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To determine the effect of moisture regain of wool on atmospheric pressure plasma treatment results, wool fibers and fabrics conditioned in 100% relative humidity (RH) and 65% RH were treated by an atmospheric pressure plasma jet with pure helium and helium/oxygen mixed gas, respectively. Scanning electron microscope (SEM) indicated that scales of wool fiber were smoothened for fibers conditioned in the 100% RH. X-ray photoelectron spectroscopy (XPS) showed that carbon content decreased substantially after the plasma treatment. Surface chemical composition of 100% RH conditioned groups changed more significantly than the 65% RH conditioned groups. Water contact angle decreased significantly after the plasma treatments. In shrinkage test, plasma-treated wool fabrics preconditioned in 100% RH showed the lowest shrinkage ratios of 5% and 6%, below 8% is required for machine-washable wool fabrics according to ISO standard.

Objectives: This study compared the diagnostic ability of image-based parameters with texture parameters in the differentiation of hepatocellular carcinoma (HCC) and hepatic lymphoma (HL) by positron emission tomography–computed tomography (PET/CT).Methods: Patients with pathological diagnosis of HCC and HL were included in this study. Image-based and texture parameters were obtained by manual drawing of region of interest. Receiver operating characteristic (ROC) was used to test the diagnostic capacity of each parameter. Binary logistic regression was used to transform the most discriminative image-based parameters, texture parameters, and the combination of these parameters into three regression models. ROC was used to test the diagnostic capacity of these models.Result: Ninety-nine patients diagnosed with HCC (n = 76) and HL (n = 23, 10 primary HL, 13 secondary HL) by histological examination were included in this study (From 2011 to 2018, West China hospital). According to the AUC and p-value, 2 image-based parameters and five texture parameters were selected. The diagnostic ability of texture-based model was better than that of image-based model, and after combination of those two groups of parameters the diagnostic capacity improved.Conclusion: Texture parameters can differentiate HCC from HL quantitatively and improve the diagnostic ability of image-based parameters.

Background Intrahepatic cholangiocarcinoma is an aggressive liver carcinoma with increasing incidence and mortality. A good auxiliary prognostic prediction tool is desperately needed for the development of treatment strategies. The purpose of this study was to explore the prognostic value of the radiomics nomogram based on enhanced CT in intrahepatic cholangiocarcinoma. Methods In this retrospective study, 101 patients with pathological confirmation of intrahepatic cholangiocarcinoma were recruited. A radiomics nomogram was developed by radiomics score and independent clinical risk factors selecting from multivariate Cox regression. All patients were stratified as high risk and low risk by a nomogram. Model performance and clinical usefulness were assessed by calibration curve, ROC curve, and survival curve. Results A total of 101patients (mean age, 58.2 years old; range 36–79 years old) were included in the study. The 1-year, 3-year, and 5-year overall survival rates were 49.5%, 26.6%, and 14.4%, respectively, with a median survival time of 12.2 months in the whole set. The least absolute shrinkage and selection operator (LASSO) method selected 3 features. Multivariate Cox analysis found three independent prognostic factors. The radiomics nomogram showed a significant prognosis value with overall survival. There was a significant difference in the 1-year and 3-year survival rates of stratified high-risk and low-risk patients in the whole set (30.4% vs. 56.4% and 13.0% vs. 30.6%, respectively, p = 0.018). Conclusions This radiomics nomogram has potential application value in the preoperative prognostic prediction of intrahepatic cholangiocarcinoma and may facilitate in clinical decision-making.

PurposeTo evaluate the value of multiple machine learning methods in classifying pathological grades (G1,G2, and G3), and to provide the best machine learning method for the identification of pathological grades of pancreatic neuroendocrine tumors (PNETs) based on radiomics.Materials and MethodsA retrospective study was conducted on 82 patients with Pancreatic Neuroendocrine tumors. All patients had definite pathological diagnosis and grading results. Using Lifex software to extract the radiomics features from CT images manually. The sensitivity, specificity, area under the curve (AUC) and accuracy were used to evaluate the performance of the classification model.ResultOur analysis shows that the CT based radiomics features combined with multi algorithm machine learning method has a strong ability to identify the pathological grades of pancreatic neuroendocrine tumors. DC + AdaBoost, DC + GBDT, and Xgboost+RF were very valuable for the differential diagnosis of three pathological grades of PNET. They showed a strong ability to identify the pathological grade of pancreatic neuroendocrine tumors. The validation set AUC of DC + AdaBoost is 0.82 (G1 vs G2), 0.70 (G2 vs G3), and 0.85 (G1 vs G3), respectively.ConclusionIn conclusion, based on enhanced CT radiomics features could differentiate between different pathological grades of pancreatic neuroendocrine tumors. Feature selection method Distance Correlation + classifier method Adaptive Boosting show a good application prospect.

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