There are more uncertainties with ice hydrometeor representations and related processes than liquid hydrometeors within microphysics parameterization(MP)schemes because of their complicated geometries and physical pro...There are more uncertainties with ice hydrometeor representations and related processes than liquid hydrometeors within microphysics parameterization(MP)schemes because of their complicated geometries and physical properties.Idealized supercell simulations are produced using the WRF model coupled with“full”Hebrew University spectral bin MP(HU-SBM),and NSSL and Thompson bulk MP(BMP)schemes.HU-SBM downdrafts are typically weaker than those of the NSSL and Thompson simulations,accompanied by less rain evaporation.HU-SBM produces more cloud ice(plates),graupel,and hail than the BMPs,yet precipitates less at the surface.The limiting mass bins(and subsequently,particle size)of rimed ice in HU-SBM and slower rimed ice fall speeds lead to smaller melting-level net rimed ice fluxes than those of the BMPs.Aggregation from plates in HU-SBM,together with snow–graupel collisions,leads to a greater snow contribution to rain than those of the BMPs.Replacing HU-SBM’s fall speeds using the formulations of the BMPs after aggregating the discrete bin values to mass mixing ratios and total number concentrations increases net rain and rimed ice fluxes.Still,they are smaller in magnitude than bulk rain,NSSL hail,and Thompson graupel net fluxes near the surface.Conversely,the melting-layer net rimed ice fluxes are reduced when the fall speeds for the NSSL and Thompson simulations are calculated using HU-SBM fall speed formulations after discretizing the bulk particle size distributions(PSDs)into spectral bins.The results highlight precipitation sensitivity to storm dynamics,fall speed,hydrometeor evolution governed by process rates,and MP PSD design.展开更多
Pulse echo accumulation is commonly employed in coherent Doppler wind LiDAR(light detection and ranging)under the assumption of steady wind.Here,the measured spectral data are analyzed in the time dimension and freque...Pulse echo accumulation is commonly employed in coherent Doppler wind LiDAR(light detection and ranging)under the assumption of steady wind.Here,the measured spectral data are analyzed in the time dimension and frequency dimension to cope with the temporal wind shear and achieve the optimal accumulation time.A hardware-efficient algorithm combining the interpolation and cross-correlation is used to enhance the wind retrieval accuracy by reducing the frequency sampling interval and then reduce the spectral width calculation error.Moreover,the temporal broadening effect and spatial broadening effect are decoupled according to the strategy we developed.展开更多
BACKGROUND Lymphovascular invasion(LVI)and perineural invasion(PNI)are important prognostic factors for gastric cancer(GC)that indicate an increased risk of metastasis and poor outcomes.Accurate preoperative predictio...BACKGROUND Lymphovascular invasion(LVI)and perineural invasion(PNI)are important prognostic factors for gastric cancer(GC)that indicate an increased risk of metastasis and poor outcomes.Accurate preoperative prediction of LVI/PNI status could help clinicians identify high-risk patients and guide treatment deci-sions.However,prior models using conventional computed tomography(CT)images to predict LVI or PNI separately have had limited accuracy.Spectral CT provides quantitative enhancement parameters that may better capture tumor invasion.We hypothesized that a predictive model combining clinical and spectral CT parameters would accurately preoperatively predict LVI/PNI status in GC patients.AIM To develop and test a machine learning model that fuses spectral CT parameters and clinical indicators to predict LVI/PNI status accurately.METHODS This study used a retrospective dataset involving 257 GC patients(training cohort,n=172;validation cohort,n=85).First,several clinical indicators,including serum tumor markers,CT-TN stages and CT-detected extramural vein invasion(CT-EMVI),were extracted,as were quantitative spectral CT parameters from the delineated tumor regions.Next,a two-step feature selection approach using correlation-based methods and information gain ranking inside a 10-fold cross-validation loop was utilized to select informative clinical and spectral CT parameters.A logistic regression(LR)-based nomogram model was subsequently constructed to predict LVI/PNI status,and its performance was evaluated using the area under the receiver operating characteristic curve(AUC).RESULTS In both the training and validation cohorts,CT T3-4 stage,CT-N positive status,and CT-EMVI positive status are more prevalent in the LVI/PNI-positive group and these differences are statistically significant(P<0.05).LR analysis of the training group showed preoperative CT-T stage,CT-EMVI,single-energy CT values of 70 keV of venous phase(VP-70 keV),and the ratio of standardized iodine concentration of equilibrium phase(EP-NIC)were independent influencing factors.The AUCs of VP-70 keV and EP-NIC were 0.888 and 0.824,respectively,which were slightly greater than those of CT-T and CT-EMVI(AUC=0.793,0.762).The nomogram combining CT-T stage,CT-EMVI,VP-70 keV and EP-NIC yielded AUCs of 0.918(0.866-0.954)and 0.874(0.784-0.936)in the training and validation cohorts,which are significantly higher than using each of single independent factors(P<0.05).CONCLUSION The study found that using portal venous and EP spectral CT parameters allows effective preoperative detection of LVI/PNI in GC,with accuracy boosted by integrating clinical markers.展开更多
In Côte d’Ivoire, the recurring and unregulated use of bushfires, which cause ecological damage, presents a pressing concern for the custodians of protected areas. This study aims to enhance our comprehension of...In Côte d’Ivoire, the recurring and unregulated use of bushfires, which cause ecological damage, presents a pressing concern for the custodians of protected areas. This study aims to enhance our comprehension of the dynamics of burnt areas within the Abokouamékro Wildlife Reserve (AWR) by employing the analysis of spectral indices derived from satellite imagery. The research methodology began with the calculation of mean indices and their corresponding spectral sub-indices, including NDVI, SAVI, NDWI, NDMI, BAI, NBR, TCW, TCG, and TCB, utilizing data from the Sentinel-2A satellite image dated January 17, 2022. Subsequently, a fuzzy classification model was applied to these various indices and sub-indices, guided by the degree of membership α, with the goal of effectively distinguishing between burned and unburned areas. Following the classification, the accuracies of the classified indices and sub-indices were validated using the coordinates of 100 data points collected within the AWR through GPS technology. The results revealed that the overall accuracy of all indices and sub-indices declines as the degree of membership α decreases from 1 to 0. Among the mean spectral indices, NDVI-mean, SAVI-mean, NDMI-mean exhibited the highest overall accuracies, achieving 97%, 95%, and 90%, respectively. These results closely mirrored those obtained by sub-indices using band 8 (NDVI-B8, SAVI-B8, and NDMI-B8), which yield respective overall accuracies of 93%, 92%, and 89%. At a degree of membership α = 1, the estimated burned areas for the most effective indices encompassed 2144.38 hectares for NDVI-mean, 1932.14 hectares for mean SAVI-mean, and 4947.13 hectares for mean NDMI-mean. A prospective approach involving the amalgamation of these three indices could have the potential to yield improved outcomes. This study could be a substantial contribution to the discrimination of bushfires in Côte d’Ivoire.展开更多
Spectral unmixing helps to identify different components present in the spectral mixtures which occur in the uppermost layer of the area owing to the low spatial resolution of hyperspectral images.Most spectral unmixi...Spectral unmixing helps to identify different components present in the spectral mixtures which occur in the uppermost layer of the area owing to the low spatial resolution of hyperspectral images.Most spectral unmixing methods are globally based and do not consider the spectral variability among its endmembers that occur due to illumination,atmospheric,and environmental conditions.Here,endmember bundle extraction plays a major role in overcoming the above-mentioned limitations leading to more accurate abundance fractions.Accordingly,a two-stage approach is proposed to extract endmembers through endmember bundles in hyperspectral images.The divide and conquer method is applied as the first step in subset images with only the non-redundant bands to extract endmembers using the Vertex Component Analysis(VCA)and N-FINDR algorithms.A fuzzy rule-based inference system utilizing spectral matching parameters is proposed in the second step to categorize endmembers.The endmember with the minimum error is chosen as the final endmember in each specific category.The proposed method is simple and automatically considers endmember variability in hyperspectral images.The efficiency of the proposed method is evaluated using two real hyperspectral datasets.The average spectral angle and abundance angle are used to analyze the performance measures.展开更多
Sanxingdui cultural relics are the precious cultural heritage of humanity with high values of history,science,culture,art and research.However,mainstream analytical methods are contacting and detrimental,which is unfa...Sanxingdui cultural relics are the precious cultural heritage of humanity with high values of history,science,culture,art and research.However,mainstream analytical methods are contacting and detrimental,which is unfavorable to the protection of cultural relics.This paper improves the accuracy of the extraction,location,and analysis of artifacts using hyperspectral methods.To improve the accuracy of cultural relic mining,positioning,and analysis,the segmentation algorithm of Sanxingdui cultural relics based on the spatial spectrum integrated network is proposed with the support of hyperspectral techniques.Firstly,region stitching algorithm based on the relative position of hyper spectrally collected data is proposed to improve stitching efficiency.Secondly,given the prominence of traditional HRNet(High-Resolution Net)models in high-resolution data processing,the spatial attention mechanism is put forward to obtain spatial dimension information.Thirdly,in view of the prominence of 3D networks in spectral information acquisition,the pyramid 3D residual network model is proposed to obtain internal spectral dimensional information.Fourthly,four kinds of fusion methods at the level of data and decision are presented to achieve cultural relic labeling.As shown by the experiment results,the proposed network adopts an integrated method of data-level and decision-level,which achieves the optimal average accuracy of identification 0.84,realizes shallow coverage of cultural relics labeling,and effectively supports the mining and protection of cultural relics.展开更多
The accurate and rapid estimation of canopy nitrogen content(CNC)in crops is the key to optimizing in-season nitrogen fertilizer application in precision agriculture.However,the determination of CNC from field samplin...The accurate and rapid estimation of canopy nitrogen content(CNC)in crops is the key to optimizing in-season nitrogen fertilizer application in precision agriculture.However,the determination of CNC from field sampling data for leaf area index(LAI),canopy photosynthetic pigments(CPP;including chlorophyll a,chlorophyll b and carotenoids)and leaf nitrogen concentration(LNC)can be time-consuming and costly.Here we evaluated the use of high-precision unmanned aerial vehicle(UAV)multispectral imagery for estimating the LAI,CPP and CNC of winter wheat over the whole growth period.A total of 23 spectral features(SFs;five original spectrum bands,17 vegetation indices and the gray scale of the RGB image)and eight texture features(TFs;contrast,entropy,variance,mean,homogeneity,dissimilarity,second moment,and correlation)were selected as inputs for the models.Six machine learning methods,i.e.,multiple stepwise regression(MSR),support vector regression(SVR),gradient boosting decision tree(GBDT),Gaussian process regression(GPR),back propagation neural network(BPNN)and radial basis function neural network(RBFNN),were compared for the retrieval of winter wheat LAI,CPP and CNC values,and a double-layer model was proposed for estimating CNC based on LAI and CPP.The results showed that the inversion of winter wheat LAI,CPP and CNC by the combination of SFs+TFs greatly improved the estimation accuracy compared with that by using only the SFs.The RBFNN and BPNN models outperformed the other machine learning models in estimating winter wheat LAI,CPP and CNC.The proposed double-layer models(R^(2)=0.67-0.89,RMSE=13.63-23.71 mg g^(-1),MAE=10.75-17.59 mg g^(-1))performed better than the direct inversion models(R^(2)=0.61-0.80,RMSE=18.01-25.12 mg g^(-1),MAE=12.96-18.88 mg g^(-1))in estimating winter wheat CNC.The best winter wheat CNC accuracy was obtained by the double-layer RBFNN model with SFs+TFs as inputs(R^(2)=0.89,RMSE=13.63 mg g^(-1),MAE=10.75 mg g^(-1)).The results of this study can provide guidance for the accurate and rapid determination of winter wheat canopy nitrogen content in the field.展开更多
Let G be a graph and A(G) the adjacency matrix of G. The spectrum of G is the eigenvalues together with their multiplicities of A(G). Chang et al. (2011) characterized the structures of all graphs with rank 4. Monsalv...Let G be a graph and A(G) the adjacency matrix of G. The spectrum of G is the eigenvalues together with their multiplicities of A(G). Chang et al. (2011) characterized the structures of all graphs with rank 4. Monsalve and Rada (2021) gave the bound of spectral radius of all graphs with rank 4. Based on these results as above, we further investigate the spectral properties of graphs with rank 4. And we give the expressions of the spectral radius and energy of all graphs with rank 4. In particular, we show that some graphs with rank 4 are determined by their spectra.展开更多
Deep learning(DL)has shown its superior performance in dealing with various computer vision tasks in recent years.As a simple and effective DL model,autoencoder(AE)is popularly used to decompose hyperspectral images(H...Deep learning(DL)has shown its superior performance in dealing with various computer vision tasks in recent years.As a simple and effective DL model,autoencoder(AE)is popularly used to decompose hyperspectral images(HSIs)due to its powerful ability of feature extraction and data reconstruction.However,most existing AE-based unmixing algorithms usually ignore the spatial information of HSIs.To solve this problem,a hypergraph regularized deep autoencoder(HGAE)is proposed for unmixing.Firstly,the traditional AE architecture is specifically improved as an unsupervised unmixing framework.Secondly,hypergraph learning is employed to reformulate the loss function,which facilitates the expression of high-order similarity among locally neighboring pixels and promotes the consistency of their abundances.Moreover,L_(1/2)norm is further used to enhance abundances sparsity.Finally,the experiments on simulated data,real hyperspectral remote sensing images,and textile cloth images are used to verify that the proposed method can perform better than several state-of-the-art unmixing algorithms.展开更多
Fire severity classifications determine fire damage and regeneration potential in post-fire areas for effective implementation of restoration applications.Since fire damage varies according to vegetation and fire char...Fire severity classifications determine fire damage and regeneration potential in post-fire areas for effective implementation of restoration applications.Since fire damage varies according to vegetation and fire characteristics,regional assessment of fire severity is crucial.The objectives of this study were:(1)to test the performance of different satellite imagery and spectral indices,and two field—measured severity indices,CBI(Composite Burn Index)and GeoCBI(Geometrically structured Composite Burn Index)to assess fire severity;(2)to calculate classification thresholds for spectral indices that performed best in the study areas;and(3)to generate fire severity maps that could be used to determine the ecological impact of forest fires.Five large fires in Pinus brutia(Turkish pine)and Pinus nigra subsp.pallasiana var.pallasiana(Anatolian black pine)—dominated forests during 2020 and 2021 were selected as study sites.The results show that GeoCBI provided more reliable estimates of field—measured fire severity than CBI.While Sentinel-2 and Landsat-8/OLI images performed similarly well,MODIS performed poorly.Fire severity classification thresholds were determined for Sentinel-2 based RdNBR,dNBR,dSAVI,dNDVI,and dNDMI and Landsat-8/OLI based dNBR,dNDVI,and dSAVI.Among several spectral indices,the highest accuracy for fire severity classification was found for Sentinel-2 based RdNBR(72.1%)and Landsat-8/OLI based dNBR(69.2%).The results can be used to assess and map fire severity in forest ecosystems similar to those in this study.展开更多
Dear Editor, This letter is concerned with a new hyperspectral fusion paradigm by simultaneously fusing hyperspectral, multispectral, and panchromatic images. Seeking an efficient prior about the target hyperspectral ...Dear Editor, This letter is concerned with a new hyperspectral fusion paradigm by simultaneously fusing hyperspectral, multispectral, and panchromatic images. Seeking an efficient prior about the target hyperspectral image(HSI), is vital for constructing an accurate fusion model in this problem. To this end, this work suggests a novel sparse tensor prior using patch-based sparse tensor dictionary learning.展开更多
Traditional Chinese medicine(TCM)is a treasure of the Chinese nation,providing effective solutions to current medical requisites.Various spectral techniques are undergoing continuous development and provide new and re...Traditional Chinese medicine(TCM)is a treasure of the Chinese nation,providing effective solutions to current medical requisites.Various spectral techniques are undergoing continuous development and provide new and reliable means for evaluating the efficacy and quality of TCM.Because spectral techniques are noninvasive,convenient,and sensitive,they have been widely applied to in vitro and in vivo TCM evaluation systems.In this paper,previous achievements and current progress in the research on spectral technologies(including fluorescence spectroscopy,photoacoustic imaging,infrared thermal imaging,laser-induced breakdown spectroscopy,hyperspectral imaging,and surface enhanced Raman spectroscopy)are discussed.The advantages and disadvantages of each technology are also presented.Moreover,the future applications of spectral imaging to identify the origins,components,and pesticide residues of TCM in vitro are elucidated.Subsequently,the evaluation of the efficacy of TCM in vivo is presented.Identifying future applications of spectral imaging is anticipated to promote medical research as well as scientific and technological explorations.展开更多
Purpose:Our study aims to compare speech understanding in noise and spectral-temporal resolution skills with regard to the degree of hearing loss,age,hearing aid use experience and gender of hearing aid users.Methods:...Purpose:Our study aims to compare speech understanding in noise and spectral-temporal resolution skills with regard to the degree of hearing loss,age,hearing aid use experience and gender of hearing aid users.Methods:Our study included sixty-eight hearing aid users aged between 40-70 years,with bilateral mild and moderate symmetrical sensorineural hearing loss.Random gap detection test,Turkish matrix test and spectral-temporally modulated ripple test were implemented on the participants with bilateral hearing aids.The test results acquired were compared statistically according to different variables and the correlations were examined.Results:No statistically significant differences were observed for speech-in-noise recognition,spectraltemporal resolution among older and younger adults in hearing aid users(p>0.05).There wasn’t found a statistically significant difference among test outcomes as regards different hearing loss degrees(p>0.05).Higher performances were obtained in terms of temporal resolution in male participants and participants with more hearing aid use experience(p<0.05).Significant correlations were obtained between the results of speech-in-noise recognition,temporal resolution and spectral resolution tests performed with hearing aids(p<0.05).Conclusion:Our study findings emphasized the importance of regular hearing aid use and it showed that some auditory skills can be improved with hearing aids.Observation of correlations among the speechin-noise recognition,temporal resolution and spectral resolution tests have revealed that these skills should be evaluated as a whole to maximize the patient’s communication abilities.展开更多
Sentinel-1A/B data are crucial for retrieving numerical information about surface phenomena and processes.Coregistration of terrain observation by progressive scans(TOPS)data is a critical step in its application.TOPS...Sentinel-1A/B data are crucial for retrieving numerical information about surface phenomena and processes.Coregistration of terrain observation by progressive scans(TOPS)data is a critical step in its application.TOPS data must be fundamentally co-registered with an accuracy of 0.001 pixels.However,various decorrelation factors due to natural vegetation and seasonal effects affect the coregistration accuracy of TOPS data.This paper proposed an enhanced spectral diversity coregistration method for dual-polarimetric(PolESD)Sentinel-1A/B TOPS data.The PolESD method suppresses speckle noise based on a unified non-local framework in dual-pol Synthetic Aperture Radar(SAR),and extracts the phase of the optimal polarization channel from the denoised polarimetric interferometric coherency matrix.Compared with the traditional ESD method developed for single-polarization data,the PolESD method can obtain more accurate coherence and phase and get more pixels for azimuth-offset estimation.In bare areas covered with low vegetation,the number of pixels selected by PolESD is more than the Boxcar method.It can also correct misregistration more effectively and eliminate phase jumps in the burst edge.Therefore,PolESD will help improve the application of TOPS data in low-coherence scenarios.展开更多
One of the hot research topics in propagation dynamics is identifying a set of critical nodes that can influence maximization in a complex network.The importance and dispersion of critical nodes among them are both vi...One of the hot research topics in propagation dynamics is identifying a set of critical nodes that can influence maximization in a complex network.The importance and dispersion of critical nodes among them are both vital factors that can influence maximization.We therefore propose a multiple influential spreaders identification algorithm based on spectral graph theory.This algorithm first quantifies the role played by the local structure of nodes in the propagation process,then classifies the nodes based on the eigenvectors of the Laplace matrix,and finally selects a set of critical nodes by the constraint that nodes in the same class are not adjacent to each other while different classes of nodes can be adjacent to each other.Experimental results on real and synthetic networks show that our algorithm outperforms the state-of-the-art and classical algorithms in the SIR model.展开更多
We propose a self-supervising learning framework for finding the dominant eigenfunction-eigenvalue pairs of linear and self-adjoint operators.We represent target eigenfunctions with coordinate-based neural networks an...We propose a self-supervising learning framework for finding the dominant eigenfunction-eigenvalue pairs of linear and self-adjoint operators.We represent target eigenfunctions with coordinate-based neural networks and employ the Fourier positional encodings to enable the approximation of high-frequency modes.We formulate a self-supervised training objective for spectral learning and propose a novel regularization mechanism to ensure that the network finds the exact eigenfunctions instead of a space spanned by the eigenfunctions.Furthermore,we investigate the effect of weight normalization as a mechanism to alleviate the risk of recovering linear dependent modes,allowing us to accurately recover a large number of eigenpairs.The effectiveness of our methods is demonstrated across a collection of representative benchmarks including both local and non-local diffusion operators,as well as high-dimensional time-series data from a video sequence.Our results indicate that the present algorithm can outperform competing approaches in terms of both approximation accuracy and computational cost.展开更多
Micrometric-thin cells(MCs)with alkali vapor atoms have been valuable for research and applications of hyperfine Zeeman splitting and atomic magnetometers under strong magnetic fields.We theoretically and experimental...Micrometric-thin cells(MCs)with alkali vapor atoms have been valuable for research and applications of hyperfine Zeeman splitting and atomic magnetometers under strong magnetic fields.We theoretically and experimentally study the saturated absorption spectra using a 100-μm cesium MC,where the pump and probe beams are linearly polarized with mutually perpendicular polarizations,and the magnetic field is along the pump beam.Because of the distinctive thin chamber of the MC,crossover spectral lines in saturated absorption spectra are largely suppressed leading to clear splittings of hyperfine Zeeman transitions in experiments,and the effect of spatial magnetic field gradient is expected to be reduced.A calculation method is proposed to achieve good agreements between theoretical calculations and experimental results.This method successfully explains the suppression of crossover lines in MCs,as well as the effects of magnetic field direction,propagation and polarization directions of the pump/probe beam on saturated absorption spectrum.The saturated absorption spectrum with suppressed crossover lines is used for laser frequency stabilization,which may provide the potential value of MCs for high spatial resolution strong-field magnetometry with high sensitivity.展开更多
Objective Ureteral lesions caused by impacted ureteral stones are likely to result in postoperative ureteral stricture.On this basis,the study aimed to investigate if dual-energy spectral computed tomography can predi...Objective Ureteral lesions caused by impacted ureteral stones are likely to result in postoperative ureteral stricture.On this basis,the study aimed to investigate if dual-energy spectral computed tomography can predict ureteral hardening caused by impacted stones and to explore the relationship between different types of ureteral lesions and the risk of ureteral stricture.Methods This prospective study collected data of 93 patients with impacted stones from hospital automation system during January 2018 to October 2019.They underwent an abdominal scan on a dual-energy spectral computed tomography.During surgery,the operator used ureteroscopy to identify ureteral lesions,which were classified into four categories:edema,polyps,pallor,and hardening.Seven months later,90 patients were reviewed for the degree of hydronephrosis.Results Endoscopic observations revealed 38(41%)cases of ureteral edema,20(22%)cases of polyps,13(14%)cases of pallor,and 22(24%)cases of hardening.There were significant differences in hydronephrosis,the period of impaction,the calcium concentration of the ureter,and the slope of the spectral Hounsfield unit curve between the four groups.After that,we evaluated the factors associated with ureteral hardening and found that the calcium concentration of the ureter and hydronephrosis remained independent predictors of ureteral hardening.Receiver operating characteristic curve analysis showed that 5.3 mg/cm^(3)calcium concentration of the ureter is an optimal cut-off value to predict ureteral hardening.The result of follow-up showed that 80 patients had complete remission of hydronephrosis,with a complete remission rate of 61.9%(13/21)in the hardening group and 97.1%(67/69)in the non-hardening group(p<0.001).Conclusion Calcium concentration of the ureter is an independent predictor of ureteral hardening.Patients with ureteral hardening have more severe hydronephrosis after ureteroscopic lithotripsy.When the calcium concentration of the ureter is less than 5.3 mg/cm^(3),ureteral lesions should be actively treated.展开更多
BACKGROUND The level of Ki-67 expression has served as a prognostic factor in gastric cancer.The quantitative parameters based on the novel dual-layer spectral detector computed tomography(DLSDCT)in discriminating the...BACKGROUND The level of Ki-67 expression has served as a prognostic factor in gastric cancer.The quantitative parameters based on the novel dual-layer spectral detector computed tomography(DLSDCT)in discriminating the Ki-67 expression status are unclear.AIM To investigate the diagnostic ability of DLSDCT-derived parameters for Ki-67 expression status in gastric carcinoma(GC).METHODS Dual-phase enhanced abdominal DLSDCT was performed preoperatively in 108 patients with gastric adenocarcinoma.Primary tumor monoenergetic CT attenuation value at 40-100 kilo electron volt(kev),the slope of the spectral curve(λ_(HU)),iodine concentration(IC),normalized IC(nIC),effective atomic number(Z^(eff))and normalized Z^(eff)(nZ^(eff))in the arterial phase(AP)and venous phase(VP)were retrospectively compared between patients with low and high Ki-67 expression in gastric adenocarcinoma.Spearman’s correlation coefficient was used to analyze the association between the above parameters and Ki-67 expression status.Receiver operating characteristic(ROC)curve analysis was performed to compare the diagnostic efficacy of the statistically significant parameters between two groups.RESULTS Thirty-seven and 71 patients were classified as having low and high Ki-67 expression,respectively.CT_(40 kev-VP),CT_(70 kev-VP),CT_(100 kev-VP),and Z^(eff)-related parameters were significantly higher,but IC-related parameters were lower in the group with low Ki-67 expression status than the group with high Ki-67 expression status,and other analyzed parameters showed no statistical difference between the two groups.Spearman’s correlation analysis showed that CT_(40 kev-VP),CT_(70 kev-VP),CT_(100 kev-VP),Z^(eff),and n Z^(eff) exhibited a negative correlation with Ki-67 status,whereas IC and nIC had positive correlation with Ki-67 status.The ROC analysis demonstrated that the multi-variable model of spectral parameters performed well in identifying the Ki-67 status[area under the curve(AUC)=0.967;sensitivity 95.77%;specificity 91.89%)].Nevertheless,the differentiating capabilities of singlevariable model were moderate(AUC value 0.630-0.835).In addition,the nZ_(VP)^(eff) and nIC_(VP)(AUC 0.835 and 0.805)showed better performance than CT_(40 kev-VP),CT_(70 kev-VP) and CT_(100 kev-VP)(AUC 0.630,0.631 and 0.662)in discriminating the Ki-67 status.CONCLUSION Quantitative spectral parameters are feasible to distinguish low and high Ki-67 expression in gastric adenocarcinoma.Z^(eff) and IC may be useful parameters for evaluating the Ki-67 expression.展开更多
基金This research was primarily supported by a NOAA Warn-on-Forecast(WoF)grant(Grant No.NA16OAR4320115).
文摘There are more uncertainties with ice hydrometeor representations and related processes than liquid hydrometeors within microphysics parameterization(MP)schemes because of their complicated geometries and physical properties.Idealized supercell simulations are produced using the WRF model coupled with“full”Hebrew University spectral bin MP(HU-SBM),and NSSL and Thompson bulk MP(BMP)schemes.HU-SBM downdrafts are typically weaker than those of the NSSL and Thompson simulations,accompanied by less rain evaporation.HU-SBM produces more cloud ice(plates),graupel,and hail than the BMPs,yet precipitates less at the surface.The limiting mass bins(and subsequently,particle size)of rimed ice in HU-SBM and slower rimed ice fall speeds lead to smaller melting-level net rimed ice fluxes than those of the BMPs.Aggregation from plates in HU-SBM,together with snow–graupel collisions,leads to a greater snow contribution to rain than those of the BMPs.Replacing HU-SBM’s fall speeds using the formulations of the BMPs after aggregating the discrete bin values to mass mixing ratios and total number concentrations increases net rain and rimed ice fluxes.Still,they are smaller in magnitude than bulk rain,NSSL hail,and Thompson graupel net fluxes near the surface.Conversely,the melting-layer net rimed ice fluxes are reduced when the fall speeds for the NSSL and Thompson simulations are calculated using HU-SBM fall speed formulations after discretizing the bulk particle size distributions(PSDs)into spectral bins.The results highlight precipitation sensitivity to storm dynamics,fall speed,hydrometeor evolution governed by process rates,and MP PSD design.
基金Project supported by the Shanghai Science and Technology Innovation Action(Grant No.22dz1208700).
文摘Pulse echo accumulation is commonly employed in coherent Doppler wind LiDAR(light detection and ranging)under the assumption of steady wind.Here,the measured spectral data are analyzed in the time dimension and frequency dimension to cope with the temporal wind shear and achieve the optimal accumulation time.A hardware-efficient algorithm combining the interpolation and cross-correlation is used to enhance the wind retrieval accuracy by reducing the frequency sampling interval and then reduce the spectral width calculation error.Moreover,the temporal broadening effect and spatial broadening effect are decoupled according to the strategy we developed.
基金Supported by Science and Technology Project of Fujian Province,No.2022Y0025.
文摘BACKGROUND Lymphovascular invasion(LVI)and perineural invasion(PNI)are important prognostic factors for gastric cancer(GC)that indicate an increased risk of metastasis and poor outcomes.Accurate preoperative prediction of LVI/PNI status could help clinicians identify high-risk patients and guide treatment deci-sions.However,prior models using conventional computed tomography(CT)images to predict LVI or PNI separately have had limited accuracy.Spectral CT provides quantitative enhancement parameters that may better capture tumor invasion.We hypothesized that a predictive model combining clinical and spectral CT parameters would accurately preoperatively predict LVI/PNI status in GC patients.AIM To develop and test a machine learning model that fuses spectral CT parameters and clinical indicators to predict LVI/PNI status accurately.METHODS This study used a retrospective dataset involving 257 GC patients(training cohort,n=172;validation cohort,n=85).First,several clinical indicators,including serum tumor markers,CT-TN stages and CT-detected extramural vein invasion(CT-EMVI),were extracted,as were quantitative spectral CT parameters from the delineated tumor regions.Next,a two-step feature selection approach using correlation-based methods and information gain ranking inside a 10-fold cross-validation loop was utilized to select informative clinical and spectral CT parameters.A logistic regression(LR)-based nomogram model was subsequently constructed to predict LVI/PNI status,and its performance was evaluated using the area under the receiver operating characteristic curve(AUC).RESULTS In both the training and validation cohorts,CT T3-4 stage,CT-N positive status,and CT-EMVI positive status are more prevalent in the LVI/PNI-positive group and these differences are statistically significant(P<0.05).LR analysis of the training group showed preoperative CT-T stage,CT-EMVI,single-energy CT values of 70 keV of venous phase(VP-70 keV),and the ratio of standardized iodine concentration of equilibrium phase(EP-NIC)were independent influencing factors.The AUCs of VP-70 keV and EP-NIC were 0.888 and 0.824,respectively,which were slightly greater than those of CT-T and CT-EMVI(AUC=0.793,0.762).The nomogram combining CT-T stage,CT-EMVI,VP-70 keV and EP-NIC yielded AUCs of 0.918(0.866-0.954)and 0.874(0.784-0.936)in the training and validation cohorts,which are significantly higher than using each of single independent factors(P<0.05).CONCLUSION The study found that using portal venous and EP spectral CT parameters allows effective preoperative detection of LVI/PNI in GC,with accuracy boosted by integrating clinical markers.
文摘In Côte d’Ivoire, the recurring and unregulated use of bushfires, which cause ecological damage, presents a pressing concern for the custodians of protected areas. This study aims to enhance our comprehension of the dynamics of burnt areas within the Abokouamékro Wildlife Reserve (AWR) by employing the analysis of spectral indices derived from satellite imagery. The research methodology began with the calculation of mean indices and their corresponding spectral sub-indices, including NDVI, SAVI, NDWI, NDMI, BAI, NBR, TCW, TCG, and TCB, utilizing data from the Sentinel-2A satellite image dated January 17, 2022. Subsequently, a fuzzy classification model was applied to these various indices and sub-indices, guided by the degree of membership α, with the goal of effectively distinguishing between burned and unburned areas. Following the classification, the accuracies of the classified indices and sub-indices were validated using the coordinates of 100 data points collected within the AWR through GPS technology. The results revealed that the overall accuracy of all indices and sub-indices declines as the degree of membership α decreases from 1 to 0. Among the mean spectral indices, NDVI-mean, SAVI-mean, NDMI-mean exhibited the highest overall accuracies, achieving 97%, 95%, and 90%, respectively. These results closely mirrored those obtained by sub-indices using band 8 (NDVI-B8, SAVI-B8, and NDMI-B8), which yield respective overall accuracies of 93%, 92%, and 89%. At a degree of membership α = 1, the estimated burned areas for the most effective indices encompassed 2144.38 hectares for NDVI-mean, 1932.14 hectares for mean SAVI-mean, and 4947.13 hectares for mean NDMI-mean. A prospective approach involving the amalgamation of these three indices could have the potential to yield improved outcomes. This study could be a substantial contribution to the discrimination of bushfires in Côte d’Ivoire.
文摘Spectral unmixing helps to identify different components present in the spectral mixtures which occur in the uppermost layer of the area owing to the low spatial resolution of hyperspectral images.Most spectral unmixing methods are globally based and do not consider the spectral variability among its endmembers that occur due to illumination,atmospheric,and environmental conditions.Here,endmember bundle extraction plays a major role in overcoming the above-mentioned limitations leading to more accurate abundance fractions.Accordingly,a two-stage approach is proposed to extract endmembers through endmember bundles in hyperspectral images.The divide and conquer method is applied as the first step in subset images with only the non-redundant bands to extract endmembers using the Vertex Component Analysis(VCA)and N-FINDR algorithms.A fuzzy rule-based inference system utilizing spectral matching parameters is proposed in the second step to categorize endmembers.The endmember with the minimum error is chosen as the final endmember in each specific category.The proposed method is simple and automatically considers endmember variability in hyperspectral images.The efficiency of the proposed method is evaluated using two real hyperspectral datasets.The average spectral angle and abundance angle are used to analyze the performance measures.
基金supported by Light of West China(No.XAB2022YN10)Shaanxi Key Rsearch and Development Plan(No.2018ZDXM-SF-093)Shaanxi Province Key Industrial Innovation Chain(Nos.S2022-YF-ZDCXL-ZDLGY-0093,2023-ZDLGY-45).
文摘Sanxingdui cultural relics are the precious cultural heritage of humanity with high values of history,science,culture,art and research.However,mainstream analytical methods are contacting and detrimental,which is unfavorable to the protection of cultural relics.This paper improves the accuracy of the extraction,location,and analysis of artifacts using hyperspectral methods.To improve the accuracy of cultural relic mining,positioning,and analysis,the segmentation algorithm of Sanxingdui cultural relics based on the spatial spectrum integrated network is proposed with the support of hyperspectral techniques.Firstly,region stitching algorithm based on the relative position of hyper spectrally collected data is proposed to improve stitching efficiency.Secondly,given the prominence of traditional HRNet(High-Resolution Net)models in high-resolution data processing,the spatial attention mechanism is put forward to obtain spatial dimension information.Thirdly,in view of the prominence of 3D networks in spectral information acquisition,the pyramid 3D residual network model is proposed to obtain internal spectral dimensional information.Fourthly,four kinds of fusion methods at the level of data and decision are presented to achieve cultural relic labeling.As shown by the experiment results,the proposed network adopts an integrated method of data-level and decision-level,which achieves the optimal average accuracy of identification 0.84,realizes shallow coverage of cultural relics labeling,and effectively supports the mining and protection of cultural relics.
基金funded by the Key Research and Development Program of Shaanxi Province of China(2022NY-063)the Chinese Universities Scientific Fund(2452020018).
文摘The accurate and rapid estimation of canopy nitrogen content(CNC)in crops is the key to optimizing in-season nitrogen fertilizer application in precision agriculture.However,the determination of CNC from field sampling data for leaf area index(LAI),canopy photosynthetic pigments(CPP;including chlorophyll a,chlorophyll b and carotenoids)and leaf nitrogen concentration(LNC)can be time-consuming and costly.Here we evaluated the use of high-precision unmanned aerial vehicle(UAV)multispectral imagery for estimating the LAI,CPP and CNC of winter wheat over the whole growth period.A total of 23 spectral features(SFs;five original spectrum bands,17 vegetation indices and the gray scale of the RGB image)and eight texture features(TFs;contrast,entropy,variance,mean,homogeneity,dissimilarity,second moment,and correlation)were selected as inputs for the models.Six machine learning methods,i.e.,multiple stepwise regression(MSR),support vector regression(SVR),gradient boosting decision tree(GBDT),Gaussian process regression(GPR),back propagation neural network(BPNN)and radial basis function neural network(RBFNN),were compared for the retrieval of winter wheat LAI,CPP and CNC values,and a double-layer model was proposed for estimating CNC based on LAI and CPP.The results showed that the inversion of winter wheat LAI,CPP and CNC by the combination of SFs+TFs greatly improved the estimation accuracy compared with that by using only the SFs.The RBFNN and BPNN models outperformed the other machine learning models in estimating winter wheat LAI,CPP and CNC.The proposed double-layer models(R^(2)=0.67-0.89,RMSE=13.63-23.71 mg g^(-1),MAE=10.75-17.59 mg g^(-1))performed better than the direct inversion models(R^(2)=0.61-0.80,RMSE=18.01-25.12 mg g^(-1),MAE=12.96-18.88 mg g^(-1))in estimating winter wheat CNC.The best winter wheat CNC accuracy was obtained by the double-layer RBFNN model with SFs+TFs as inputs(R^(2)=0.89,RMSE=13.63 mg g^(-1),MAE=10.75 mg g^(-1)).The results of this study can provide guidance for the accurate and rapid determination of winter wheat canopy nitrogen content in the field.
文摘Let G be a graph and A(G) the adjacency matrix of G. The spectrum of G is the eigenvalues together with their multiplicities of A(G). Chang et al. (2011) characterized the structures of all graphs with rank 4. Monsalve and Rada (2021) gave the bound of spectral radius of all graphs with rank 4. Based on these results as above, we further investigate the spectral properties of graphs with rank 4. And we give the expressions of the spectral radius and energy of all graphs with rank 4. In particular, we show that some graphs with rank 4 are determined by their spectra.
基金National Natural Science Foundation of China(No.62001098)Fundamental Research Funds for the Central Universities of Ministry of Education of China(No.2232020D-33)。
文摘Deep learning(DL)has shown its superior performance in dealing with various computer vision tasks in recent years.As a simple and effective DL model,autoencoder(AE)is popularly used to decompose hyperspectral images(HSIs)due to its powerful ability of feature extraction and data reconstruction.However,most existing AE-based unmixing algorithms usually ignore the spatial information of HSIs.To solve this problem,a hypergraph regularized deep autoencoder(HGAE)is proposed for unmixing.Firstly,the traditional AE architecture is specifically improved as an unsupervised unmixing framework.Secondly,hypergraph learning is employed to reformulate the loss function,which facilitates the expression of high-order similarity among locally neighboring pixels and promotes the consistency of their abundances.Moreover,L_(1/2)norm is further used to enhance abundances sparsity.Finally,the experiments on simulated data,real hyperspectral remote sensing images,and textile cloth images are used to verify that the proposed method can perform better than several state-of-the-art unmixing algorithms.
基金funded by the Turkish General Directorate of Forestry(project number:19.9402/2020-2023)。
文摘Fire severity classifications determine fire damage and regeneration potential in post-fire areas for effective implementation of restoration applications.Since fire damage varies according to vegetation and fire characteristics,regional assessment of fire severity is crucial.The objectives of this study were:(1)to test the performance of different satellite imagery and spectral indices,and two field—measured severity indices,CBI(Composite Burn Index)and GeoCBI(Geometrically structured Composite Burn Index)to assess fire severity;(2)to calculate classification thresholds for spectral indices that performed best in the study areas;and(3)to generate fire severity maps that could be used to determine the ecological impact of forest fires.Five large fires in Pinus brutia(Turkish pine)and Pinus nigra subsp.pallasiana var.pallasiana(Anatolian black pine)—dominated forests during 2020 and 2021 were selected as study sites.The results show that GeoCBI provided more reliable estimates of field—measured fire severity than CBI.While Sentinel-2 and Landsat-8/OLI images performed similarly well,MODIS performed poorly.Fire severity classification thresholds were determined for Sentinel-2 based RdNBR,dNBR,dSAVI,dNDVI,and dNDMI and Landsat-8/OLI based dNBR,dNDVI,and dSAVI.Among several spectral indices,the highest accuracy for fire severity classification was found for Sentinel-2 based RdNBR(72.1%)and Landsat-8/OLI based dNBR(69.2%).The results can be used to assess and map fire severity in forest ecosystems similar to those in this study.
文摘Dear Editor, This letter is concerned with a new hyperspectral fusion paradigm by simultaneously fusing hyperspectral, multispectral, and panchromatic images. Seeking an efficient prior about the target hyperspectral image(HSI), is vital for constructing an accurate fusion model in this problem. To this end, this work suggests a novel sparse tensor prior using patch-based sparse tensor dictionary learning.
基金supported by the National Key R&D Program of China(Grant No.:2017YFC1702003)the Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences(Grant No.:2019e12M-5-078).
文摘Traditional Chinese medicine(TCM)is a treasure of the Chinese nation,providing effective solutions to current medical requisites.Various spectral techniques are undergoing continuous development and provide new and reliable means for evaluating the efficacy and quality of TCM.Because spectral techniques are noninvasive,convenient,and sensitive,they have been widely applied to in vitro and in vivo TCM evaluation systems.In this paper,previous achievements and current progress in the research on spectral technologies(including fluorescence spectroscopy,photoacoustic imaging,infrared thermal imaging,laser-induced breakdown spectroscopy,hyperspectral imaging,and surface enhanced Raman spectroscopy)are discussed.The advantages and disadvantages of each technology are also presented.Moreover,the future applications of spectral imaging to identify the origins,components,and pesticide residues of TCM in vitro are elucidated.Subsequently,the evaluation of the efficacy of TCM in vivo is presented.Identifying future applications of spectral imaging is anticipated to promote medical research as well as scientific and technological explorations.
文摘Purpose:Our study aims to compare speech understanding in noise and spectral-temporal resolution skills with regard to the degree of hearing loss,age,hearing aid use experience and gender of hearing aid users.Methods:Our study included sixty-eight hearing aid users aged between 40-70 years,with bilateral mild and moderate symmetrical sensorineural hearing loss.Random gap detection test,Turkish matrix test and spectral-temporally modulated ripple test were implemented on the participants with bilateral hearing aids.The test results acquired were compared statistically according to different variables and the correlations were examined.Results:No statistically significant differences were observed for speech-in-noise recognition,spectraltemporal resolution among older and younger adults in hearing aid users(p>0.05).There wasn’t found a statistically significant difference among test outcomes as regards different hearing loss degrees(p>0.05).Higher performances were obtained in terms of temporal resolution in male participants and participants with more hearing aid use experience(p<0.05).Significant correlations were obtained between the results of speech-in-noise recognition,temporal resolution and spectral resolution tests performed with hearing aids(p<0.05).Conclusion:Our study findings emphasized the importance of regular hearing aid use and it showed that some auditory skills can be improved with hearing aids.Observation of correlations among the speechin-noise recognition,temporal resolution and spectral resolution tests have revealed that these skills should be evaluated as a whole to maximize the patient’s communication abilities.
基金supported by Jilin Changbaishan Volcano National Observation and Research Station(Project No.NORSCBS20-04)National Natural Science Foundation of China(42174023)the Fundamental Research Fund for the Central Universities of Central South University(No.506021722).
文摘Sentinel-1A/B data are crucial for retrieving numerical information about surface phenomena and processes.Coregistration of terrain observation by progressive scans(TOPS)data is a critical step in its application.TOPS data must be fundamentally co-registered with an accuracy of 0.001 pixels.However,various decorrelation factors due to natural vegetation and seasonal effects affect the coregistration accuracy of TOPS data.This paper proposed an enhanced spectral diversity coregistration method for dual-polarimetric(PolESD)Sentinel-1A/B TOPS data.The PolESD method suppresses speckle noise based on a unified non-local framework in dual-pol Synthetic Aperture Radar(SAR),and extracts the phase of the optimal polarization channel from the denoised polarimetric interferometric coherency matrix.Compared with the traditional ESD method developed for single-polarization data,the PolESD method can obtain more accurate coherence and phase and get more pixels for azimuth-offset estimation.In bare areas covered with low vegetation,the number of pixels selected by PolESD is more than the Boxcar method.It can also correct misregistration more effectively and eliminate phase jumps in the burst edge.Therefore,PolESD will help improve the application of TOPS data in low-coherence scenarios.
基金the National Natural Science Foundation of China(Grant No.62176217)the Program from the Sichuan Provincial Science and Technology,China(Grant No.2018RZ0081)the Fundamental Research Funds of China West Normal University(Grant No.17E063)。
文摘One of the hot research topics in propagation dynamics is identifying a set of critical nodes that can influence maximization in a complex network.The importance and dispersion of critical nodes among them are both vital factors that can influence maximization.We therefore propose a multiple influential spreaders identification algorithm based on spectral graph theory.This algorithm first quantifies the role played by the local structure of nodes in the propagation process,then classifies the nodes based on the eigenvectors of the Laplace matrix,and finally selects a set of critical nodes by the constraint that nodes in the same class are not adjacent to each other while different classes of nodes can be adjacent to each other.Experimental results on real and synthetic networks show that our algorithm outperforms the state-of-the-art and classical algorithms in the SIR model.
基金Project supported by the U.S.Department of Energy under the Advanced Scientific Computing Research Program(No.DE-SC0019116)the U.S.Air Force Office of Scientific Research(No.AFOSR FA9550-20-1-0060)。
文摘We propose a self-supervising learning framework for finding the dominant eigenfunction-eigenvalue pairs of linear and self-adjoint operators.We represent target eigenfunctions with coordinate-based neural networks and employ the Fourier positional encodings to enable the approximation of high-frequency modes.We formulate a self-supervised training objective for spectral learning and propose a novel regularization mechanism to ensure that the network finds the exact eigenfunctions instead of a space spanned by the eigenfunctions.Furthermore,we investigate the effect of weight normalization as a mechanism to alleviate the risk of recovering linear dependent modes,allowing us to accurately recover a large number of eigenpairs.The effectiveness of our methods is demonstrated across a collection of representative benchmarks including both local and non-local diffusion operators,as well as high-dimensional time-series data from a video sequence.Our results indicate that the present algorithm can outperform competing approaches in terms of both approximation accuracy and computational cost.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61571018 and 61531003).
文摘Micrometric-thin cells(MCs)with alkali vapor atoms have been valuable for research and applications of hyperfine Zeeman splitting and atomic magnetometers under strong magnetic fields.We theoretically and experimentally study the saturated absorption spectra using a 100-μm cesium MC,where the pump and probe beams are linearly polarized with mutually perpendicular polarizations,and the magnetic field is along the pump beam.Because of the distinctive thin chamber of the MC,crossover spectral lines in saturated absorption spectra are largely suppressed leading to clear splittings of hyperfine Zeeman transitions in experiments,and the effect of spatial magnetic field gradient is expected to be reduced.A calculation method is proposed to achieve good agreements between theoretical calculations and experimental results.This method successfully explains the suppression of crossover lines in MCs,as well as the effects of magnetic field direction,propagation and polarization directions of the pump/probe beam on saturated absorption spectrum.The saturated absorption spectrum with suppressed crossover lines is used for laser frequency stabilization,which may provide the potential value of MCs for high spatial resolution strong-field magnetometry with high sensitivity.
文摘Objective Ureteral lesions caused by impacted ureteral stones are likely to result in postoperative ureteral stricture.On this basis,the study aimed to investigate if dual-energy spectral computed tomography can predict ureteral hardening caused by impacted stones and to explore the relationship between different types of ureteral lesions and the risk of ureteral stricture.Methods This prospective study collected data of 93 patients with impacted stones from hospital automation system during January 2018 to October 2019.They underwent an abdominal scan on a dual-energy spectral computed tomography.During surgery,the operator used ureteroscopy to identify ureteral lesions,which were classified into four categories:edema,polyps,pallor,and hardening.Seven months later,90 patients were reviewed for the degree of hydronephrosis.Results Endoscopic observations revealed 38(41%)cases of ureteral edema,20(22%)cases of polyps,13(14%)cases of pallor,and 22(24%)cases of hardening.There were significant differences in hydronephrosis,the period of impaction,the calcium concentration of the ureter,and the slope of the spectral Hounsfield unit curve between the four groups.After that,we evaluated the factors associated with ureteral hardening and found that the calcium concentration of the ureter and hydronephrosis remained independent predictors of ureteral hardening.Receiver operating characteristic curve analysis showed that 5.3 mg/cm^(3)calcium concentration of the ureter is an optimal cut-off value to predict ureteral hardening.The result of follow-up showed that 80 patients had complete remission of hydronephrosis,with a complete remission rate of 61.9%(13/21)in the hardening group and 97.1%(67/69)in the non-hardening group(p<0.001).Conclusion Calcium concentration of the ureter is an independent predictor of ureteral hardening.Patients with ureteral hardening have more severe hydronephrosis after ureteroscopic lithotripsy.When the calcium concentration of the ureter is less than 5.3 mg/cm^(3),ureteral lesions should be actively treated.
文摘BACKGROUND The level of Ki-67 expression has served as a prognostic factor in gastric cancer.The quantitative parameters based on the novel dual-layer spectral detector computed tomography(DLSDCT)in discriminating the Ki-67 expression status are unclear.AIM To investigate the diagnostic ability of DLSDCT-derived parameters for Ki-67 expression status in gastric carcinoma(GC).METHODS Dual-phase enhanced abdominal DLSDCT was performed preoperatively in 108 patients with gastric adenocarcinoma.Primary tumor monoenergetic CT attenuation value at 40-100 kilo electron volt(kev),the slope of the spectral curve(λ_(HU)),iodine concentration(IC),normalized IC(nIC),effective atomic number(Z^(eff))and normalized Z^(eff)(nZ^(eff))in the arterial phase(AP)and venous phase(VP)were retrospectively compared between patients with low and high Ki-67 expression in gastric adenocarcinoma.Spearman’s correlation coefficient was used to analyze the association between the above parameters and Ki-67 expression status.Receiver operating characteristic(ROC)curve analysis was performed to compare the diagnostic efficacy of the statistically significant parameters between two groups.RESULTS Thirty-seven and 71 patients were classified as having low and high Ki-67 expression,respectively.CT_(40 kev-VP),CT_(70 kev-VP),CT_(100 kev-VP),and Z^(eff)-related parameters were significantly higher,but IC-related parameters were lower in the group with low Ki-67 expression status than the group with high Ki-67 expression status,and other analyzed parameters showed no statistical difference between the two groups.Spearman’s correlation analysis showed that CT_(40 kev-VP),CT_(70 kev-VP),CT_(100 kev-VP),Z^(eff),and n Z^(eff) exhibited a negative correlation with Ki-67 status,whereas IC and nIC had positive correlation with Ki-67 status.The ROC analysis demonstrated that the multi-variable model of spectral parameters performed well in identifying the Ki-67 status[area under the curve(AUC)=0.967;sensitivity 95.77%;specificity 91.89%)].Nevertheless,the differentiating capabilities of singlevariable model were moderate(AUC value 0.630-0.835).In addition,the nZ_(VP)^(eff) and nIC_(VP)(AUC 0.835 and 0.805)showed better performance than CT_(40 kev-VP),CT_(70 kev-VP) and CT_(100 kev-VP)(AUC 0.630,0.631 and 0.662)in discriminating the Ki-67 status.CONCLUSION Quantitative spectral parameters are feasible to distinguish low and high Ki-67 expression in gastric adenocarcinoma.Z^(eff) and IC may be useful parameters for evaluating the Ki-67 expression.
