophagus, stomach); although little is known regarding its significance in GIST. For immune check-point inhibition, studies have demonstrated expression to be associated with patient outcomes in numerous cancer subtypes. The present body of work aims to evaluate SD, PD-L1 and VISTA; both in terms of its nature and significance in a clinical setting. Here we found PD-L1 expression in immune cells (IC) and immature SD to be associated with worse cancer free survival, while positive VISTA expression was found to be associated with improved outcomes. High-grade, immature SD had the highest propensity for death/recurrence and was the only variable found to have prognostic significance on multivariate analysis. Our findings support the evaluation of SD, PD-L1 and VISTA in GIST, with clinical practice implications for pathologists. Ultimately, we hope our findings lead to improved prognostication, further optimization of therapeutics, and improved outcomes in a true clinical environment. For GIST, PD-L1 and VISTA could be both clinically relevant and targetable, while SD may be the answer to clinical heterogeneity.

Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly spread worldwide. Most of the infected patients present with respiratory symptoms and acute lung damage. Here, we present three cases of patients with COVID-19 disease whose main clinical manifestations are gastrointestinal symptoms. In our first case, we present a COVID-19 patient with histologic findings associated with ischemic necrosis of the small bowel. In the second and third cases, we demonstrate acute cholecystitis and histology showing microvascular thrombosis. These three cases highlight the ischemic and thrombotic changes seen in the setting of COVID-19 infection without classic respiratory symptoms, with resulting severe gastrointestinal and hepatobiliary disease requiring surgical management. Although the bile or stool viral load was not tested in these patients, the small intestine and gallbladder were infected with SARS-CoV-2, most likely via the epithelial angiotensin-converting enzyme 2 (ACE2) receptor.

Artificial intelligence (AI) is a new frontier and often enigmatic for medical professionals. Cloud computing could open up the field of computer vision to a wider medical audience and deep learning on the cloud allows one to design, develop, train and deploy applications with ease. In the field of histopathology, the implementation of various applications in AI has been successful for whole slide images rich in biological diversity. However, the analysis of other tissue medias, including electron microscopy, is yet to be explored. The present study aims to evaluate deep learning for the classification of medical kidney disease on electron microscopy images: amyloidosis, diabetic glomerulosclerosis, membranous nephropathy, membranoproliferative glomerulonephritis (MPGN), and thin basement membrane disease (TBMD). We found good overall classification with the MedKidneyEM-v1 Classifier and when looking at normal and diseased kidneys, the average area under the curve for precision and recall was 0.841. The average area under the curve for precision and recall on the disease only cohort was 0.909. Digital pathology will shape a new era for medical kidney disease and the present study demonstrates the feasibility of deep learning for electron microscopy. Future approaches could be used by renal pathologists to improve diagnostic concordance, determine therapeutic strategies, and optimize patient outcomes in a true clinical environment.

Cancer comprises epithelial tumor cells and associated stroma, often times referred to as the "tumoral microenvironment". Cancer-associated fibroblasts (CAFs) are the most notable components of the tumor mesenchyme. CAFs promote the initiation of cancer through angiogenesis, invasion and metastasis. Histologically, the differentiation of stroma has been reported to correlate with prognostic outcomes in patients with colorectal cancer. This review summarizes our current understanding of the extracellular matrix (ECM) in colorectal carcinoma (CRC), showcasing the functions of CAFs and its role in stromal differentiation (SD). We also review current state-of-the-art biology, histopathology, computation, and genomics in the setting of the stroma. SD is distinctive morphologically, and is easily recognized by a surgical pathologist; we offer a lexicon and guide for discovering the essence of stroma, as well as an incipient vision of the future for computation and molecular genomics. We propose that the mesenchymal phenotype, which encompasses a cancer migratory/metastatic capacity, could occur through the process of SD. Looking forward, pathologists will need to invest time and energy into SD, embracing the concept and propagating its use. For patients with colorectal cancer, stroma is a brave new frontier, one not only rich in biologic diversity, but also potentially critical for therapeutic decision making.

Endometrial cancer (EC) is a disease with good and poor prognostic subtypes. Dedifferentiated endometrial carcinoma (DEC), undifferentiated endometrial carcinoma (UEC), and clear cell endometrial carcinoma (CEC) are rare high-grade tumors, associated with a poor prognosis and high pathologic stage. Many studies have been performed on the programmed death-ligand 1 (PD-L1) axis mainly focus on endometrioid adenocarcinomas and little research has been done on rare subtypes. The present body of work aims to evaluate the role of indoleamine-2,3-dioxygenase (IDO-1) and stromal differentiation (SD), their correlation with clinicopathologic features and overall survival. Here we found that positive IDO-1 expression in immune cells correlated with worse disease-free survival (p = 0.02), recurrence (p = 0.03), high pathologic tumor stage (p = 0.024), lymph node metastasis (p = 0.028), and myometrial invasion (p = 0.03). Our findings suggest IDO-1 to be relevant in both MMR intact and deficient tumors; however, >20% immune cell staining was restricted to MMR deficient cancers. For the stroma, immature, myxoid differentiation was found to correlate with worse disease-free survival (p = 0.04). We also found the correlation between IDO-1 expression and immature stroma. Looking forward, IDO-1 could be promising for immunotherapy and SD could be the answer to clinical heterogeneity.

Colorectal cancer (CRC) is the third most common cause of cancer related death in the United States (Jasperson et al. in Gastroenterology 138:2044-2058, https://doi.org/10.1053/j.gastro.2010.01.054 , 2010). Many studies have explored prognostic factors in CRC. Today, much focus has been placed on the tumor microenvironment, including different immune cells and the extracellular matrix (ECM). The present study aims to evaluate the role of V-domain immunoglobulin suppressor of T cell activation (VISTA). We utilized QuPath for whole slides image analysis, performing superpixel image segmentation (SIS) on a 226 patient-cohort. High VISTA expression correlated with better disease-free survival (DFS), high tumor infiltrative lymphocyte, microsatellite instability, BRAF mutational status as well as lower tumor stage. High VISTA expression was also associated with mature stromal differentiation (SD). When cohorts were separated based on SD and MMR, only patients with immature SD and microsatellite stability were found to correlate VISTA expression with DFS. Considering raised VISTA expression is associated with improved survival, TILs, mature SD, and MMR in CRC; careful, well-designed clinical trials should be pursued which incorporate the underlying tumoral microenvironment.