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. Random forests (RF) models were constructed for outcome prediction with internal cross-validation in addition to the use of separate randomly selected training (70%) and testing (30%) sets. Accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were determined for predicting positive versus negative nodal status in the neck. RESULTS: Depending on the model used and subset of patients evaluated, an accuracy, sensitivity, specificity, PPV, and NPV of up to 88, 100, 67, 83, and 100%, respectively, could be achieved using multi-energy texture analysis. Texture evaluation of VMIs at 65 keV alone or in combination with only iodine maps had a much lower accuracy. CONCLUSIONS: Multi-energy DECT texture analysis of HNSCC is superior to texture analysis of 65 keV VMIs and iodine maps alone and can be used to predict cervical nodal metastases with relatively high accuracy, providing information not currently available by expert evaluation of the primary tumor alone. KEY POINTS: • Texture features of HNSCC tumor are predictive of nodal status. • Multi-energy texture analysis is superior to analysis of datasets at a single energy. • Dual-energy CT texture analysis with machine learning can enhance noninvasive diagnostic tumor evaluation.

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. Since these genes are differentially regulated during osteoblastogenesis or myogenesis, these results suggest cell- and promoter-context specific functions for αNAC. We hypothesized that αNAC dynamically recruits corepressors to inhibit Myogenin expression in cells committing to the osteoblastic lineage or to inhibit Osteocalcin transcription in differentiating myoblasts. Using co-immunoprecipitation assays, we detected complexes between αNAC and the corepressors HDAC1 and HDAC3, in myoblasts and osteoblasts. Sequential ChIP confirmed HDAC1 recruitment by αNAC at the Osteocalcin and Myogenin promoters. Interaction with the corepressors was detectable in pre-osteoblasts and in myoblasts but disappeared as the cells differentiate. Treatment with an HDAC inhibitor caused de-repression of Osteocalcin expression in myoblasts. Overexpression of αNAC in myoblasts inhibits expression of Myogenin and differentiation. However, overexpression of an N-terminus truncated αNAC mutant allowed myoblasts to express Myogenin and differentiate, and this mutant did not interact with HDAC1 or HDAC3. This study identified an additional DNA-binding target and novel protein-protein interactions for αNAC. We propose that αNAC plays a role in regulating gene transcription during mesenchymal cell differentiation by differentially recruiting corepressors at target promoters.