Simultaneous fluorescence and Compton scattering computed tomography based on linear polarization X-ray

Purpose To propose a method for simultaneous fluorescence and Compton scattering computed tomography by using linearly polarized X-rays.Methods Monte Carlo simulations were adopted to demonstrate the feasibility of the proposed method.In the simulations,the phantom is a polytetrafluoroethylene cylinder inside which are cylindrical columns containing aluminum,water,and gold(Au)-loaded water solutions with Au concentrations ranging between 0.5 and 4.0 wt%,and a parallel-hole collimator imaging geometry was adopted.The light source was modeled based on a Thomson scattering X-ray source.The phantom images for both imaging modalities were reconstructed using a maximumlikelihood expectation maximization algorithm.Results Both the X-ray fluorescence computed tomography(XFCT)and Compton scattering computed tomography(CSCT)images of the phantom were accurately reconstructed.A similar attenuation contrast problem for the different cylindrical columns in the phantom can be resolved in the XFCT and CSCT images.The interplay between XFCT and CSCT was analyzed,and the contrast-to-noise ratio(CNR)of the reconstruction was improved by correcting for the mutual influence between the two imaging modalities.Compared with K-edge subtraction imaging,XFCT exhibits a CNR advantage for the phantom.Conclusion Simultaneous XFCT and CSCT can be realized by using linearly polarized X-rays.The synergy between the two imaging modalities would have an important application in cancer radiation therapy.

A machine learning-based strategy for predicting the mechanical strength of coral reef limestone using X-ray computed tomography

Different sedimentary zones in coral reefs lead to significant anisotropy in the pore structure of coral reef limestone(CRL),making it difficult to study mechanical behaviors.With X-ray computed tomography(CT),112 CRL samples were utilized for training the support vector machine(SVM)-,random forest(RF)-,and back propagation neural network(BPNN)-based models,respectively.Simultaneously,the machine learning model was embedded into genetic algorithm(GA)for parameter optimization to effectively predict uniaxial compressive strength(UCS)of CRL.Results indicate that the BPNN model with five hidden layers presents the best training effect in the data set of CRL.The SVM-based model shows a tendency to overfitting in the training set and poor generalization ability in the testing set.The RF-based model is suitable for training CRL samples with large data.Analysis of Pearson correlation coefficient matrix and the percentage increment method of performance metrics shows that the dry density,pore structure,and porosity of CRL are strongly correlated to UCS.However,the P-wave velocity is almost uncorrelated to the UCS,which is significantly distinct from the law for homogenous geomaterials.In addition,the pore tensor proposed in this paper can effectively reflect the pore structure of coral framework limestone(CFL)and coral boulder limestone(CBL),realizing the quantitative characterization of the heterogeneity and anisotropy of pore.The pore tensor provides a feasible idea to establish the relationship between pore structure and mechanical behavior of CRL.

Seeing through Materials:X-Ray Imaging Using Computed Tomography

X-ray computed tomography(XCT)has recently emerged as a powerful tool for characterizing the evolution of microstructure during phase transformation in three dimensional(3D)such as dendritic solidification of alloys.This paper briefly reviews the recent advances in the in-situ observation of aluminium alloys,magnesium alloys and nickel-based superalloys during solidification using laboratory XCT and synchrotron X-ray sources.The focus is on the growth kinetics of dendrites,porosity and secondary phases.In addition,in-situ characterization during the loading and corrosion process is also discussed.

Importance of computed tomography in posterior malleolar fractures:Added information to preoperative X-ray studies

BACKGROUND Ankle fractures are common lesions of the lower limbs.Approximately 40%of ankle fractures affect the posterior malleolus(PM).Historically,PM osteosynthesis was recommended when PM size in X-ray images was greater than 25%of the joint.Currently,computed tomography(CT)has been gaining traction in the preoperative evaluation of ankle fractures.AIM To elucidate the similarity in dimensions and to correlate PM size in X-ray images with the articular surface of the affected tibial plafond in the axial view on CT(AXCT)of a PM fracture.METHODS Eighty-one patients(mean age:39.4±13.5 years)were evaluated(54.3%were male).Two independent examiners measured PM size in profile X-ray images(PMXR)and sagittal CT(SAGCT)slices.The correlation of the measurements between the examiners and the difference in the PM fragment sizes between the two images were compared.Next,the PM size in PMXR was compared with the surface of the tibial plafond involved in the fracture in AXCT according to the Haraguchi classification.RESULTS The correlation rates between the examiners were 0.93 and 0.94 for PMXR and SAGCT,respectively(P<0.001).Fragments were 2.12%larger in SAGCT than in PMXR(P=0.018).In PMXR,there were 56 cases<25%and 25 cases≥25%.When PMXR was<25%,AXCT corresponded to 10.13%of the tibial plafond.When PMXR was≥25%,AXCT was 24.52%(P<0.001).According to the Haraguchi classification,fracture types I and II had similar PMXR measurements that were greater than those of type III.When analyzing AXCT,a significant difference was found between the three types,with II>I>III(P<0.001).CONCLUSION PM fractures show different sizes using X-ray or CT images.CT showed a larger PM in the sagittal plane and allowed the visualization of the real dimensions of the tibial plafond surface.

Energy spectrum computed tomography multi-parameter imaging in preoperative assessment of vascular and neuroinvasive status in gastric cancer

BACKGROUND Vascular and nerve infiltration are important indicators for the progression and prognosis of gastric cancer(GC),but traditional imaging methods have some limitations in preoperative evaluation.In recent years,energy spectrum computed tomography(CT)multiparameter imaging technology has been gradually applied in clinical practice because of its advantages in tissue contrast and lesion detail display.AIM To explore and analyze the value of multiparameter energy spectrum CT imaging in the preoperative assessment of vascular invasion(LVI)and nerve invasion(PNI)in GC patients.METHODS Data from 62 patients with GC confirmed by pathology and accompanied by energy spectrum CT scanning at our hospital between September 2022 and September 2023,including 46 males and 16 females aged 36-71(57.5±9.1)years,were retrospectively collected.The patients were divided into a positive group(42 patients)and a negative group(20 patients)according to the presence of LVI/PNI.The CT values(CT40 keV,CT70 keV),iodine concentration(IC),and normalized IC(NIC)of lesions in the upper energy spectrum CT images of the arterial phase,venous phase,and delayed phase 40 and 70 keV were measured,and the slopes of the energy spectrum curves[K(40-70)]from 40 to 70 keV were calculated.Arterial Core Tip:To investigate the application value of multiparameter energy spectrum computed tomography(CT)imaging in the preoperative assessment of vascular and nerve infiltration in patients with gastric cancer(GC).The imaging data of GC patients were retrospectively analyzed to evaluate the accuracy and sensitivity of CT for identifying and quantifying vascular and nerve infiltration and for comparison with postoperative pathological results.The purpose of this study was to verify the clinical feasibility and potential advantages of multiparameter energy spectrum CT imaging in guiding preoperative diagnosis and treatment decision-making and to provide a new imaging basis for improving the diagnostic accuracy and prognosis of GC patients.

Deep learning model combined with computed tomography features to preoperatively predicting the risk stratification of gastrointestinal stromal tumors

BACKGROUND Gastrointestinal stromal tumors(GIST)are prevalent neoplasm originating from the gastrointestinal mesenchyme.Approximately 50%of GIST patients experience tumor recurrence within 5 years.Thus,there is a pressing need to accurately evaluate risk stratification preoperatively.AIM To assess the application of a deep learning model(DLM)combined with computed tomography features for predicting risk stratification of GISTs.METHODS Preoperative contrast-enhanced computed tomography(CECT)images of 551 GIST patients were retrospectively analyzed.All image features were independently analyzed by two radiologists.Quantitative parameters were statistically analyzed to identify significant predictors of high-risk malignancy.Patients were randomly assigned to the training(n=386)and validation cohorts(n=165).A DLM and a combined DLM were established for predicting the GIST risk stratification using convolutional neural network and subsequently evaluated in the validation cohort.RESULTS Among the analyzed CECT image features,tumor size,ulceration,and enlarged feeding vessels were identified as significant risk predictors(P<0.05).In DLM,the overall area under the receiver operating characteristic curve(AUROC)was 0.88,with the accuracy(ACC)and AUROCs for each stratification being 87%and 0.96 for low-risk,79%and 0.74 for intermediate-risk,and 84%and 0.90 for high-risk,respectively.The overall ACC and AUROC were 84%and 0.94 in the combined model.The ACC and AUROCs for each risk stratification were 92%and 0.97 for low-risk,87%and 0.83 for intermediate-risk,and 90%and 0.96 for high-risk,respectively.Differences in AUROCs for each risk stratification between the two models were significant(P<0.05).CONCLUSION A combined DLM with satisfactory performance for preoperatively predicting GIST stratifications was developed using routine computed tomography data,demonstrating superiority compared to DLM.

Impact of computed tomography/magnetic resonance imaging registration on rehabilitation after percutaneous endoscopic decompression for lumbar stenosis: Retrospective study

BACKGROUND Percutaneous endoscopic lumbar decompression(PELD)shows promise for lumbar spinal stenosis(LSS)treatment,but its use is limited by the disease's complexity and procedural challenges.AIM In this study,the effects of preoperative planning and intraoperative guidance with computed tomography(CT)/magnetic resonance imaging(MRI)registration techniques on PELD for LSS and postoperative rehabilitation outcomes were evaluated.METHODS This retrospective study was conducted with data from patients who underwent PELD for LSS between January 2021 and December 2023.Patients were assigned to preoperative CT/MRI registration and control groups.Data collected included the operative time,length of hospital stay,visual analog scale(VAS)scores for low back and leg pain,and the Japanese Orthopaedic Association(JOA)lumbar spine score.Differences between groups were assessed using Student’s t test.RESULTS Data from 135 patients(71 in the CT/MRI registration group,64 in the control group)were analyzed.The operative time was significantly shorter in the CT/MRI registration group(P=0.007).At 2 months postoperatively,both groups showed significant reductions in VAS leg and low back pain scores(all P<0.001)and improvements in the JOA score(both P<0.001).No complication or death occurred.Preoperatively,pain and JOA scores were similar between groups(P=0.830,P=0.470,and P=0.287,respectively).At 2 months postoperatively,patients in the CT/MRI registration group reported lower leg and low back pain levels(P<0.001 and P=0.001,respectively)and had higher JOA scores(P=0.004)than did patients in the control group.CONCLUSION Preoperative CT/MRI registration for PELD for LSS reduced the operative time and VAS pain scores at 2 months and improved JOA scores,demonstrating enhanced effectiveness and safety.

Use of X-ray computed tomography to study structures and particle contacts of granite residual soil

A small problem about soil particle regularization and contacts but essential to geotechnical engineering was studied.The soils sourced from Guangzhou and Xiamen were sieved into five different particle scale ranges(d<0.075 mm,0.075 mm≤d<0.1 mm,0.1 mm≤d<0.2 mm,0.2 mm≤d<0.5 mm and 0.5 mm≤d<1.0 mm)to study the structures and particle contacts of granite residual soil.The X-ray micro computed tomography method was used to reconstruct the microstructure of granite residual soil.The particle was identified and regularized using principal component analysis(PCA).The particle contacts and geometrical characteristics in 3D space were analyzed and summarized using statistical analyses.The results demonstrate that the main types of contact among the particles are face-face,face-angle,face-edge,edge-edge,edge-angle and angle-angle contacts for particle sizes less than 0.2 mm.When the particle sizes are greater than 0.2 mm,the contacts are effectively summarized as face-face,face-angle,face-edge,edge-edge,edge-angle,angle-angle,sphere-sphere,sphere-face,sphere-edge and sphere-angle contacts.The differences in porosity among the original sample,reconstructed sample and regularized sample are closely related to the water-swelling and water-disintegrable characteristics of granite residual soil.

Use of high-resolution X-ray computed tomography and 3D image analysis to quantify mineral dissemination and pore space in oxide copper ore particles

Mineral dissemination and pore space distribution in ore particles are important features that influence heap leaching performance. To quantify the mineral dissemination and pore space distribution of an ore particle, a cylindrical copper oxide ore sample (I center dot 4.6 mm x 5.6 mm) was scanned using high-resolution X-ray computed tomography (HRXCT), a nondestructive imaging technology, at a spatial resolution of 4.85 mu m. Combined with three-dimensional (3D) image analysis techniques, the main mineral phases and pore space were segmented and the volume fraction of each phase was calculated. In addition, the mass fraction of each mineral phase was estimated and the result was validated with that obtained using traditional techniques. Furthermore, the pore phase features, including the pore size distribution, pore surface area, pore fractal dimension, pore centerline, and the pore connectivity, were investigated quantitatively. The pore space analysis results indicate that the pore size distribution closely fits a log-normal distribution and that the pore space morphology is complicated, with a large surface area and low connectivity. This study demonstrates that the combination of HRXCT and 3D image analysis is an effective tool for acquiring 3D mineralogical and pore structural data.

Microstructure Evolution of Sandstone Cemented by Microbe Cement Using X-ray Computed Tomography

The bio-sandstone, which was cemented by microbe cement, was firstly prepared, and then the microstructure evolution process was studied by X-ray computed tomography （X-CT） technique. The experimental results indicate that the microstructure of bio-sandstone becomes dense with the development of age. The evolution of inner structure at different positions is different due to the different contents of microbial induced precipitation calcite. Besides, the increase rate of microbial induced precipitation calcite gradually decreases because of the reduction of microbe absorption content with the decreasing pore size in bio-sandstone.

Effects of dry-wet cycles on three-dimensional pore structure and permeability characteristics of granite residual soil using X-ray micro computed tomography

Due to seasonal climate alterations,the microstructure and permeability of granite residual soil are easily affected by multiple dry-wet cycles.The X-ray micro computed tomography(micro-CT)acted as a nondestructive tool for characterizing the microstructure of soil samples exposed to a range of damage levels induced by dry-wet cycles.Subsequently,the variations of pore distribution and permeability due to drywet cycling effects were revealed based on three-dimensional(3D)pore distribution analysis and seepage simulations.According to the results,granite residual soils could be separated into four different components,namely,pores,clay,quartz,and hematite,from micro-CT images.The reconstructed 3D pore models dynamically demonstrated the expanding and connecting patterns of pore structures during drywet cycles.The values of porosity and connectivity are positively correlated with the number of dry-wet cycles,which were expressed by exponential and linear functions,respectively.The pore volume probability distribution curves of granite residual soil coincide with the χ^(2)distribution curve,which verifies the effectiveness of the assumption of χ^(2)distribution probability.The pore volume distribution curves suggest that the pores in soils were divided into four types based on their volumes,i.e.micropores,mesopores,macropores,and cracks.From a quantitative and visual perspective,considerable small pores are gradually transformed into cracks with a large volume and a high connectivity.Under the action of dry-wet cycles,the number of seepage flow streamlines which contribute to water permeation in seepage simulation increases distinctly,as well as the permeability and hydraulic conductivity.The calculated hydraulic conductivity is comparable with measured ones with an acceptable error margin in general,verifying the accuracy of seepage simulations based on micro-CT results.

Quantification of 3D macropore networks in forest soils in Touzhai valley(Yunnan,China)using X-ray computed tomography and image analysis

The three dimensional （3D） geometry of soil macropores largely controls preferential flow, which is a significant infiltrating mechanism for rainfall in forest soils and affects slope stability. However, detailed studies on the 3D geometry of macropore networks in forest soils are rare. The intense rainfall-triggered potentially unstable slopes were threatening the villages at the downstream of Touzhai valley （Yunnan, China）. We visualized and quantified the 3D macropore networks in undisturbed soil columns （Histosols） taken from a forest hillslope in Touzhai valley, and compared them with those in agricultural soils （corn and soybean in USA; barley, fodder beet and red fescue in Denmark） and grassland soils in USA. We took two large undisturbed soil columns （250 mm^25o mmxsoo mm）, and scanned the soil columns at in-situ soil water content conditions using X-ray computed tomography at a voxel resolution of 0.945 × 0.945 × 1.500o mm^3. After reconstruction and visualization, we quantified the characteristics of macropore networks. In the studied forest soils, the main types of maeropores were root channels, inter-aggregate voids, maeropores without knowing origin, root-soil interfaee and stone-soil interface. While maeropore networks tend to be more eomplex, larger, deeper and longer. The forest soils have high maeroporosity, total maeropore wall area density, node density, and large maeropore volume, hydraulie radius, mean maeropore length, angle, and low tortuosity. The findings suggest that maeropore networks in the forest soils have high inter- connectivity, vertical continuity, linearity and less vertically oriented.

In situ compression and X-ray computed tomography of flow battery electrodes

Redox flow batteries offer a potential solution to an increase in renewable energy generation on the grid by offering long-term, large-scale storage and regulation of power. However, they are currently un- derutilised due to cost and performance issues, many of which are linked to the microstructure of the porous carbon electrodes used. Here, for the first time, we offer a detailed study of the in situ effects of compression on a commercially available carbon felt electrode. Visualisation of electrode structure us- ing X-ray computed tomography shows the non-linear way that these materials compress and various metrics are used to elucidate the changes in porosity, pore size distribution and tortuosity factor under compressions from 0%-90%.

Kernel crack characteristics for X-ray computed microtomography(μCT)and their relationship with the breakage rate of maize varieties

The most significant problem of maize grain mechanical harvesting quality in China at present is the high grain breakage rate(BR).BR is often the key characteristic that is measured to select hybrids desirable for mechanical grain harvesting.However,conventional BR evaluation and measurement methods have challenges and limitations.Microstructural crack parameters evaluation of maize kernel is of great importance to BR.In this connection,X-ray computed microtomography(μ-CT)has proven to be a quite useful method for the assessment of microstructure,as it provides important microstructural parameters,such as object volume,surface,surface/volume ratio,number of closed pores,and others.X-ray computed microtomography is a non-destructive technique that enables the reuse of samples already measured and also yields bidimensional(2D)cross-sectional images of the sample as well as volume rendering.In this paper,six different maize hybrid genotypes are used as materials,and the BR of the maize kernels of each variety is tested in the field mechanical grain harvesting,and the BR is used as an index for evaluating the breakage resistance of the variety.The crack characteristic parameters of kernel were detected by X-ray micro-computed tomography,and the relationship between the BR and the kernel crack characteristics was analyzed by stepwise regression analysis.Establishing a relationship between crack characteristic parameters and BR of maize is vital for judging breakage resistance.The results of stepwise multiple linear regression(MLR)showed that the crack characteristics of the object surface,number of closed pores,surface of closed pores,and closed porosity percent were significantly correlated to the BR of field mechanical grain harvesting,with the standard partial regression coefficients of–0.998,–0.988,–0.999,and–0.998,respectively.The R2 of this model was 0.999.Results validation showed that the Stepwise MLR Model could well predict the BR of maize based on these four variables.

Performance evaluation of the simpli¯ed spherical harmonics approximation for cone-beam X-ray luminescence computed tomography imaging

As an emerging molecular imaging modality,cone-beam X-ray luminescence computed tomog-raphy(CB-XLCT)uses X-ray-excitable probes to produce near-infrared(NIR)luminescence and then reconst ructs three-dimensional(3D)distribution of the probes from surface measurements.A proper photon-transportation model is critical to accuracy of XLCT.Here,we presented a systematic comparison between the common-used Monte Carlo model and simplified spherical harmonics(SPN).The performance of the two methods was evaluated over several main spec-trums using a known XLCT material.We designed both a global measurement based on the cosine similarity and a locally-averaged relative error,to quantitatively assess these methods.The results show that the SP_(3) could reach a good balance between the modeling accuracy and computational efficiency for all of the tested emission spectrums.Besides,the SP_(1)(which is equivalent to the difusion equation(DE))can be a reasonable alternative model for emission wavelength over 692nm.In vivo experiment further demonstrates the reconstruction perfor-mance of the SP:and DE.This study would provide a valuable guidance for modeling the photon-transportation in CB-XLCT.

Ring artifacts removal in X-ray-induced acoustic computed tomography

X-ray-induced acoustic computed tomography(XACT)is a hybrid imaging modality for detecting X-ray absorption distribution via ultrasound emission.It facilitates imaging from a single projection X-ray illumination,thus reducing the radiation exposure and improving imaging speed.Nonuniform detector response caused by the interference between multichannel data acquisition for ring array transducers and amplifier systems yields ring artifacts in the reconstructed XACT images,which compromises the image quality.We propose model-based algorithms for ring artifacts corrected XACT imaging and demonstrate their effcacy on numerical and experimental measurements.The corrected reconstructions indicate significantly reduced ring artifacts as compared to their conventional counterparts.

Distinct root system acclimation patterns of seagrass Zostera japonica in sediments of different trophic status:a research by X-ray computed tomography

Conspecific seagrass living in differing environments may develop different root system acclimation patterns.We applied X-ray computed tomography(CT)for imaging and quantifying roots systems of Zostera japonica collected from typical oligotrophic and eutrophic sediments in two coastal sites of northern China,and determined sediment physicochemical properties that might influence root system morphology,density,and distribution.The trophic status of sediments had little influence on the Z.japonica root length,and diameters of root and rhizome.However,Z.japonica in oligotrophic sediment developed the root system with longer rhizome node,deeper rhizome distribution,and larger allocation to below-ground tissues in order to acquire more nutrients and relieve the N deficiency.And the lower root and rhizome densities of Z.japonica in eutrophic sediment were mainly caused by fewer shoots and shorter longevity,which was resulted from the more serious sulfide inhibition.Our results systematically revealed the effect of sediment trophic status on the phenotypic plasticity,quantity,and distribution of Z.japonica root system,and demonstrated the feasibly of X-ray CT in seagrass root system research.

Preoperative prediction of lymphovascular and perineural invasion in gastric cancer using spectral computed tomography imaging and machine learning

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.

Double contrast-enhanced ultrasonography improves diagnostic accuracy of T staging compared with multi-detector computed tomography in gastric cancer patients

BACKGROUND Gastric cancer(GC)is the most common malignant tumor and ranks third for cancer-related deaths among the worldwide.The disease poses a serious public health problem in China,ranking fifth for incidence and third for mortality.Knowledge of the invasive depth of the tumor is vital to treatment decisions.AIM To evaluate the diagnostic performance of double contrast-enhanced ultrasonography(DCEUS)for preoperative T staging in patients with GC by comparing with multi-detector computed tomography(MDCT).METHODS This single prospective study enrolled patients with GC confirmed by preoperative gastroscopy from July 2021 to March 2023.Patients underwent DCEUS,including ultrasonography(US)and intravenous contrast-enhanced ultrasonography(CEUS),and MDCT examinations for the assessment of preoperative T staging.Features of GC were identified on DCEUS and criteria developed to evaluate T staging according to the 8th edition of AJCC cancer staging manual.The diagnostic performance of DCEUS was evaluated by comparing it with that of MDCT and surgical-pathological findings were considered as the gold standard.RESULTS A total of 229 patients with GC(80 T1,33 T2,59 T3 and 57 T4)were included.Overall accuracies were 86.9%for DCEUS and 61.1%for MDCT(P<0.001).DCEUS was superior to MDCT for T1(92.5%vs 70.0%,P<0.001),T2(72.7%vs 51.5%,P=0.041),T3(86.4%vs 45.8%,P<0.001)and T4(87.7%vs 70.2%,P=0.022)staging of GC.CONCLUSION DCEUS improved the diagnostic accuracy of preoperative T staging in patients with GC compared with MDCT,and constitutes a promising imaging modality for preoperative evaluation of GC to aid individualized treatment decision-making.

Evaluating microvascular invasion in hepatitis B virus-related hepatocellular carcinoma based on contrast-enhanced computed tomography radiomics and clinicoradiological factors

BACKGROUND Microvascular invasion(MVI)is a significant indicator of the aggressive behavior of hepatocellular carcinoma(HCC).Expanding the surgical resection margin and performing anatomical liver resection may improve outcomes in patients with MVI.However,no reliable preoperative method currently exists to predict MVI status or to identify patients at high-risk group(M2).AIM To develop and validate models based on contrast-enhanced computed tomo-graphy(CECT)radiomics and clinicoradiological factors to predict MVI and identify M2 among patients with hepatitis B virus-related HCC(HBV-HCC).The ultimate goal of the study was to guide surgical decision-making.METHODS A total of 270 patients who underwent surgical resection were retrospectively analyzed.The cohort was divided into a training dataset(189 patients)and a validation dataset(81)with a 7:3 ratio.Radiomics features were selected using intra-class correlation coefficient analysis,Pearson or Spearman’s correlation analysis,and the least absolute shrinkage and selection operator algorithm,leading to the construction of radscores from CECT images.Univariate and multivariate analyses identified significant clinicoradiological factors and radscores associated with MVI and M2,which were subsequently incorporated into predictive models.The models’performance was evaluated using calibration,discrimination,and clinical utility analysis.RESULTS Independent risk factors for MVI included non-smooth tumor margins,absence of a peritumoral hypointensity ring,and a high radscore based on delayed-phase CECT images.The MVI prediction model incorporating these factors achieved an area under the curve(AUC)of 0.841 in the training dataset and 0.768 in the validation dataset.The M2 prediction model,which integrated the radscore from the 5 mm peritumoral area in the CECT arterial phase,α-fetoprotein level,enhancing capsule,and aspartate aminotransferase level achieved an AUC of 0.865 in the training dataset and 0.798 in the validation dataset.Calibration and decision curve analyses confirmed the models’good fit and clinical utility.CONCLUSION Multivariable models were constructed by combining clinicoradiological risk factors and radscores to preoper-atively predict MVI and identify M2 among patients with HBV-HCC.Further studies are needed to evaluate the practical application of these models in clinical settings.