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Accurate pretreatment assessment of histological differentiation grade of head and neck squamous cell carcinoma (HNSCC) is crucial for prognosis evaluation. This study aimed to construct and validate a contrast-enhanced computed tomography (CECT)-based deep learning radiomics nomogram (DLRN) to predict histological differentiation grades of HNSCC.A total of 204 patients with HNSCC who underwent CECT scans were enrolled in this study. The participants recruited from two hospitals were split into a training set (n=124, 74 well/moderately differentiated and 50 poorly differentiated) of patients from one hospital and an external test set of patients from the other hospital (n=80, 49 well/moderately differentiated and 31 poorly differentiated). CECT-based manually-extracted radiomics (MER) features and deep learning (DL) features were extracted and selected. The selected MER features and DL features were then combined to construct a DLRN via multivariate logistic regression. The predictive performance of the DLRN was assessed using ROCs and decision curve analysis (DCA).Three MER features and seven DL features were finally selected. The DLRN incorporating the selected MER and DL features showed good predictive value for the histological differentiation grades of HNSCC (well/moderately differentiated vs. poorly differentiated) in both the training (AUC, 0.878) and test (AUC, 0.822) sets. DCA demonstrated that the DLRN was clinically useful for predicting histological differentiation grades of HNSCC.A CECT-based DLRN was constructed to predict histological differentiation grades of HNSCC. The DLRN showed good predictive efficacy and might be useful for prognostic evaluation of patients with HNSCC.

Objective： Preoperative differentiation between parotid Warthin’s tumor (WT) and pleomorphic adenoma (PMA) is crucial for treatment decisions. The purpose of this study was to establish and validate an MRI-based radiomics nomogram for preoperative differentiation between WT and PMA. Methods and materials: A total of 127 patients with histological diagnosis of WT or PMA from two clinical centres were enrolled in training set (n = 75; WT = 34, PMA = 41) and external test set (n = 52; WT = 24, PMA = 28). Radiomics features were extracted from axial T1WI and fs-T2WI images. A radiomics signature was constructed, and a radiomics score (Rad-score) was calculated. A clinical factors model was built using demographics and MRI findings. A radiomics nomogram combining the independent clinical factors and Rad-score was constructed. The receiver operating characteristic analysis was used to assess the performance levels of the nomogram, radiomics signature and clinical model. Results: The radiomics nomogram incorporating the age and radiomics signature showed favourable predictive value for differentiating parotid WT from PMA, with AUCs of 0.953 and 0.918 for the training set and test set, respectively. Conclusions: The MRI-based radiomics nomogram had good performance in distinguishing parotid WT from PMA, which could optimize clinical decision-making.

The Qinglong deposit is a newly-characterized, large Sb-(Au) deposit in the Youjiang basin (Guizhou, SW China). Four mineralization stages are identified (i.e., pre-ore, early-ore, late-ore and post-ore) in this study based on crosscutting relationships. Our study shows that the Qinglong Sb mineralization (early-ore stage) was dominated by quartz-stibnite ± fluorite, whilst the Sb-Au mineralization (late-ore stage) comprises quartz-stibnite ± pyrite. Electron Probe Microanalysis (EPMA) indicates that the Au is present as Au1+ in the arsenian pyrite. Besides, our new fluid inclusion data from fluorite, stibnite and quartz show that the Sb-Au-rich hydrothermal fluids were derived from the mixing fluids consisting of H2O, NaCl, hydrocarbon and minor CO2. In the early-ore stage, the ore-forming fluids are characterized by medium to low temperatures (161–294 °C, mean: 220 °C), medium to low salinities (0.35–13.18 wt% NaCl equiv.), low pH, and the presence of hydrocarbons and CO2. In comparison, the late-ore stage ore-forming fluids are featured by low temperatures (113–255 °C, mean: 175 °C), low salinities (0.18–6.30 wt% NaCl equiv.) and nearly neutral pH. The δ34S∑S values (−2.8 to 2.3‰) of the ore-forming fluids suggest that the sulfur at Qinglong was dominantly magmatic-derived. From the early-ore to late-ore stage, the ore-forming fluid temperature and oxygen fugacity (fO2) decrease but the pH increases (from acidic to neutral). Such physicochemical transition may have significantly decreased the Sb solubility from 10 s to 100 s of ppm to 0.001 ppm. Therefore, we propose that fluid mixing was the main process that substantially reduced the fluid temperature and oxygen fugacity, which effectively lowered the Sb solubility and led to abundant stibnite precipitation. The stibnite precipitation had likely decreased the H2S concentrations of the ore-forming fluids, thereby destabilizing the AuHS0 complexes in the fluids and caused local Au precipitation.