An integrated model incorporating deep learning, hand-crafted radiomics and clinical and US features to diagnose central lymph node metastasis in patients with papillary thyroid cancer.

Objective: To evaluate the value of an integrated model incorporating deep learning (DL), hand-crafted radiomics and clinical and US imaging features for diagnosing central lymph node metastasis (CLNM) in patients with papillary thyroid cancer (PTC).

Methods: This retrospective study reviewed 613 patients with clinicopathologically confirmed PTC from two institutions. The DL model and hand-crafted radiomics model were developed using primary lesion images and then integrated with clinical and US features selected by multivariate analysis to generate an integrated model.

A Survey on Non-Autoregressive Generation for Neural Machine Translation and Beyond.

Non-autoregressive (NAR) generation, which is first proposed in neural machine translation (NMT) to speed up inference, has attracted much attention in both machine learning and natural language processing communities. While NAR generation can significantly accelerate inference speed for machine translation, the speedup comes at the cost of sacrificed translation accuracy compared to its counterpart, autoregressive (AR) generation. In recent years, many new models and algorithms have been designed/proposed to bridge the accuracy gap between NAR generation and AR generation.

The Predictive Value of ACR TI-RADS Classification for Central Lymph Node Metastasis of Papillary Thyroid Carcinoma: A Retrospective Study.

Background: The aim of this retrospective study was to evaluate the risk factors for central lymph node metastasis (CLNM) in papillary thyroid carcinoma (PTC), according to the guidelines of the 2017 Thyroid Imaging Report and Data System (TI-RADS) published by the American College of Radiology (ACR).

Methods: This study included a retrospective analysis of 844 patients with PTC who were pathologically diagnosed, treated with central lymph node dissection, and divided into CLNM and nonmetastatic groups. Univariate and multivariate analyses were performed to determine the relationship between the TI-RADS score and CLNM.

: A potential prognostic biomarker of colorectal cancer.

Colorectal cancer (CRC) is the third most common malignancy worldwide with the second highest mortality rate. Although multidisciplinary cooperative therapies are helpful for improving the survival of CRC patients, the prognosis remains poor. Therefore, it is imperative to seek new biomarkers for the development of individualized treatment for each CRC patient.

Formin-like 3 regulates RhoC/FAK pathway and actin assembly to promote cell invasion in colorectal carcinoma.

Aim: To clarify the underlying mechanism of formin-like 3 (FMNL3) in the promotion of colorectal carcinoma (CRC) cell invasion.

Methods: The biological function analyses of FMNL3 were performed by gain- and loss-of function approaches. Changes in the F-actin cytoskeleton were detected by the technologies of phalloidin-TRITC labeling and confocal microscopy.

Increased expression of formin-like 3 contributes to metastasis and poor prognosis in colorectal carcinoma.

Formin-like 3 (FMNL3), a member of diaphanous-related formins subfamily, plays an important role in cytoskeleton reorganization, cell adhesion and cancer cell invasion in vitro. This study aimed to explore the expression of FMNL3 in colorectal carcinoma (CRC) cell-lines and tissues, and further evaluate its prognostic value and correlation with the clinicopathological parameters, and also investigate the effects of FMNL3 gene silencing on the growth and metastasis of CRC in vivo. Immunohistochemical analysis showed that FMNL3 protein was distributed in a punctuate aggregation pattern and located mainly in the cytoplasm of glandular cavity side, close to the nucleus of CRC cells.