Head and neck squamous cell carcinoma: prediction of cervical lymph node metastasis by dual-energy CT texture analysis with machine learning.

Objectives: This study was conducted in order to evaluate a novel risk stratification model using dual-energy CT (DECT) texture analysis of head and neck squamous cell carcinoma (HNSCC) with machine learning to (1) predict associated cervical lymphadenopathy and (2) compare the accuracy of spectral versus single-energy (65 keV) texture evaluation for endpoint prediction.

Methods: Eighty-seven patients with HNSCC were evaluated. Texture feature extraction was performed on virtual monochromatic images (VMIs) at 65 keV alone or different sets of multi-energy VMIs ranging from 40 to 140 keV, in addition to iodine material decomposition maps and other clinical information.

αNAC interacts with histone deacetylase corepressors to control Myogenin and Osteocalcin gene expression.

In the nucleus of differentiated osteoblasts, the DNA-binding αNAC protein acts as a transcriptional coactivator of the Osteocalcin gene. Chromatin immunoprecipitation-microarray assays (ChIP-chip) showed that αNAC binds the Osteocalcin promoter but also identified the Myogenin promoter as an αNAC target. Here, we confirm these array data using quantitative ChIP and further detected that αNAC binds to these promoters in myoblasts.