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.

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.

Computed tomography for the prediction of oesophageal variceal bleeding:A surrogate or complementary to the gold standard?

In this editorial we comment on the in-press article in the World Journal of Gastrointestinal endoscopy about the role of computed tomography(CT)for the prediction of esophageal variceal bleeding.The mortality and morbidity are much increased in patients with chronic liver diseases when complicated with variceal bleeding.Predicting the patient at a risk of bleeding is extremely important and receives a great deal of attention,paving the way for primary prophylaxis either using medical treatment including carvedilol or propranolol,or endoscopic band ligation.Endoscopic examination and the hepatic venous pressure gradient are the gold standards in the diagnosis and prediction of variceal bleeding.Several non-invasive laboratory and radiological examinations are used for the prediction of variceal bleeding.The contrast-enhanced multislice CT is a widely used non-invasive,radiological examination that has many advantages.In this editorial we briefly comment on the current research regarding the use of CT as a non-invasive tool in predicting the variceal bleeding.

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.

Future directions of noninvasive prediction of esophageal variceal bleeding:No worry about the present computed tomography inefficiency

In this editorial,we comment on the minireview by Martino A,published in the recent issue of World Journal of Gastrointestinal Endoscopy 2023;15(12):681-689.We focused mainly on the possibility of replacing the hepatic venous pressure gradient(HVPG)and endoscopy with noninvasive methods for predicting esophageal variceal bleeding.The risk factors for bleeding were the size of the varices,the red sign and the Child-Pugh score.The intrinsic core factor that drove these changes was the HVPG.Therefore,the present studies investigating noninvasive methods,including computed tomography,magnetic resonance imaging,elastography,and laboratory tests,are working on correlating imaging or serum marker data with intravenous pressure and clinical outcomes,such as bleeding.A single parameter is usually not enough to construct an efficient model.Therefore,multiple factors were used in most of the studies to construct predictive models.Encouraging results have been obtained,in which bleeding prediction was partly reached.However,these methods are not satisfactory enough to replace invasive methods,due to the many drawbacks of different studies.There is still plenty of room for future improvement.Prediction of the precise timing of bleeding using various models,and extracting the texture of variceal walls using high-definition imaging modalities to predict the red sign are interesting directions to lay investment on.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Computed tomography-based radiomics diagnostic approach for differential diagnosis between early-and late-stage pancreatic ductal adenocarcinoma

BACKGROUND One of the primary reasons for the dismal survival rates in pancreatic ductal adenocarcinoma(PDAC)is that most patients are usually diagnosed at late stages.There is an urgent unmet clinical need to identify and develop diagnostic methods that could precisely detect PDAC at its earliest stages.METHODS A total of 71 patients with pathologically proved PDAC based on surgical resection who underwent contrast-enhanced computed tomography(CT)within 30 d prior to surgery were included in the study.Tumor staging was performed in accordance with the 8th edition of the American Joint Committee on Cancer staging system.Radiomics features were extracted from the region of interest(ROI)for each patient using Analysis Kit software.The most important and predictive radiomics features were selected using Mann-Whitney U test,univar-iate logistic regression analysis,and minimum redundancy maximum relevance(MRMR)method.Random forest(RF)method was used to construct the radiomics model,and 10-times leave group out cross-validation(LGOCV)method was used to validate the robustness and reproducibility of the model.RESULTS A total of 792 radiomics features(396 from late arterial phase and 396 from portal venous phase)were extracted from the ROI for each patient using Analysis Kit software.Nine most important and predictive features were selected using Mann-Whitney U test,univariate logistic regression analysis,and MRMR method.RF method was used to construct the radiomics model with the nine most predictive radiomics features,which showed a high discriminative ability with 97.7%accuracy,97.6%sensitivity,97.8%specificity,98.4%positive predictive value,and 96.8%negative predictive value.The radiomics model was proved to be robust and reproducible using 10-times LGOCV method with an average area under the curve of 0.75 by the average performance of the 10 newly built models.CONCLUSION The radiomics model based on CT could serve as a promising non-invasive method in differential diagnosis between early and late stage PDAC.

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.

Computed tomography three-dimensional reconstruction in the diagnosis of bleeding small intestinal polyps:A case report

BACKGROUND Computed tomography(CT)small bowel three-dimensional(3D)reconstruction is a powerful tool for the diagnosis of small bowel disease and can clearly show the intestinal lumen and wall as well as the outside structure of the wall.The horizontal axis position can show the best adjacent intestinal tube and the lesion between the intestinal tubes,while the coronal position can show the overall view of the small bowel.The ileal end of the localization of the display of excellent,and easy to quantitative measurement of the affected intestinal segments,the sagittal position for the rectum and the pre-sacral lesions show the best,for the discovery of fistulae is also helpful.Sagittal view can show rectal and presacral lesions and is useful for fistula detection.It is suitable for the assessment of inflammatory bowel disease,such as assessment of disease severity and diagnosis and differential diagnosis of the small bowel and mesenteric space-occupying lesions as well as the judgment of small bowel obstruction points.CASE SUMMARY Bleeding caused by small intestinal polyps is often difficult to diagnose in clinical practice.This study reports a 29-year-old male patient who was admitted to the hospital with black stool and abdominal pain for 3 months.Using the combination of CT-3D reconstruction and capsule endoscopy,the condition was diagnosed correctly,and the polyps were removed using single-balloon enteroscopyendoscopic retrograde cholangiopancreatography without postoperative complications.CONCLUSION The role of CT-3D in gastrointestinal diseases was confirmed.CT-3D can assist in the diagnosis and treatment of gastrointestinal diseases in combination with capsule endoscopy and small intestinal microscopy.