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.

Computed tomography-based nomogram of Siewert type Ⅱ/Ⅲ adenocarcinoma of esophagogastric junction to predict response to docetaxel, oxaliplatin and S-1

BACKGROUND Neoadjuvant chemotherapy(NAC)has become the standard care for advanced adenocarcinoma of esophagogastric junction(AEG),although a part of the patients cannot benefit from NAC.There are no models based on baseline computed tomography(CT)to predict response of Siewert type II or III AEG to NAC with docetaxel,oxaliplatin and S-1(DOS).AIM To develop a CT-based nomogram to predict response of Siewert type II/III AEG to NAC with DOS.METHODS One hundred and twenty-eight consecutive patients with confirmed Siewert type II/III AEG underwent CT before and after three cycles of NAC with DOS,and were randomly and consecutively assigned to the training cohort(TC)(n=94)and the validation cohort(VC)(n=34).Therapeutic effect was assessed by disease-control rate and progressive disease according to the Response Evaluation Criteria in Solid Tumors(version 1.1)criteria.Possible prognostic factors associated with responses after DOS treatment including Siewert classification,gross tumor volume(GTV),and cT and cN stages were evaluated using pretherapeutic CT data in addition to sex and age.Univariate and multivariate analyses of CT and clinical features in the TC were performed to determine independent factors associated with response to DOS.A nomogram was established based on independent factors to predict the response.The predictive performance of the nomogram was evaluated by Concordance index(C-index),calibration and receiver operating characteristics curve in the TC and VC.RESULTS Univariate analysis showed that Siewert type(52/55 vs 29/39,P=0.005),pretherapeutic cT stage(57/62 vs 24/32,P=0.028),GTV(47.3±27.4 vs 73.2±54.3,P=0.040)were significantly associated with response to DOS in the TC.Multivariate analysis of the TC also showed that the pretherapeutic cT stage,GTV and Siewert type were independent predictive factors related to response to DOS(odds ratio=4.631,1.027 and 7.639,respectively;all P<0.05).The nomogram developed with these independent factors showed an excellent performance to predict response to DOS in the TC and VC(C-index:0.838 and 0.824),with area under the receiver operating characteristic curve of 0.838 and 0.824,respectively.The calibration curves showed that the practical and predicted response to DOS effectively coincided.CONCLUSION A novel nomogram developed with pretherapeutic cT stage,GTV and Siewert type predicted the response of Siewert type II/III AEG to NAC with DOS.

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 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.

Contribution of Computed Tomography to the Diagnosis of Urinary Lithiasis in a West African University Hospital

Introduction: Urinary lithiasis is defining as a condition characterized by the formation of concretions in the kidneys or urinary excretory tract. We aimed to study professional practice in CT urinary lithiasis at CHUYO. Materials and method: This was a descriptive cross-sectional study with retrospective data collection covering 3 years from January 2017 to December 2019. Results: In general, it was noted that CT scans in our environment provide all the elements needed by urologists to diagnose urinary lithiasis and its impact on the urinary system. However, there are shortcomings, particularly in terms of the accuracy of the shape and density of the stones in the CT scan reports, which does not make it easier for urologists to decide on treatment. Conclusion: Computed tomography plays a major role in the diagnosis and therapeutic management of urinary lithiasis, and its use needs to be improved in our context.

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.

Computed tomography imaging and clinical significance of bacterium-positive pulmonary tuberculosis complicated with diabetes

BACKGROUND The increasing prevalence of tuberculosis(TB)and diabetes on a global scale poses a significant health challenge,particularly due to their co-occurrence,which amplifies the severity,recurrence and mortality rates associated with both conditions.This highlights the need for further investigation into their interrelationship.AIM To explore the computed tomography(CT)imaging and clinical significance of bacterium-positive pulmonary TB(PTB)combined with diabetes.METHODS There were 50 patients with bacterium-positive PTB and diabetes,and 50 with only bacterium-positive PTB.The latter were designated as the control group.The CT imaging of the two groups of patients was compared,including lesion range,shape,density and calcification.RESULTS No significant differences were observed in age,gender,smoking and drinking history,high blood pressure,hyperlipidemia and family genetic factors between the groups.However,compared to the patients diagnosed solely with simple bacterium-positive PTB,those with concurrent diabetes showed a wider range of lesions and more complex and diverse morphology on CT images.Among them,intrapulmonary tuberculosis lesions were often accompanied by manifestations of pulmonary infection,such as cavity formation and bronchiectasis.At the same time,diabetes-related signs were often seen on CT images,such as pulmonary infection combined with diabetic pulmonary lesions.Logistic regression analysis identified age and medical history as significant factors influencing the degree of pulmonary infection and CT imaging outcomes in patients with both TB and diabetes.This suggests that older age and specific medical histories may increase the risk or severity of pulmonary damage in these patients.CONCLUSION CT imaging reveals more complex lesions in PTB patients with diabetes,emphasizing the need for careful evaluation and comprehensive analysis to enhance diagnostic accuracy.

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 radiogenomics:A potential tool for prediction of molecular subtypes in gastric stromal tumor

BACKGROUND Preoperative knowledge of mutational status of gastrointestinal stromal tumors(GISTs)is essential to guide the individualized precision therapy.AIM To develop a combined model that integrates clinical and contrast-enhanced computed tomography(CE-CT)features to predict gastric GISTs with specific genetic mutations,namely KIT exon 11 mutations or KIT exon 11 codons 557-558 deletions.METHODS A total of 231 GIST patients with definitive genetic phenotypes were divided into a training dataset and a validation dataset in a 7:3 ratio.The models were constructed using selected clinical features,conventional CT features,and radiomics features extracted from abdominal CE-CT images.Three models were developed:ModelCT sign,modelCT sign+rad,and model CTsign+rad+clinic.The diagnostic performance of these models was evaluated using receiver operating characteristic(ROC)curve analysis and the Delong test.RESULTS The ROC analyses revealed that in the training cohort,the area under the curve(AUC)values for model_(CT sign),model_(CT sign+rad),and modelCT_(sign+rad+clinic)for predicting KIT exon 11 mutation were 0.743,0.818,and 0.915,respectively.In the validation cohort,the AUC values for the same models were 0.670,0.781,and 0.811,respectively.For predicting KIT exon 11 codons 557-558 deletions,the AUC values in the training cohort were 0.667,0.842,and 0.720 for model_(CT sign),model_(CT sign+rad),and modelCT_(sign+rad+clinic),respectively.In the validation cohort,the AUC values for the same models were 0.610,0.782,and 0.795,respectively.Based on the decision curve analysis,it was determined that the model_(CT sign+rad+clinic)had clinical significance and utility.CONCLUSION Our findings demonstrate that the combined modelCT_(sign+rad+clinic)effectively distinguishes GISTs with KIT exon 11 mutation and KIT exon 11 codons 557-558 deletions.This combined model has the potential to be valuable in assessing the genotype of GISTs.

Constructing a nomogram to predict overall survival of colon cancer based on computed tomography characteristics and clinicopathological factors

BACKGROUND The colon cancer prognosis is influenced by multiple factors,including clinical,pathological,and non-biological factors.However,only a few studies have focused on computed tomography(CT)imaging features.Therefore,this study aims to predict the prognosis of patients with colon cancer by combining CT imaging features with clinical and pathological characteristics,and establishes a nomogram to provide critical guidance for the individualized treatment.AIM To establish and validate a nomogram to predict the overall survival(OS)of patients with colon cancer.METHODS A retrospective analysis was conducted on the survival data of 249 patients with colon cancer confirmed by surgical pathology between January 2017 and December 2021.The patients were randomly divided into training and testing groups at a 1:1 ratio.Univariate and multivariate logistic regression analyses were performed to identify the independent risk factors associated with OS,and a nomogram model was constructed for the training group.Survival curves were calculated using the Kaplan–Meier method.The concordance index(C-index)and calibration curve were used to evaluate the nomogram model in the training and testing groups.RESULTS Multivariate logistic regression analysis revealed that lymph node metastasis on CT,perineural invasion,and tumor classification were independent prognostic factors.A nomogram incorporating these variables was constructed,and the C-index of the training and testing groups was 0.804 and 0.692,respectively.The calibration curves demonstrated good consistency between the actual values and predicted probabilities of OS.CONCLUSION A nomogram combining CT imaging characteristics and clinicopathological factors exhibited good discrimination and reliability.It can aid clinicians in risk stratification and postoperative monitoring and provide important guidance for the individualized treatment of patients with colon cancer.

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.

Neural Network Robust Control Based on Computed Torque for Lower Limb Exoskeleton

The lower limb exoskeletons are used to assist wearers in various scenarios such as medical and industrial settings.Complex modeling errors of the exoskeleton in different application scenarios pose challenges to the robustness and stability of its control algorithm.The Radial Basis Function(RBF)neural network is used widely to compensate for modeling errors.In order to solve the problem that the current RBF neural network controllers cannot guarantee the asymptotic stability,a neural network robust control algorithm based on computed torque method is proposed in this paper,focusing on trajectory tracking.It innovatively incorporates the robust adaptive term while introducing the RBF neural network term,improving the compensation ability for modeling errors.The stability of the algorithm is proved by Lyapunov method,and the effectiveness of the robust adaptive term is verified by the simulation.Experiments wearing the exoskeleton under different walking speeds and scenarios were carried out,and the results show that the absolute value of tracking errors of the hip and knee joints of the exoskeleton are consistently less than 1.5°and 2.5°,respectively.The proposed control algorithm effectively compensates for modeling errors and exhibits high robustness.

Computed tomography-based radiomic model for the prediction of neoadjuvant immunochemotherapy response in patients with advanced gastric cancer

BACKGROUND Neoadjuvant immunochemotherapy(nICT)has emerged as a popular treatment approach for advanced gastric cancer(AGC)in clinical practice worldwide.However,the response of AGC patients to nICT displays significant heterogeneity,and no existing radiomic model utilizes baseline computed tomography to predict treatment outcomes.AIM To establish a radiomic model to predict the response of AGC patients to nICT.METHODS Patients with AGC who received nICT(n=60)were randomly assigned to a training cohort(n=42)or a test cohort(n=18).Various machine learning models were developed using selected radiomic features and clinical risk factors to predict the response of AGC patients to nICT.An individual radiomic nomogram was established based on the chosen radiomic signature and clinical signature.The performance of all the models was assessed through receiver operating characteristic curve analysis,decision curve analysis(DCA)and the Hosmer Lemeshow goodness-of-fit test.RESULTS The radiomic nomogram could accurately predict the response of AGC patients to nICT.In the test cohort,the area under curve was 0.893,with a 95%confidence interval of 0.803-0.991.DCA indicated that the clinical application of the radiomic nomogram yielded greater net benefit than alternative models.CONCLUSION A nomogram combining a radiomic signature and a clinical signature was designed to predict the efficacy of nICT in patients with AGC.This tool can assist clinicians in treatment-related 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.

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.

Evaluation of mitral chordae tendineae length using four-dimensional computed

BACKGROUND Mitral valvuloplasty using artificial chordae tendineae represents an effective surgical approach for treating mitral regurgitation.Achieving precise measurements of artificial chordae tendineae length(CL)is an important factor in the procedure;however,no objective index currently exists to facilitate this measurement.Therefore,preoperative assessment of CL is critical for surgical planning and support.Four-dimensional x-ray micro-computed tomography(4D-CT)may be useful for accurate CL measurement considering that it allows for dynamic three-dimensional(3D)evaluation compared to that with transthoracic echocardiography,a conventional inspection method.AIM To investigate the behavior and length of mitral chordae tendineae during systole using 4D-CT.METHODS Eleven adults aged>70 years without mitral valve disease were evaluated.A 64-slice CT scanner was used to capture 20 phases in the cardiac cycle in electrocardiographic synchronization.The length of the primary chordae tendineae was measured from early systole to early diastole using the 3D image.The primary chordae tendineae originating from the anterior papillary muscle and attached to the A1-2 region and those from the posterior papillary muscle and attached to the A2-3 region were designated as cA and cP,respectively.The behavior and maximum lengths[cA(ma),cP(max)]were compared,and the correlation with body surface area(BSA)was evaluated.RESULTS In all cases,the mitral anterior leaflet chordae tendineae could be measured.In most cases,the cA and cP chordae tendineae could be measured visually.The mean cA(max)and cP(max)were 20.2 mm±1.95 mm and 23.5 mm±4.06 mm,respectively.cP(max)was significantly longer.The correlation coefficients(r)with BSA were 0.60 and 0.78 for cA(max)and cP(max),respectively.Both cA and cP exhibited constant variation in CL during systole,with a maximum 1.16-fold increase in cA and a 1.23-fold increase in cP from early to mid-systole.For cP,CL reached a plateau at 15%and remained elongated until end-systole,whereas for cA,after peaking at 15%,CL shortened slightly and then moved toward its peak again as end-systole approached.CONCLUSION The study suggests that 4D-CT is a valuable tool for accurate measurement of both the length and behavior of chordae tendineae within the anterior leaflet of the mitral valve.

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.

Correlation between abdominal computed tomography signs and postoperative prognosis for patients with colorectal cancer

BACKGROUND Patients with different stages of colorectal cancer(CRC)exhibit different abdominal computed tomography(CT)signs.Therefore,the influence of CT signs on CRC prognosis must be determined.AIM To observe abdominal CT signs in patients with CRC and analyze the correlation between the CT signs and postoperative prognosis.METHODS The clinical history and CT imaging results of 88 patients with CRC who underwent radical surgery at Xingtan Hospital Affiliated to Shunde Hospital of Southern Medical University were retrospectively analyzed.Univariate and multivariate Cox regression analyses were used to explore the independent risk factors for postoperative death in patients with CRC.The three-year survival rate was analyzed using the Kaplan-Meier curve,and the correlation between postoperative survival time and abdominal CT signs in patients with CRC was analyzed using Spearman correlation analysis.RESULTS For patients with CRC,the three-year survival rate was 73.86%.The death group exhibited more severe characteristics than the survival group.A multivariate Cox regression model analysis showed that body mass index(BMI),degree of periintestinal infiltration,tumor size,and lymph node CT value were independent factors influencing postoperative death(P<0.05 for all).Patients with characteristics typical to the death group had a low three-year survival rate(log-rankχ2=66.487,11.346,12.500,and 27.672,respectively,P<0.05 for all).The survival time of CRC patients was negatively correlated with BMI,degree of periintestinal infiltration,tumor size,lymph node CT value,mean tumor long-axis diameter,and mean tumor short-axis diameter(r=-0.559,0.679,-0.430,-0.585,-0.425,and-0.385,respectively,P<0.05 for all).BMI was positively correlated with the degree of periintestinal invasion,lymph node CT value,and mean tumor short-axis diameter(r=0.303,0.431,and 0.437,respectively,P<0.05 for all).CONCLUSION The degree of periintestinal infiltration,tumor size,and lymph node CT value are crucial for evaluating the prognosis of patients with CRC.