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.

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.

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.

Computed tomography for prediction of esophageal variceal bleeding

This letter to the editor relates to the study entitled“The role of computed tomography for the prediction of esophageal variceal bleeding:Current status and future perspectives”.Esophageal variceal bleeding(EVB)is one of the most common and severe complications related to portal hypertension(PH).Despite marked advances in its management during the last three decades,EVB is still associated with significant morbidity and mortality.The risk of first EVB is related to the severity of both PH and liver disease,and to the size and endoscopic appearance of esophageal varices.Indeed,hepatic venous pressure gradient(HVPG)and esophagogastroduodenoscopy(EGD)are currently recognized as the“gold standard”and the diagnostic reference standard for the prediction of EVB,respectively.However,HVPG is an invasive,expensive,and technically complex procedure,not widely available in clinical practice,whereas EGD is mainly limited by its invasive nature.In this scenario,computed tomography(CT)has been recently proposed as a promising modality for the non-invasive prediction of EVB.While CT serves solely as a diagnostic tool and cannot replace EGD or HVPG for delivering therapeutic and physiological information,it has the potential to enhance the prediction of EVB more effectively when combined with liver disease scores,HVPG,and EGD.However,to date,evidence concerning the role of CT in this setting is still lacking,therefore we aim to summarize and discuss the current evidence concerning the role of CT in predicting the risk of EVB.

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.

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.

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%.

Optimization-based image reconstruction in x-ray computed tomography by sparsity exploitation of local continuity and nonlocal spatial self-similarity

The additional sparse prior of images has been the subject of much research in problems of sparse-view computed tomography（CT） reconstruction. A method employing the image gradient sparsity is often used to reduce the sampling rate and is shown to remove the unwanted artifacts while preserve sharp edges, but may cause blocky or patchy artifacts.To eliminate this drawback, we propose a novel sparsity exploitation-based model for CT image reconstruction. In the presented model, the sparse representation and sparsity exploitation of both gradient and nonlocal gradient are investigated.The new model is shown to offer the potential for better results by introducing a similarity prior information of the image structure. Then, an effective alternating direction minimization algorithm is developed to optimize the objective function with a robust convergence result. Qualitative and quantitative evaluations have been carried out both on the simulation and real data in terms of accuracy and resolution properties. The results indicate that the proposed method can be applied for achieving better image-quality potential with the theoretically expected detailed feature preservation.

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.

Lymphatic plastic bronchitis and primary chylothorax: A study based on computed tomography lymphangiography

BACKGROUND This study presents an evaluation of the computed tomography lymphangio-graphy(CTL)features of lymphatic plastic bronchitis(PB)and primary chylotho-rax to improve the diagnostic accuracy for these two diseases.AIM To improve the diagnosis of lymphatic PB or primary chylothorax,a retrospective analysis of the clinical features and CTL characteristics of 71 patients diagnosed with lymphatic PB or primary chylothorax was performed.METHODS The clinical and CTL data of 71 patients(20 with lymphatic PB,41 with primary chylothorax,and 10 with lymphatic PB with primary chylothorax)were collected retrospectively.CTL was performed in all patients.The clinical manifestations,CTL findings,and conventional chest CT findings of the three groups of patients were compared.The chi-square test or Fisher's exact test was used to compare the differences among the three groups.A difference was considered to be statistically significant when P<0.05.RESULTS(1)The percentages of abnormal contrast medium deposits on CTL in the three groups were as follows:Thoracic duct outlet in 14(70.0%),33(80.5%)and 8(80.0%)patients;peritracheal region in 18(90.0%),15(36.6%)and 8(80.0%)patients;pleura in 6(30.0%),33(80.5%)and 9(90.0%)patients;pericardium in 6(30.0%),6(14.6%)and 4(40.0%)patients;and hilum in 16(80.0%),11(26.8%)and 7(70.0%)patients;and(2)the abnormalities on conven-tional chest CT in the three groups were as follows:Ground-glass opacity in 19(95.0%),18(43.9%)and 8(80.0%)patients;atelectasis in 4(20.0%),26(63.4%)and 7(70.0%)patients;interlobular septal thickening in 12(60.0%),11(26.8%)and 3(30.0%)patients;bronchovascular bundle thickening in 14(70.0%),6(14.6%)and 4(40.0%)patients;localized mediastinal changes in 14(70.0%),14(34.1%),and 7(70.0%)patients;diffuse mediastinal changes in 6(30.0%),5(12.2%),and 3(30.0%)patients;cystic lesions in the axilla in 2(10.0%),6(14.6%),and 2(20.0%)patients;and cystic lesions in the chest wall in 0(0%),2(4.9%),and 2(4.9%)patients.CONCLUSION CTL is well suited to clarify the characteristics of lymphatic PB and primary chylothorax.This method is an excellent tool for diagnosing these two diseases.

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.

A deep learning model based on contrast-enhanced computed tomography for differential diagnosis of gallbladder carcinoma

Background:Gallbladder carcinoma(GBC)is highly malignant,and its early diagnosis remains difficult.This study aimed to develop a deep learning model based on contrast-enhanced computed tomography(CT)images to assist radiologists in identifying GBC.Methods:We retrospectively enrolled 278 patients with gallbladder lesions(>10 mm)who underwent contrast-enhanced CT and cholecystectomy and divided them into the training(n=194)and validation(n=84)datasets.The deep learning model was developed based on ResNet50 network.Radiomics and clinical models were built based on support vector machine(SVM)method.We comprehensively compared the performance of deep learning,radiomics,clinical models,and three radiologists.Results:Three radiomics features including LoG_3.0 gray-level size zone matrix zone variance,HHL firstorder kurtosis,and LHL gray-level co-occurrence matrix dependence variance were significantly different between benign gallbladder lesions and GBC,and were selected for developing radiomics model.Multivariate regression analysis revealed that age≥65 years[odds ratios(OR)=4.4,95%confidence interval(CI):2.1-9.1,P<0.001],lesion size(OR=2.6,95%CI:1.6-4.1,P<0.001),and CA-19-9>37 U/mL(OR=4.0,95%CI:1.6-10.0,P=0.003)were significant clinical risk factors of GBC.The deep learning model achieved the area under the receiver operating characteristic curve(AUC)values of 0.864(95%CI:0.814-0.915)and 0.857(95%CI:0.773-0.942)in the training and validation datasets,which were comparable with radiomics,clinical models and three radiologists.The sensitivity of deep learning model was the highest both in the training[90%(95%CI:82%-96%)]and validation[85%(95%CI:68%-95%)]datasets.Conclusions:The deep learning model may be a useful tool for radiologists to distinguish between GBC and benign gallbladder lesions.

The diagnostic yield for computed tomography pulmonary angiography in patients with anticoagulation

BACKGROUND:Patients who present to the emergency department(ED)for suspected pulmonary embolism(PE)are often on active oral anticoagulation(AC).However,the diagnostic yield of computed tomography pulmonary angiography(CTPA)in screening for PE in patients who present on AC has not been well characterized.We aim to investigate the diagnostic yield of CTPA in diagnosing PE depending on AC status.METHODS:We reviewed and analyzed the electronic medical records of patients who underwent CTPA for PE at a university hospital ED from June 1,2019,to March 25,2022.Primary outcome was the incidence of PE on CTPA depending on baseline AC status and indication for AC.RESULTS:Of 2,846 patients,242 were on AC for a history of venous thromboembolism(VTE),210 were on AC for other indications,and 2,394 were not on AC.The incidence of PE on CTPA was significantly lower in patients on AC for other indications(5.7%)when compared to patients on AC for prior VTE(24.3%)and patients not on AC at presentation(9.8%)(P<0.001).In multivariable analysis among the whole cohort,AC was associated with a positive CTPA(odds ratio[OR]0.26,95%confidence interval[CI]:0.15-0.45,P<0.001).CONCLUSION:The incidence of PE among patients undergoing CTPA in the ED is lower in patients previously on AC for indications other than VTE when compared to those not on AC or those on AC for history of VTE.AC status and indication for AC may affect pre-test probability of a positive CTPA,and AC status therefore warrants consideration as part of future diagnostic algorithms among patients with suspected PE.

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.

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.

Review on article of preoperative prediction in chronic hepatitis B virus patients using spectral computed tomography and machine learning

This letter comments on the article that developed and tested a machine learning model that predicts lymphovascular invasion/perineural invasion status by combining clinical indications and spectral computed tomography characteristics accurately.We review the research content,methodology,conclusions,strengths and weaknesses of the study,and introduce follow-up research to this work.

Whole-volume histogram analysis of spectral-computed tomography iodine maps characterizes HER2 expression in gastric cancer

BACKGROUND Although surgery remains the primary treatment for gastric cancer(GC),the identification of effective alternative treatments for individuals for whom surgery is unsuitable holds significance.HER2 overexpression occurs in approximately 15%-20%of advanced GC cases,directly affecting treatment-related decisions.Spectral-computed tomography(sCT)enables the quantification of material compositions,and sCT iodine concentration parameters have been demonstrated to be useful for the diagnosis of GC and prediction of its invasion depth,angioge-nesis,and response to systemic chemotherapy.No existing report describes the prediction of GC HER2 status through histogram analysis based on sCT iodine maps(IMs).AIM To investigate whether whole-volume histogram analysis of sCT IMs enables the prediction of the GC HER2 status.METHODS This study was performed with data from 101 patients with pathologically confirmed GC who underwent preoperative sCT examinations.Nineteen parameters were extracted via sCT IM histogram analysis:The minimum,maximum,mean,standard deviation,variance,coefficient of variation,skewness,kurtosis,entropy,percentiles(1st,5th,10th,25th,50th,75th,90th,95th,and 99th),and lesion volume.Spearman correlations of the parameters with the HER2 status and clinicopathological parameters were assessed.Receiver operating characteristic curves were used to evaluate the parameters’diagnostic performance.RESULTS Values for the histogram parameters of the maximum,mean,standard deviation,variance,entropy,and percentiles were significantly lower in the HER2+group than in the HER2–group(all P<0.05).The GC differentiation and Lauren classification correlated significantly with the HER2 status of tumor tissue(P=0.001 and 0.023,respectively).The 99th percentile had the largest area under the curve for GC HER2 status identification(0.740),with 76.2%,sensitivity,65.0%specificity,and 67.3%accuracy.All sCT IM histogram parameters correlated positively with the GC HER2 status(r=0.237-0.337,P=0.001-0.017).CONCLUSION Whole-lesion histogram parameters derived from sCT IM analysis,and especially the 99th percentile,can serve as imaging biomarkers of HER2 overexpression in GC.

Computed tomography-based radiomics to predict early recurrence of hepatocellular carcinoma post-hepatectomy in patients background on cirrhosis

BACKGROUND The prognosis for hepatocellular carcinoma(HCC)in the presence of cirrhosis is unfavourable,primarily attributable to the high incidence of recurrence.AIM To develop a machine learning model for predicting early recurrence(ER)of posthepatectomy HCC in patients with cirrhosis and to stratify patients’overall survival(OS)based on the predicted risk of recurrence.METHODS In this retrospective study,214 HCC patients with cirrhosis who underwent curative hepatectomy were examined.Radiomics feature selection was conducted using the least absolute shrinkage and selection operator and recursive feature elimination methods.Clinical-radiologic features were selected through univariate and multivariate logistic regression analyses.Five machine learning methods were used for model comparison,aiming to identify the optimal model.The model’s performance was evaluated using the receiver operating characteristic curve[area under the curve(AUC)],calibration,and decision curve analysis.Additionally,the Kaplan-Meier(K-M)curve was used to evaluate the stratification effect of the model on patient OS.RESULTS Within this study,the most effective predictive performance for ER of post-hepatectomy HCC in the background of cirrhosis was demonstrated by a model that integrated radiomics features and clinical-radiologic features.In the training cohort,this model attained an AUC of 0.844,while in the validation cohort,it achieved a value of 0.790.The K-M curves illustrated that the combined model not only facilitated risk stratification but also exhibited significant discriminatory ability concerning patients’OS.CONCLUSION The combined model,integrating both radiomics and clinical-radiologic characteristics,exhibited excellent performance in HCC with cirrhosis.The K-M curves assessing OS revealed statistically significant differences.