The mammalian peptide hormone stanniocalcin 2 (STC2) plays an oncogenic role in many human cancers. However, the exact function of STC2 in human head and neck squamous cell carcinoma (HNSCC) is unclear. We aimed to examine the function and clinical significance of STC2 in HNSCC. Using in vitro and in vivo assays, we show that overexpression of STC2 suppressed cell apoptosis, promoted cell proliferation, migration, invasion, and cell cycle arrest at the G1/S transition. By contrast, silencing of STC2 inhibited these activities. We further show that STC2 upregulated the phosphorylation of AKT and enhanced HNSCC metastasis via Snail-mediated increase of vimentin and decrease of E-cadherin. These responses were blocked by silencing of STC2/Snail expression or inhibition of pAKT activity. Furthermore, clinical data indicate that high STC2 expression was associated with high levels of pAKT and Snail in tumor samples from HNSCC patients with regional lymph node metastasis (P < 0.01). Thus, we conclude that STC2 controls HNSCC metastasis via the PI3K/AKT/Snail signaling axis and that targeted therapy against STC2 may be a novel strategy to effectively treat patients with metastatic HNSCC.

Conclusion: Chinese patients have a higher rate of lymphoepithelial carcinoma (LEC) and salivary duct carcinoma (SDC). Comprehensive use of diagnostic modalities, neck dissection, and postoperative radiation will improve the treatment results for salivary gland tumors (SGTs). Objectives: To study the clinicopathological characteristics of SGTs in a Chinese population. Methods: The records of SGT patients operated in a tertiary cancer hospital of China were retrieved. Results: From December 1997 to December 2007, 289 malignant and 887 benign SGTs were operated at Cancer Hospital, Shanghai, China. Pleomorphic adenoma and Warthin's tumor were the most common types of SGT. Mucoepidermoid carcinoma (24.6% of malignant cases) and adenoid cystic carcinoma (18.0%) were the most frequent malignant cases, followed by acinic cell carcinoma (12.1%), LEC (9.7%), and SDC (9.3%). The sensitivity and specificity of ultrasound scan, fine needle aspiration biopsy, and frozen section were 58.3 and 88.6%, 87.2 and 96.7%, 86.9 and 99.6%, respectively. Neck dissections and postoperative radiation were carried out for 48.6 and 48.0% of carcinomas, respectively. The percentage of tumors by pathologic TNM stage were 23.7% for stage I, 32.9% for stage II, 17.3% for stage III, and 26.1% for stage IV. The 5-year overall survival rate was 88.0%.

Background: To explore whether deep convolutional neural networks (DCNNs) have the potential to improve diagnostic efficiency and increase the level of interobserver agreement in the classification of thyroid nodules in histopathological slides.Methods: A total of 11,715 fragmented images from 806 patients' original histological images were divided into a training dataset and a test dataset. Inception-ResNet-v2 and VGG-19 were trained using the training dataset and tested using the test dataset to determine the diagnostic efficiencies of different histologic types of thyroid nodules, including normal tissue, adenoma, nodular goiter, papillary thyroid carcinoma (PTC), follicular thyroid carcinoma (FTC), medullary thyroid carcinoma (MTC) and anaplastic thyroid carcinoma (ATC). Misdiagnoses were further analyzed.Results: The total 11,715 fragmented images were divided into a training dataset and a test dataset for each pathology type at a ratio of 5:1. Using the test set, VGG-19 yielded a better average diagnostic accuracy than did Inception-ResNet-v2 (97.34% vs. 94.42%, respectively). The VGG-19 model applied to 7 pathology types showed a fragmentation accuracy of 88.33% for normal tissue, 98.57% for ATC, 98.89% for FTC, 100% for MTC, 97.77% for PTC, 100% for nodular goiter and 92.44% for adenoma. It achieved excellent diagnostic efficiencies for all the malignant types. Normal tissue and adenoma were the most challenging histological types to classify. Conclusions:The DCNN models, especially VGG-19, achieved satisfactory accuracies on the task of differentiating thyroid tumors by histopathology. Analysis of the misdiagnosed cases revealed that normal tissue and adenoma were the most challenging histological types for the DCNN to differentiate, while all the malignant classifications achieved excellent diagnostic efficiencies. The results indicate that DCNN models may have potential for facilitating histopathologic thyroid disease diagnosis.

In cN+ PTC, especially a primary site in the inferior pole, level III and/or level IV metastasis, attention should be given to excising the nodal tissue in LNSS.