Endometrioid Adenocarcinoma Metastatic to the Thyroid, Presenting Like Anaplastic Thyroid Cancer

Metastasis of uterine cancer to the head and neck is extremely rare. We report what we believe to be the first documented case of endometrioid adenocarcinoma metastasizing to the thyroid gland. An 80-year-old woman was referred to the otolaryngology service with a rapidly growing neck mass. The mass appeared to originate from the thyroid gland. Her clinical presentation was consistent with anaplastic thyroid carcinoma. A tracheostomy was performed. An open biopsy established the diagnosis of moderately differentiated adenocarcinoma, consistent with a gynecologic primary. The patient had undergone a hysterectomy 5 years prior for endometrioid adenocarcinoma. The thyroid tumor histology and immunophenotype corresponded well with her prior endometrial carcinoma, indicating that the thyroid mass was a metastasis from the endometrial primary. Radiotherapy appears to offer good local disease control in this rare case of endometrioid adenocarcinoma metastatic to the thyroid

Automated cervical digitized histology whole-slide image analysis toolbox

Background: Cervical intraepithelial neoplasia (CIN) is regarded as a potential precancerous state of the uterine cervix. Timely and appropriate early treatment of CIN can help reduce cervical cancer mortality. Accurate estimation of CIN grade correlated with human papillomavirus type, which is the primary cause of the disease, helps determine the patient's risk for developing the disease. Colposcopy is used to select women for biopsy. Expert pathologists examine the biopsied cervical epithelial tissue under a microscope. The examination can take a long time and is prone to error and often results in high inter-and intra-observer variability in outcomes. Methodology: We propose a novel image analysis toolbox that can automate CIN diagnosis using whole slide image (digitized biopsies) of cervical tissue samples. The toolbox is built as a four-step deep learning model that detects the epithelium regions, segments the detected epithelial portions, analyzes local vertical segment regions, and finally classifies each epithelium block with localized attention. We propose an epithelium detection network in this study and make use of our earlier research on epithelium segmentation and CIN classification to complete the design of the end-to-end CIN diagnosis toolbox. Results: The results show that automated epithelium detection and segmentation for CIN classification yields comparable results to manually segmented epithelium CIN classification. Conclusion: This highlights the potential as a tool for automated digitized histology slide image analysis to assist expert pathologists

EpithNet: Deep Regression for Epithelium Segmentation in Cervical Histology Images

Background: Automated pathology techniques for detecting cervical cancer at the premalignant stage have advantages for women in areas with limited medical resources. Methods: This article presents EpithNet, a deep learning approach for the critical step of automated epithelium segmentation in digitized cervical histology images. EpithNet employs three regression networks of varying dimensions of image input blocks (patches) surrounding a given pixel, with all blocks at a fixed resolution, using varying network depth. Results: The proposed model was evaluated on 311 digitized histology epithelial images and the results indicate that the technique maximizes region-based information to improve pixel-wise probability estimates. EpithNet-mc model, formed by intermediate concatenation of the convolutional layers of the three models, was observed to achieve 94% Jaccard index (intersection over union) which is 26.4% higher than the benchmark model. Conclusions: EpithNet yields better epithelial segmentation results than state-of-the-art benchmark methods

Multiple kallikrein (KLK 5, 7, 8, and 10) expression in squamous cell carcinoma of the oral cavity

Oral squamous cell carcinoma (OSCC) represents 3% of all cancer deaths in the U.S. and is ranked one of the top 10 cancers worldwide. The 5-year survival rate has remained at a low 50% for the past several decades, necessitating discovery of novel biomarkers of aggressive disease and therapeutic targets. As overexpression of urinary type plasminogen activator and receptor (uPA/R) in OSCC is associated with malignant progression and poor outcome, cell lines were generated with either overexpression (SCC25-uPAR+) or silencing (SCC25-uPAR-KD) of uPAR. As SCC25- uPAR+ tumors behaved more aggressively both in vitro and in vivo, comparative cDNA microarray analysis was used to identify additional genes that may be associated with aggressive tumors. Four members of the human tissue kallikrein family (KLK 5, 7, 8, and 10) were identified and real-time RT-PCR (qPCR) was used to verify and quantify gene expression. qPCR analysis revealed 2.8-, 5.3-, 4.0-, and 3.5-fold increases in gene expression for KLK5, 7, 8, and 10, respectively, in SCC25-uPAR+ versus SCC25-uPAR-KD. Immunohistochemical analysis demonstrated strong reactivity for KLKs 5, 7, 8 and 10 in both orthotopic murine tumors and human OSCC tissues. Control experiments show lack of reactivity against KLK3 (prostate specific antigen). These results demonstrate that kallikreins 5, 7, 8, and 10 are abundantly expressed in human OSCC and may be implicated in malignant progression