A nomogram based on dual-layer detector spectral computed tomography quantitative parameters and morphological quantitative indicator for distinguishing metastatic and nonmetastatic regional lymph nodes in pancreatic ductal adenocarcinoma.

Background: There is no unified scope for regional lymph node (LN) dissection in patients with pancreatic ductal adenocarcinoma (PDAC). Incomplete regional LN dissection can lead to postoperative recurrence, while blind expansion of the scope of regional LN dissection significantly increases the perioperative risk without significantly prolonging overall survival. We aimed to establish a noninvasive visualization tool based on dual-layer detector spectral computed tomography (DLCT) to predict the probability of regional LN metastasis in patients with PDAC. Methods: A total of 163 regional LNs were reviewed and divided into a metastatic cohort (n=58 LNs) and nonmetastatic cohort (n=105 LNs). The DLCT quantitative parameters and the nodal ratio of the longest axis to the shortest axis (L/S) of the regional LNs were compared between the two cohorts. The DLCT quantitative parameters included the iodine concentration in the arterial phase (APIC), normalized iodine concentration in the arterial phase (APNIC), effective atomic number in the arterial phase (APZeff), normalized effective atomic number in the arterial phase (APNZeff), slope of the spectral attenuation curves in the arterial phase (APλHU), iodine concentration in the portal venous phase (PVPIC), normalized iodine concentration in the portal venous phase (PVPNIC), effective atomic number in the portal venous phase (PVPZeff), normalized effective atomic number in the portal venous phase (PVPNZeff), and slope of the spectral attenuation curves in the portal venous phase (PVPλHU). Logistic regression analysis based on area under the curve (AUC) was used to analyze the diagnostic performance of significant DLCT quantitative parameters, L/S, and the models combining significant DLCT quantitative parameters and L/S. A nomogram based on the models with highest diagnostic performance was developed as a predictor. The goodness of fit and clinical applicability of the nomogram were assessed through calibration curve and decision curve analysis (DCA). Results: The combined model of APNIC + L/S (APNIC + L/S) had the highest diagnostic performance among all models, yielding an AUC, sensitivity, and specificity of 0.878 [95% confidence interval (CI): 0.825-0.931], 0.707, and 0.886, respectively. The calibration curve indicated that the APNIC-L/S nomogram had good agreement between the predicted probability and the actual probability. Meanwhile, the decision curve indicated that the APNIC-L/S nomogram could produce a greater net benefit than could the all- or-no-intervention strategy, with threshold probabilities ranging from 0.0 to 0.75. Conclusions: As a valid and visual noninvasive prediction tool, the APNIC-L/S nomogram demonstrated favorable predictive efficacy for identifying metastatic LNs in patients with PDAC.

Differentiation of benign and malignant lesions in Bethesda III and IV thyroid nodules via dual-energy computed tomography quantitative parameters and morphologic features.

Background: Thyroid nodules (TNs) cytologically defined as category Bethesda III and IV pose a major diagnostic challenge before surgery, demanding new methods to reduce unnecessary diagnostic thyroid lobectomies for patients with benign TNs. This study aimed to assess whether a model combining dual-energy computed tomography (DECT) quantitative parameters with morphologic features could reliably differentiate between benign and malignant lesions in Bethesda III and IV TNs. Methods: Data from 77 patients scheduled for thyroid surgery for Bethesda III and IV TNs (malignant =48; benign =29) who underwent DECT scans were reviewed. DECT quantitative parameters including normalized iodine concentration (NIC), attenuation on the slope of spectral Hounsfield unit (HU) curve, and normalized effective atomic number (Zeff) were measured in the arterial phase (AP) and venous phase (VP). DECT quantitative parameters and morphologic features were compared between the malignant and benign cohorts. The receiver operating characteristic curve was performed to compare the performances of significant DECT quantitative parameters, morphologic features, or the models combining the DECT parameters, respectively, with morphologic features. A nomogram was constructed from the optimal performance model, and the performance was evaluated via the calibration curve and decision curve analysis. Results: The areas under the receiver operating characteristic curve with 95% confidence interval (CI) of the NIC in the AP (AP-NIC), slope of spectral HU curve in the AP, and NZeff in the AP were 0.749 (95% CI: 0.641-0.857), 0.654 (95% CI: 0.530-0.778), and 0.722 (95% CI: 0.602-0.842), respectively. The model combining AP-NIC with enhanced blurring showed the highest diagnostic performance, with an area under the receiver operating characteristic curve (AUC), sensitivity, and specificity of 0.808, 0.854, and 0.655, respectively; it was then used to construct a nomogram. The calibration curve showed that the discrepancy between the prediction of the nomogram and actual observations was less than 5%. The decision curve analysis indicated the nomogram had a positive net benefit in threshold risk ranges of 14% to 58% or 60% to 91% for malignant Bethesda III and IV TNs. Conclusions: The model combining AP-NIC with enhanced blurring could reliably differentiate between benign and malignant lesions in Bethesda III and IV TNs.

Detection of malignant lesions in cytologically indeterminate thyroid nodules using a dual-layer spectral detector CT-clinical nomogram.

Purpose: To evaluate the capability of dual-layer detector spectral CT (DLCT) quantitative parameters in conjunction with clinical variables to detect malignant lesions in cytologically indeterminate thyroid nodules (TNs). Materials and methods: Data from 107 patients with cytologically indeterminate TNs who underwent DLCT scans were retrospectively reviewed and randomly divided into training and validation sets (7:3 ratio). DLCT quantitative parameters (iodine concentration (IC), NICP (IC nodule/IC thyroid parenchyma), NICA (IC nodule/IC ipsilateral carotid artery), attenuation on the slope of spectral HU curve and effective atomic number), along with clinical variables, were compared between benign and malignant cohorts through univariate analysis. Multivariable logistic regression analysis was employed to identify independent predictors which were used to construct the clinical model, DLCT model, and combined model. A nomogram was formulated based on optimal performing model, and its performance was assessed using receiver operating characteristic curve, calibration curve, and decision curve analysis. The nomogram was subsequently tested in the validation set. Results: Independent predictors associated with malignant TNs with indeterminate cytology included NICP in the arterial phase, Hashimoto's Thyroiditis (HT), and BRAF V600E (all p < 0.05). The DLCT-clinical nomogram, incorporating the aforementioned variables, exhibited superior performance than the clinical model or DLCT model in both training set (AUC: 0.875 vs 0.792 vs 0.824) and validation set (AUC: 0.874 vs 0.792 vs 0.779). The DLCT-clinical nomogram demonstrated satisfactory calibration and clinical utility in both training set and validation set. Conclusion: The DLCT-clinical nomogram emerges as an effective tool to detect malignant lesions in cytologically indeterminate TNs.

Development and validation of a model for predicting the expression of Ki-67 in pancreatic ductal adenocarcinoma with radiological features and dual-energy computed tomography quantitative parameters.

OBJECTIVE: To construct and validate a model based on the dual-energy computed tomography (DECT) quantitative parameters and radiological features to predict Ki-67 expression levels in pancreatic ductal adenocarcinoma (PDAC). MATERIALS AND METHODS: Data from 143 PDAC patients were analysed. The variables of clinic, radiology and DECT were evaluated. In the arterial phase and portal venous phase (PVP), the normalized iodine concentration (NIC), normalized effective atomic number and slope of the spectral attenuation curves were measured. The extracellular volume fraction (ECVf) was measured in the equilibrium phase. Univariate analysis was used to screen independent risk factors to predict Ki-67 expression. The Radiology, DECT and DECT-Radiology models were constructed, and their diagnostic effectiveness and clinical applicability were obtained through area under the curve (AUC) and decision curve analysis, respectively. The nomogram was established based on the optimal model, and its goodness-of-fit was assessed by a calibration curve. RESULTS: Computed tomography reported regional lymph node status, NIC of PVP, and ECVf were independent predictors for Ki-67 expression prediction. The AUCs of the Radiology, DECT, and DECT-Radiology models were 0.705, 0.884, and 0.905, respectively, in the training cohort, and 0.669, 0.835, and 0.865, respectively, in the validation cohort. The DECT-Radiology nomogram was established based on the DECT-Radiology model, which showed the highest net benefit and satisfactory consistency. CONCLUSIONS: The DECT-Radiology model shows favourable predictive efficacy for Ki-67 expression, which may be of value for clinical decision-making in PDAC patients. CRITICAL RELEVANCE STATEMENT: The DECT-Radiology model could contribute to the preoperative and non-invasive assessment of Ki-67 expression of PDAC, which may help clinicians to screen out PDAC patients with high Ki-67 expression. KEY POINTS: • Dual-energy computed tomography (DECT) can predict Ki-67 in pancreatic ductal adenocarcinoma (PDAC). • The DECT-Radiology model facilitates preoperative and non-invasive assessment of PDAC Ki-67 expression. • The nomogram may help screen out PDAC patients with high Ki-67 expression.

Development of a CT radiomics nomogram for preoperative prediction of Ki-67 index in pancreatic ductal adenocarcinoma: a two-center retrospective study.

OBJECTIVES: To develop and validate a contrast-enhanced computed tomography (CECT)-based radiomics nomogram for the preoperative evaluation of Ki-67 proliferation status in pancreatic ductal adenocarcinoma (PDAC). METHODS: In this two-center retrospective study, a total of 181 patients (95 in the training cohort; 42 in the testing cohort, and 44 in the external validation cohort) with PDAC who underwent CECT examination were included. Radiomic features were extracted from portal venous phase images. The radiomics signatures were built by using two feature-selecting methods (relief and recursive feature elimination) and four classifiers (support vector machine, naive Bayes, linear discriminant analysis (LDA), and logistic regression (LR)). Multivariate LR was used to build a clinical model and radiomics-clinical nomogram. The predictive performances of the models were evaluated using area under receiver operating characteristic curve (AUC) and decision curve analysis (DCA). RESULTS: The relief selector and LDA classifier using twelve features built the optimal radiomics signature, with AUCs of 0.948, 0.927, and 0.824 in the training, testing, and external validation cohorts, respectively. The radiomics-clinical nomogram incorporating the optimal radiomics signature, CT-reported lymph node status, and CA19-9 showed better predictive performance with AUCs of 0.976, 0.955, and 0.882 in the training, testing, and external validation cohorts, respectively. The calibration curve and DCA demonstrated goodness-of-fit and improved benefits in clinical practice of the nomogram. CONCLUSIONS: The radiomics-clinical nomogram is an effective and non-invasive computer-aided tool to predict the Ki-67 expression status in patients with PDAC. CLINICAL RELEVANCE STATEMENT: The radiomics-clinical nomogram is an effective and non-invasive computer-aided tool to predict the Ki-67 expression status in patients with pancreatic ductal adenocarcinoma. KEY POINTS: The radiomics analysis could be helpful to predict Ki-67 expression status in patients with pancreatic ductal adenocarcinoma (PDAC). The radiomics-clinical nomogram integrated with the radiomics signature, clinical data, and CT radiological features could significantly improve the differential diagnosis of Ki-67 expression status. The radiomics-clinical nomogram showed satisfactory calibration and net benefit for discriminating high and low Ki-67 expression status in PDAC.

Diagnostic accuracy of dual-energy computed tomography-based nomogram for differentiating papillary thyroid microcarcinomas from micronodular goiters.

Background: The misdiagnosis of papillary thyroid microcarcinoma (PTMC) and micronodular goiter (MNG) may lead to overtreatment and unnecessary medical expenditure by patients. This study developed and validated a dual-energy computed tomography (DECT)-based nomogram for the preoperative differential diagnosis of PTMC and MNG. Methods: This retrospective study analyzed the data of 366 pathologically confirmed thyroid micronodules, of which 183 were PTMCs and 183 were MNGs, from 326 patients who underwent DECT examinations. The cohort was divided into the training (n=256) and validation cohorts (n=110). The conventional radiological features and DECT quantitative parameters were analyzed. The iodine concentration (IC), normalized iodine concentration (NIC), effective atomic number, normalized effective atomic number, and slope of the spectral attenuation curves in the arterial phase (AP) and venous phase (VP) were measured. A univariate analysis and stepwise logistic regression analysis were performed to screen the independent indicators for PTMC. A radiological model, DECT model, and DECT-radiological nomogram were constructed, and the performances of the 3 models were assessed using the receiver operating characteristic curve, DeLong test, and a decision curve analysis (DCA). Results: The IC in the AP [odds ratio (OR) =0.172], NIC in the AP (OR =0.003), punctate calcification (OR =2.163), and enhanced blurring (OR =3.188) were identified as independent predictors in the stepwise-logistic regression. The areas under the curve with 95% confidence intervals (CIs) of the radiological model, DECT model, and DECT-radiological nomogram were 0.661 (95% CI: 0.595-0.728), 0.856 (95% CI: 0.810-0.902), and 0.880 (95% CI: 0.839-0.921), respectively, in the training cohort; and 0.701 (95% CI: 0.601-0.800), 0.791 (95% CI: 0.704-0.877), and 0.836 (95% CI: 0.760-0.911), respectively, in the validation cohort. The diagnostic performance of the DECT-radiological nomogram was better than that of the radiological model (P<0.05). The DECT-radiological nomogram was found to be well calibrated and had a good net benefit. Conclusions: DECT provides valuable information for differentiating between PTMC and MNG. The DECT-radiological nomogram could serve as an easy-to-use, noninvasive, and effective method for differentiating between PTMC and MNG and help clinicians in decision-making.

Nomogram based on spectral CT quantitative parameters and typical radiological features for distinguishing benign from malignant thyroid micro-nodules.

PURPOSE: To analyse the predictive effect of a nomogram combining dual-layer spectral computed tomography (DSCT) quantitative parameters with typical radiological features in distinguishing benign micro-nodule from thyroid microcarcinoma (TMC). METHODS: Data from 342 instances with thyroid micro-nodules (≤1 cm) who underwent DSCT (benign group: n = 170; malignant group: n = 172) were reviewed. Typical radiological features including micro-calcification and enhanced blurring, and DSCT quantitative parameters including attenuation on virtual monoenergetic images (40 keV, 70 keV and 100 keV), the slope of the spectral HU curve (λHU), normalized iodine concentration (NIC), and normalized effective atomic number (NZeff) in the arterial phase (AP) and venous phase (VP), were measured and compared between the benign and malignant groups. The receiver operating characteristic (ROC) curve was used to assess the diagnostic performance of significant quantitative DSCT parameters or the models combining DSCT parameters respectively and typical radiological features based on multivariate logistic regression (LR) analysis. A nomogram was developed using predictors with the highest diagnostic performance in the above model, as determined by multivariate LR analysis. RESULTS: The DSCT parameter APλHU showed the greatest diagnostic efficiency in identifying patients with TMC, with an area under the ROC curve (AUC) of 0.829, a sensitivity and specificity of 0.738 and 0.753, respectively. Then, APλHU was combined with the two radiological features to construct the DSCT-Radiological nomogram, which had an AUC of 0.858, a sensitivity of 0.791 and a specificity of 0.800. The calibration curve of the nomogram demonstrated that the prediction result was in good agreement with the actual observation. The decision curve revealed that the nomogram can result in a greater net benefit than the all/none-intervention strategy for all threshold probabilities. CONCLUSION: As a valid and visual noninvasive prediction tool, the DSCT-Radiological nomogram incorporating DSCT quantitative parameters and radiological features shows favourable predictive efficiency for identifying benign and malignant thyroid micro-nodules.

Subtraction coronary CT angiography in patients with high heart rate.

All the previous subtraction coronary CT angiography (CCTA) had strict heart rate (HR) inclusion criteria. In this study, a new subtraction method was applied to patients with various HR. The post-contrast scan time was respectively 3.5 s after ascending aorta peak enhancement while HR >80 bpm, 4 s while 65≤ HR ≤80 bpm and 4.5 s while HR <65 bpm. Forty-six patients who underwent the new subtraction protocol were enrolled and patients were stratified into the high HR group (≥70 bpm) and low HR group (<70 bpm). Eighteen patients with 15 severe calcification segments and 25 stent segments further received invasive coronary angiography (ICA). In all included patients, the coronary artery enhancement was compared between the high and low HR groups. In patients with ICA performed, the image quality improvement and diagnostic effectiveness for detection of significant coronary segments stenosis (>50%) were compared between the conventional CCTA and subtraction CCTA and between the high HR group and low HR group, respectively. All enrolled patients got sufficient coronary artery enhancement. In patients with ICA performed, receiver operating characteristic (ROC) curve analysis showed that the area under the curve (AUC) for the diagnosis of significant stenosis was 0.93 in subtraction CCTA and 0.73 in conventional CCTA (p < 0.05). Furthermore, there were no significant differences in image quality improvement, specificity, positive predictive value and accuracy between the high HR group and low HR group. The new subtraction CCTA method broadened the clinical availability for patients with high HR.

The application of 3-dimensional magnetic resonance imaging in nasopharyngeal carcinoma with pterygopalatine fossa invasion.

OBJECT: The pterygopalatine fossa (PPF) is a covert neurovascular pathway in the skull base and connects with numerous intracranial and extracranial spaces. The aim of this study was to explore the magnetic resonance imaging (MRI) features of PPF invasion in patients with nasopharyngeal carcinoma (NPC). MATERIAL AND METHODS: The medical records of 88 patients with stage T3 or T4 NPC were retrospectively analyzed. The 3-Dimensional (3D) volumetric images of MRI were reconstructed for the tiny connecting conduits of the invaded PPFs in the NPC patients. The infiltration incidence of conduits and connected further structures were calculated. RESULTS: Forty-six PPFs from 37 patients were invaded by NPC. The proportions of stage T4 NPC and intracranial extension were higher in patients with PPF invasion than that without PPF invasion (P < 0.05). Each connecting conduit of the PPF had corresponding optimal reconstructed orientation based on 3D volumetric MRI images. The first three most common infiltrated conduits were palatovaginal canal, vidian canal and sphenopalatine foramen, which were adjacent to the nasopharynx. Among the conduits connecting with further structures, the most common infiltrated conduit was pterygomaxillary fissure, followed by foramen rotundum and inferior orbital fissure. Furthermore, The NPC lesions involved stage T4 structures via the conduits from 19.6% of the invaded PPFs. CONCLUSIONS: The application of high-quality reconstruction images based on 3D sequence of MRI in NPC patients proved to be feasible and beneficial for the manifestation of the invaded PPFs and connecting conduits.

Factors influencing apparent diffusion coefficient value in extrahepatic cholangiocarcinoma: A retrospective study.

BACKGROUND: Most cases of extrahepatic cholangiocarcinoma (ECC) show various degrees of hyperintensity on diffusion-weighted imaging (DWI) and hypointensity on apparent diffusion coefficient (ADC). However, the factors influencing ADC values in ECC have not yet been explored extensively. Therefore, this study explored the independent factors influencing ADC values in ECC. METHODS: A total of 88 patients with ECC confirmed by surgical pathology were retrospectively assessed. All patients underwent abdominal magnetic resonance imaging (MRI) at 3.0 T and ADC values of the tumor were measured. The correlation between ADC values and multiple clinicopathological features in ECC was analyzed, and independent factors influencing the ADC values in ECC were explored further. RESULTS: The ADC value of the tumor showed a significant difference in different perineural invasion group (p = 0.048), microvascular invasion group (p = 0.001), vascular endothelial growth factor expression group (p < 0.001), lymphatic status group (p = 0.003), and differentiation degree (DD) group (p < 0.001). However, there were no significant differences in different sex (p = 0.715), tumor location (p = 0.659), and degree of T stage (p = 0.879). Moreover, ADC value was negatively correlated with microvascular density (r = -0.725, p < 0.001) and lesion size (r = -0.244, p = 0.023). Nevertheless, there was no correlation between ADC value and patient age (r = 0.026, p = 0.812). Further regression analysis indicated that ADC value was independently associated with pathological DD of ECC (R2 = 0.678, p < 0.001) but not with other clinicopathological factors (p > 0.05). CONCLUSION: ADC value of ECC is independently correlated with pathological DD of ECC, indicating that ADC value is a potential imaging biomarker for predicting the degree of ECC malignancy.

Feasibility of a CT-based lymph node radiomics nomogram in detecting lymph node metastasis in PDAC patients.

Objectives: To investigate the potential value of a contrast enhanced computed tomography (CECT)-based radiological-radiomics nomogram combining a lymph node (LN) radiomics signature and LNs' radiological features for preoperative detection of LN metastasis in patients with pancreatic ductal adenocarcinoma (PDAC). Materials and methods: In this retrospective study, 196 LNs in 61 PDAC patients were enrolled and divided into the training (137 LNs) and validation (59 LNs) cohorts. Radiomic features were extracted from portal venous phase images of LNs. The least absolute shrinkage and selection operator (LASSO) regression algorithm with 10-fold cross-validation was used to select optimal features to determine the radiomics score (Rad-score). The radiological-radiomics nomogram was developed by using significant predictors of LN metastasis by multivariate logistic regression (LR) analysis in the training cohort and validated in the validation cohort independently. Its diagnostic performance was assessed by receiver operating characteristic curve (ROC), decision curve (DCA) and calibration curve analyses. Results: The radiological model, including LN size, and margin and enhancement pattern (three significant predictors), exhibited areas under the curves (AUCs) of 0.831 and 0.756 in the training and validation cohorts, respectively. Nine radiomic features were used to construct a radiomics model, which showed AUCs of 0.879 and 0.804 in the training and validation cohorts, respectively. The radiological-radiomics nomogram, which incorporated the LN Rad-score and the three LNs' radiological features, performed better than the Rad-score and radiological models individually, with AUCs of 0.937 and 0.851 in the training and validation cohorts, respectively. Calibration curve analysis and DCA revealed that the radiological-radiomics nomogram showed satisfactory consistency and the highest net benefit for preoperative diagnosis of LN metastasis. Conclusions: The CT-based LN radiological-radiomics nomogram may serve as a valid and convenient computer-aided tool for personalized risk assessment of LN metastasis and help clinicians make appropriate clinical decisions for PADC patients.

Proton Magnetic Resonance Spectroscopy at 3.0T in Rabbit With VX2 Liver Cancer: Diagnostic Efficacy and Correlations With Tumor Size.

Purposes: The purpose of this study was to explore the diagnostic performance of Cho peak area (Cho Are), Cho peak amplitude (Cho Amp), and the combined approach (Cho Are_Amp) in detecting rabbit VX2 liver cancer at the early stage via hydrogen-1 proton magnetic resonance spectroscopy (1H-MRS), as well as the correlations between Cho Are, Cho Amp, and tumor parameters like diameter and volume. Methods: Conventional magnetic resonance imaging (MRI) and MRS were performed to scan the VX2 liver cancer in rabbit. The tumor diameter was measured on T2-weighted imaging (T2WI), and the tumor volume was accordingly calculated. Cho Are and Cho Amp were obtained from MRS. The diagnostic performance of Cho Are, Cho Amp, and Cho Are_Amp was assessed by a receiver operating characteristic (ROC) curve and area under ROC curve (AUC), whereas specificity and sensitivity were calculated by the maximum Youden's index. Spearman's correlation analysis was performed to evaluate the relevance between tumor parameters (diameter, volume) and radiological indexes (Cho Are, Cho Amp). Results: ROC curve analysis showed that Cho Amp, Cho Are, and Cho Are_Amp were effective in diagnosing VX2 liver cancer. The AUC of Cho Amp was 0.883, and the specificity and sensitivity were 0.944 and 0.722, respectively (p < 0.001). The AUC of Cho Are was 0.807, and the specificity and sensitivity were 0.778 and 0.833, respectively (p < 0.05). The AUC of Cho Are_Amp was 0.892, and the specificity and sensitivity were 0.833 and 0.833, respectively (p < 0.001). Cho Are and Cho Amp exhibited a high positive correlation with tumor diameter and tumor volume, among which Cho Amp demonstrated better correlations to tumor diameter and tumor volume (r = 0.956 and 0.946) than that of Cho Are (r = 0.787 and 0.794). A high positive correlation was detected between Cho Are and Cho Amp (r = 0.787), as well as tumor diameter and tumor volume (r = 0.965). Conclusion: Cho Are_Amp can be used as an effective tool in diagnosing early-stage VX2 liver cancer with satisfied diagnostic accuracy. Cho Are and Cho Amp were positively correlated with tumor volume and tumor diameter. The results of this study provide further evidence that Cho Amp and Cho Are_Amp of MRS could aid in the early diagnosis of liver cancer.

A Preliminary Study on the Diagnostic Efficacy of Proton Magnetic Resonance Spectroscopy at 3.0T in Rabbit With VX2 Liver Tumor.

BACKGROUND: To investigate the diagnostic efficacy of choline (Cho) value of magnetic resonance spectroscopy (MRS) in rabbit with VX2 liver tumor via comparative and quantitative analysis with the choline compounds concentration measured by enzyme linked immunosorbent assay (ELISA). METHODS: MRS was performed on normal liver and VX2 tumor. The Cho value of VX2 tumor was compared with that of normal liver. Tissues were harvested for ELISA to detect the concentrations of acetylcholine (ACh), glycophorophosphygholine (GPC) and phosphochorine (PC). The diagnostic performance of Cho value and concentrations of choline compounds were assessed by receiver operating characteristic (ROC) curve and area under ROC curve (AUC). The specificity and sensitivity were discussed by the maximum Youden's index. RESULTS: The concentration of ACh was obviously higher than that of GPC and PC both in VX2 tumor and normal liver (P < 0.01). Furthermore, the concentration differences among ACh, GPC and PG were the third power of 10. Both the ACh concentration and Cho value of MRS in VX2 tumor were significantly higher than those in normal liver (P < 0.01). The AUC of ACh in VX2 tumor was 0.883, when the cutoff value was 7259000, the sensitivity and specificity of the diagnosis of liver cancer were 94.4% and 77.8%, respectively. The AUC of Cho in VX2 tumor was 0.807, when the cutoff value was 28.35, the sensitivity and specificity of the diagnosis of liver cancer were 83.3% and 77.8%, respectively. CONCLUSION: The change of Cho value in MRS between liver cancer and normal liver was consistent with the changes of concentrations of choline compounds measured by ELISA, especially the change of ACh concentration. The diagnostic efficiency of Cho value and that of choline compounds concentration in liver cancer were extremely similar, with the AUC more than 0.8. We conclude that MRS may be applied as an important, non-invasive biomarker for the diagnosis of liver cancer.

A clinical-radiomics nomogram may provide a personalized 90-day functional outcome assessment for spontaneous intracerebral hemorrhage.

OBJECTIVES: To develop and validate a noncontrast computed tomography (NCCT)-based clinical-radiomics nomogram to identify spontaneous intracerebral hemorrhage (sICH) patients with a poor 90-day prognosis on admission. METHODS: In this double-center retrospective study, data from 435 patients with sICH (training cohort: n = 244; internal validation cohort: n = 104; external validation cohort: n = 87) were reviewed. The radiomics score (Rad-score) was calculated based on the coefficients of the selected radiomics features. A clinical-radiomics nomogram was developed by using independent predictors of poor outcome at 90 days through multivariate logistic regression analysis in the training cohort and was validated in the internal and external cohorts. RESULTS: At 90 days, 200 of 435 (46.0%) patients had a poor prognosis. The clinical-radiomics nomogram was developed by six independent predictors namely midline shift, NCCT time from sICH onset, Glasgow Coma Scale score, serum glucose, uric acid, and Rad-score. In identifying patients with poor prognosis, the clinical-radiomics nomogram showed an area under the receiver operating characteristic curve (AUC) of 0.81 in the training cohort, an AUC of 0.78 in the internal validation cohort, and an AUC of 0.73 in the external validation cohort. The calibration curve revealed that the clinical-radiomics nomogram showed satisfactory calibration in the training and internal validation cohorts (both p > 0.05), but slightly poor agreement in the external validation cohort (p < 0.05). CONCLUSIONS: The clinical-radiomics nomogram is a valid computer-aided tool that may provide personalized risk assessment of 90-day functional outcome for sICH patients. KEY POINTS: • The proposed Rad-score was significantly associated with 90-day poor functional outcome in patients with sICH. • The clinical-radiomics nomogram showed satisfactory calibration and the most net benefit for discriminating 90-day poor outcome. • The clinical-radiomics nomogram may provide personalized risk assessment of 90-day functional outcome for sICH patients.

A comprehensive study on ozone pollution in a megacity in North China Plain during summertime: Observations, source attributions and ozone sensitivity.

Tropospheric ozone (O3) pollution has been becoming prominent in North China Plain (NCP) in China since last decade. In order to clarify the source contribution and formation mechanism of O3, the critical precursors of volatile organic compounds (VOCs) were measured with both on-line and off-line methods in Luoyang City in summer of 2019. The concentrations of nitrogen oxides (NOx, sum of NO and NO2) and O3 were simultaneously monitored. Fifty-seven VOCs measured in U.S. Photochemical Assessment Monitoring Station (PAMS) showed daily concentrations in a range of 14.5 ± 5.33 to 29.2 ± 11.2 ppbv in Luoyang, which were comparable with those in other Chinese megacities. The mass compositions of VOCs were determined, with comparatively low proportions of alkanes (<50%) but high fractions of photoreactive alkenes and alkyne. Source apportionment of VOCs was conducted by Hybrid Environmental Receptor Model (HERM). The results indicated that industrial (38.5%) and traffic (32.0%) were the two dominated pollution sources of VOCs in the urban, while the biogenic and residential sources had contributions of 15.8% and 13.8%, respectively. To further measure the O3 formation sensitivity and its source attribution, the WRF-CHEM model was adopted in this study. The variation of O3 between the observation and the stimulation using the local emission inventory showed an index of agreement (IOA) of 0.85. The simulation conducted by WRF-CHEM indicated an average of 43.5% of the O3 was associated with the regional transportation, revealing the importance of inter-regional prevention and control policy. Traffic and biogenic emissions were the two major pollution sources to an O3 episode occurred from July 21 to July 27, 2019 (when O3 concentration over 150 µg m-3) in Luoyang, with average contributions of 22.9% and 18.3%, respectively. The O3 isopleths proved that its formation in the atmosphere of Luoyang was in transitional regime and collectively controlled by both VOCs and NOx. This was different from the observations in main cities of NCP before implantations of strict emission controls. The isopleths additionally designated that the O3 formation regime would move forward or shift to NOx regime after a reduction of over 45% during the episode. Similar patterns were also reported in other Chinese megacities such as Beijing and Shanghai, due to the tightening of the NOx control policies. Our results do support that the simultaneous controls of NOx and VOCs were effective in reductions of tropospheric O3 in Luoyang. Meanwhile, joint regional control policies on the emissions of NOx and VOCs can potentially overwhelm the current O3 pollutions in China.

Noncontrast Computed Tomography-Based Radiomics Analysis in Discriminating Early Hematoma Expansion after Spontaneous Intracerebral Hemorrhage.

OBJECTIVE: To determine whether noncontrast computed tomography (NCCT) models based on multivariable, radiomics features, and machine learning (ML) algorithms could further improve the discrimination of early hematoma expansion (HE) in patients with spontaneous intracerebral hemorrhage (sICH). MATERIALS AND METHODS: We retrospectively reviewed 261 patients with sICH who underwent initial NCCT within 6 hours of ictus and follow-up CT within 24 hours after initial NCCT, between April 2011 and March 2019. The clinical characteristics, imaging signs and radiomics features extracted from the initial NCCT images were used to construct models to discriminate early HE. A clinical-radiologic model was constructed using a multivariate logistic regression (LR) analysis. Radiomics models, a radiomics-radiologic model, and a combined model were constructed in the training cohort (n = 182) and independently verified in the validation cohort (n = 79). Receiver operating characteristic analysis and the area under the curve (AUC) were used to evaluate the discriminative power. RESULTS: The AUC of the clinical-radiologic model for discriminating early HE was 0.766. The AUCs of the radiomics model for discriminating early HE built using the LR algorithm in the training and validation cohorts were 0.926 and 0.850, respectively. The AUCs of the radiomics-radiologic model in the training and validation cohorts were 0.946 and 0.867, respectively. The AUCs of the combined model in the training and validation cohorts were 0.960 and 0.867, respectively. CONCLUSION: NCCT models based on multivariable, radiomics features and ML algorithm could improve the discrimination of early HE. The combined model was the best recommended model to identify sICH patients at risk of early HE.

A Preliminary Study of CT Texture Analysis for Characterizing Epithelial Tumors of the Parotid Gland.

OBJECTIVE: The aim of this study was to explore and validate the diagnostic performance of whole-volume CT texture features in differentiating the common benign and malignant epithelial tumors of the parotid gland. MATERIALS AND METHODS: Contrast-enhanced CT images of 83 patients with common benign and malignant epithelial tumors of the parotid gland confirmed by histopathology were retrospectively analyzed, including 50 patients with pleomorphic adenoma (PA) and 33 patients with malignant epithelial tumors. Quantitative texture features of tumors were extracted from CT images of arterial phase. The diagnostic performance of texture features was evaluated via receiver operating characteristic (ROC) curve and area under ROC curve (AUC). The specificity and sensitivity were respectively discussed by the maximum Youden's index. RESULTS: All the texture features were subject to normal distribution and homoscedasticity. Energy, mean, correlation, and sum entropy of epithelial malignancy group were significantly higher than those of PA group (P<0.05). There were no statistically significant differences between PA group and epithelial malignancy group in uniformity, entropy, skewness, kurtosis, contrast, and difference entropy (P>0.05). The AUC of each texture feature and joint diagnostic model was 0.887 (energy), 0.734 (mean), 0.739 (correlation), 0.623 (sum entropy), 0.888 (energy-mean), 0.883 (energy-correlation), 0.784 (mean-correlation). The diagnostic efficiency of energy-mean was the best. Based on the maximum Youden's index, the specificity of energy-correlation was the highest (97%) and the sensitivity of energy was the highest (97%). CONCLUSION: Energy, mean, correlation, and sum entropy can be the effective quantitative texture features to differentiate the benign and malignant epithelial tumors of the parotid gland. With higher AUC, energy and energy-mean are superior to other indexes or joint diagnostic models in differentiating the benign and malignant epithelial tumors of the parotid gland. CT texture analysis can be used as a noninvasive and valuable means of preoperative assessment of parotid epithelial tumors without additional cost to the patients.

Improving the diagnosis of common parotid tumors via the combination of CT image biomarkers and clinical parameters.

BACKGROUND: Our study aims to develop and validate diagnostic models of the common parotid tumors based on whole-volume CT textural image biomarkers (IBMs) in combination with clinical parameters at a single institution. METHODS: The study cohort was composed of 51 pleomorphic adenoma (PA) patients and 42 Warthin tumor (WT) patients. Clinical parameters and conventional image features were scored retrospectively and textural IBMs were extracted from CT images of arterial phase. Independent-samples t test or Chi-square test was used for evaluating the significance of the difference among clinical parameters, conventional CT image features, and textural IBMs. The diagnostic performance of univariate model and multivariate model was evaluated via receiver operating characteristic (ROC) curve and area under ROC curve (AUC). RESULTS: Significant differences were found in clinical parameters (age, gender, disease duration, smoking), conventional image features (site, maximum diameter, time-density curve, peripheral vessels sign) and textural IBMs (mean, uniformity, energy, entropy) between PA group and WT group (P<0.05). ROC analysis showed that clinical parameter (age) and quantitative textural IBMs (mean, energy, entropy) were able to categorize the patients into PA group and WT group, with the AUC of 0.784, 0.902, 0.910, 0.805, respectively. When IBMs were added in clinical model, the multivariate models including age-mean and age-energy performed significantly better than the univariate models with the improved AUC of 0.940, 0.944, respectively (P<0.001). CONCLUSIONS: Both clinical parameter and CT textural IBMs can be used for the preoperative, noninvasive diagnosis of parotid PA and WT. The diagnostic performance of textural IBM model was obviously better than that of clinical model and conventional image model in this study. While the multivariate model consisted of clinical parameter and textural IBM had the optimal diagnostic performance, which would contribute to the better selection of individualized surgery program.

Aerosols chemical composition, light extinction, and source apportionment near a desert margin city, Yulin, China.

Daily PM10and PM2.5 sampling was conducted during four seasons from December 2013 to October 2014 at three monitoring sites over Yulin, a desert margin city. PM10 and PM2.5 levels, water soluble ions, organic carbon (OC), and elemental carbon (EC) were also analyzed to characterize their chemical profiles. b ext (light extinction coefficient) was calculated, which showed the highest in winter with an average of 232.95 ± 154.88 Mm-1, followed by autumn, summer, spring. Light extinction source apportionment results investigated (NH4)2SO4 and NH4NO3 played key roles in the light extinction under high RH conditions during summer and winter. Sulfate, nitrate and Ca2 + dominated in PM10/PM2.5 ions. Ion balance results illustrated that PM samples were alkaline, and PM10 samples were more alkaline than PM2.5. High SO4 2-/K+ and Cl-/K+ ratio indicated the important contribution of coal combustion, which was consistent with the OC/EC regression equation intercepts results. Principal component analysis (PCA) analyses results showed that the fugitive dust was the most major source of PM, followed by coal combustion & gasoline vehicle emissions, secondary formation and diesel vehicle emissions. Potential contribution source function (PSCF) results suggested that local emissions, as well as certain regional transport from northwesterly and southerly areas contributed to PM2.5 loadings during the whole year. Local government should take some measures to reduce the PM levels.

Abnormal Baseline Brain Activity in Neuromyelitis Optica Patients Without Brain Lesion Detected by Resting-State Functional Magnetic Resonance Imaging.

OBJECTIVE: To investigate the baseline brain activity in neuromyelitis optica patients without brain lesion using the regional amplitude of low-frequency fluctuation (ALFF) and fractional amplitude of low-frequency fluctuation (fALFF) as indexes. MATERIALS AND METHODS: Forty-two patients of NMO with normal performance in conventional MRI and 42 healthy controls, matched in gender and age, were enrolled in this study. Resting-state functional magnetic resonance imaging (rs-fMRI) data acquired using the rs-fMRI Data Analysis Toolkit. The relationships between expanded disability states scale (EDSS) scores, abnormal baseline brain activity and disease duration were explored. RESULTS: The left inferior temporal, left cerebellum_4_5, bilateral superior temporal pole, left caudate, right superior temporal, left middle frontal and left superior occipital showed significantly increased ALFF in the NMO. Regions of abnormal fALFF were similar to those of ALFF except that increased fALFF were also indicated in the right cerebellum crus2, right hippocampus, left parahippocampal gyrus and left supplementary motor area. Furthermore, a significant correlation between EDSS scores and ALFF/fALFF was noted in the left inferior temporal gyrus. CONCLUSION: Results confirmed the disturbances in NMO-related neural networks, which probably be related to spinal cord damage.