Search

Accurate prediction of the expression of programmed death ligand 1 (PD-L1) in head and neck squamous cell carcinoma (HNSCC) before immunotherapy is crucial. This study was performed to construct and validate a contrast-enhanced computed tomography (CECT)-based radiomics signature to predict the expression of PD-L1 in HNSCC.In total, 157 patients with confirmed HNSCC who underwent CECT scans and immunohistochemical examination of tumor PD-L1 expression were enrolled in this study. The patients were divided into a training set (n = 104; 62 PD-L1-positive and 42 PD-L1-negative) and an external validation set (n = 53; 34 PD-L1-positive and 19 PD-L1-negative). A radiomics signature was constructed from radiomics features extracted from the CECT images, and a radiomics score was calculated. Performance of the radiomics signature was assessed using receiver operating characteristics analysis.Nine features were finally selected to construct the radiomics signature. The performance of the radiomics signature to distinguish between a PD-L1-positive and PD-L1-negative status in both the training and validation sets was good, with an area under the receiver operating characteristics curve of 0.852 and 0.802 for the training and validation sets, respectively.A CECT-based radiomics signature was constructed to predict the expression of PD-L1 in HNSCC. This model showed favorable predictive efficacy and might be useful for identifying patients with HNSCC who can benefit from anti-PD-L1 immunotherapy.• Accurate prediction of the expression of PD-L1 in HNSCC before immunotherapy is crucial. • A CECT-based radiomics signature showed favorable predictive efficacy in estimation of the PD-L1 expression status in patients with HNSCC.

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.

BackgroundAcute intracranial hypertension (IH) is commonly found in patients with cerebral venous thrombosis (CVT). Some magnetic resonance imaging (MRI) findings that are suggestive of acute IH an...

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.

Accurate prediction of the expression level of programmed death ligand 1 (PD-L1) in head and neck squamous cell carcinoma (HNSCC) is crucial before immunotherapy. The purpose of this study was to construct and validate a contrast-enhanced computed tomography (CECT)-based radiomics signature to discriminate between high and low expression status of PD-L1.A total of 179 HNSCC patients who underwent immunohistochemical examination of tumor PD-L1 expression at one of two centers were enrolled in this study and divided into a training set (n = 122; 55 high PD-L1 expression and 67 low PD-L1 expression) and an external validation set (n = 57; 26 high PD-L1 expression and 31 low PD-L1 expression). The least absolute shrinkage and selection operator method was used to select the key features for a CECT-image-based radiomics signature. The performance of the radiomics signature was assessed using receiver operating characteristics analysis.Six features were finally selected to construct the radiomics signature. The performance of the radiomics signature in the discrimination between high and low PD-L1 expression status was good in both the training and validation sets, with areas under the receiver operating characteristics curve of 0.889 and 0.834 for the training and validation sets, respectively.The constructed CECT-based radiomics signature model showed favorable performance for discriminating between high and low PD-L1 expression status in HNSCC patients. It may be useful for screening out those patients with HNSCC who can best benefit from anti-PD-L1 immunotherapy.

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.

The Leishan-Rongjiang antimony ore field (LAOF) is in a unique geotectonic location in the uplift between the Youjiang and Xiangzhong basins. This paper focuses on two representative deposits in the LAOF: the Bameng and Peize antimony (Sb) deposits. We analyzed fluid inclusions (FIs) in stibnite and coexisting quartz, as well as the sulfur isotopic composition of stibnite, to better understand the nature of the ore-forming fluid and the metallogenic process. The FIs data from samples of the stibnite and coexisting quartz indicate that the ore-forming fluids were characterized by low-temperature (150–210 °C), low-salinity (1.5 wt%–6.0 wt% NaCl equiv.), and low-density (0.872–0.961 g/cm3). The δ34S values of stibnite (−8.21‰ to 3.76‰, average = −6.30‰) fall in between the sulfur isotopic compositions of the mantle and of biogenic sulfur in sedimentary rocks. However, the δ34S∑S values (−4.41‰ to +0.04‰, average = −2.49‰) of the ore-forming fluids are generally closer to the sulfur isotopic composition of the mantle source, indicating that the sulfur in the LAOF was mainly sourced from the mantle, but with possible involvement of biogenic sulfur. In addition, FIs petrography and ore deposit geology show that fluid boiling resulted from an abrupt decrease in pressure, which may have triggered the precipitation of stibnite. We conclude that low-temperature, dilute hydrothermal fluids with mixed origins migrated along the regional fault and interacted with the wall rock, extracting the ore-forming materials. Then, the ore-forming fluids were injected into the fault fracture zones.