Low-dose computed tomography with 4th-generation iterative reconstruction algorithm in assessment of oncologic patients

AIM To compare radiation dose and image quality of lowdose computed tomography(CT) protocol combined with hybrid-iterative reconstruction algorithm with standarddose CT examinations for follow-up of oncologic patients. METHODS Fifty-one patients with known malignant diseases which underwent, during clinical follow-up, both standarddose and low-dose whole-body CT scans were enrolled. Low-dose CT was performed on 256-row scanner, with 120 kV and automated m A modulation, and iterative reconstruction algorithm. Standard-dose CT was performed on 16-rows scanner, with 120 kV, 200-400 m As(depending on patient weight). We evaluated density values and signal-to-noise ratio, along with image noise(SD), sharpness and diagnostic quality with 4-point scale.RESULTS Density values in liver, spleen and aorta were higher in lowdose images(liver 112.55 HU vs 103.90 HU, P < 0.001), as SD values in liver and spleen(liver 16.81 vs 14.41). Volumetric-Computed-Tomographic-Dose-Index(CTDIvol) and Dose-Length-Product(DLP) were significantly lower in low-dose CT as compared to standard-dose(DLP 1025.6 m Gy*cm vs 1429.2 m Gy*cm, P < 0.001) with overall dose reduction of 28.9%. Qualitative analysis did not reveal significant differences in image noise and diagnostic quality.CONCLUSION Automatic tube-current modulation combined with hybriditerative algorithm allows radiation dose reduction of 28.9% without loss of diagnostic quality, being useful in reducing dose exposure in oncologic patients.

Quantitative evaluation of deep convolutional neural network-based image denoising for low-dose computed tomography

To minimize radiation risk,dose reduction is important in the diagnostic and therapeutic applications of computed tomography(CT).However,image noise degrades image quality owing to the reduced X-ray dose and a possible unacceptably reduced diagnostic performance.Deep learning approaches with convolutional neural networks(CNNs)have been proposed for natural image denoising;however,these approaches might introduce image blurring or loss of original gradients.The aim of this study was to compare the dose-dependent properties of a CNN-based denoising method for low-dose CT with those of other noise-reduction methods on unique CT noise-simulation images.To simulate a low-dose CT image,a Poisson noise distribution was introduced to normal-dose images while convoluting the CT unit-specific modulation transfer function.An abdominal CT of 100 images obtained from a public database was adopted,and simulated dose-reduction images were created from the original dose at equal 10-step dose-reduction intervals with a final dose of 1/100.These images were denoised using the denoising network structure of CNN(DnCNN)as the general CNN model and for transfer learning.To evaluate the image quality,image similarities determined by the structural similarity index(SSIM)and peak signal-to-noise ratio(PSNR)were calculated for the denoised images.Significantly better denoising,in terms of SSIM and PSNR,was achieved by the DnCNN than by other image denoising methods,especially at the ultra-low-dose levels used to generate the 10%and 5%dose-equivalent images.Moreover,the developed CNN model can eliminate noise and maintain image sharpness at these dose levels and improve SSIM by approximately 10%from that of the original method.In contrast,under small dose-reduction conditions,this model also led to excessive smoothing of the images.In quantitative evaluations,the CNN denoising method improved the low-dose CT and prevented over-smoothing by tailoring the CNN model.

Coronary artery calcium score on low-dose computed tomography for lung cancer screening

AIM: To evaluate the feasibility of coronary artery calcium score(CACS) on low-dose non-gated chest CT(ngCCT).METHODS: Sixty consecutive individuals(30 males; 73 ± 7 years) scheduled for risk stratification by means of unenhanced ECG-triggered cardiac computed to-mography(gCCT) underwent additional unenhanced ngCCT. All CT scans were performed on a 64-slice CT scanner(Somatom Sensation 64 Cardiac, Siemens, Germany). CACS was calculated using conventional methods/scores(Volume, Mass, Agatston) as previ-ously described in literature. The CACS value obtained were compared. The Mayo Clinic classification was used to stratify cardiovascular risk based on Agatston CACS. Differences and correlations between the two methods were compared. A P-value < 0.05 was considered sig-nificant.RESULTS: Mean CACS values were significantly higher for gCCT as compared to ngCCT(Volume: 418 ± 747 vs 332 ± 597; Mass: 89 ± 151 vs 78 ± 141; Agatston: 481 ± 854 vs 428 ± 776; P < 0.05). The correlation between the two values was always very high(Volume: r = 0.95; Mass: r = 0.97; Agatston: r = 0.98). Of the 6 patients with 0 Agatston score on gCCT, 2(33%) showed an Agatston score > 0 in the ngCCT. Of the 3 patients with 1-10 Agatston score on gCCT, 1(33%) showed an Agatston score of 0 in the ngCCT. Overall, 23(38%) patients were reclassified in a different car-diovascular risk category, mostly(18/23; 78%) shifting to a lower risk in the ngCCT. The estimated radiation dose was significantly higher for gCCT(DLP 115.8 ± 50.7 vs 83.8 ± 16.3; Effective dose 1.6 ± 0.7 mSv vs 1.2 ± 0.2 mSv; P < 0.01).CONCLUSION: CACS assessment is feasible on ngCCT; the variability of CACS values and the associated re-stratification of patients in cardiovascular risk groups should be taken into account.

Quantitative Assessment of Air Trapping Using Inspiratory and Expiratory Low-Dose Computed Tomography

Objective: The purpose of this study was to evaluate the effect of radiation dose reduction on the quantification of air trapping on expiratory CT. Materials and methods: This study was conducted as a retrospective evaluation of inspiratory and expiratory CT studies performed in routine clinical practice before and after alteration of the scanning protocol for expiratory CT at our institute. Eighty-six patients who had a clinical diagnosis of chronic obstructive pulmonary disease (COPD) and underwent inspiratory and expiratory CT and pulmonary function testing (PFT) were included. For the quantitative analysis, CT scans were obtained at six evenly spaced levels from the lung apices to the bases. The area of segmented lung without emphysema between -500 to -950 HU was obtained from the summation of six slices. The relative area between -900 and -950 HU for the area of the segmented lung (RA900-950) was calculated on both the inspiratory and expiratory scans. Comparisons of the RA-change between the standard-dose group (200 mA) and the low-dose group (80 mA) were performed by Mann-Whitney U test. Results: There was no significant difference between the standard-dose group and the low-dose group in the mean RA-change, and RA-change in both the standard-dose and low-dose groups correlated significantly with the results of PFT. In addition, there were no prominent differences in the correlation coefficients between the two groups. Conclusions: Low-dose CT could evaluate air trapping objectively and was not inferior to standard-dose CT for this purpose.

Accuracy of baseline low-dose computed tomography lung cancer screening: a systematic review and meta-analysis

Background:Screening using low-dose computed tomography(LDCT)is a more effective approach and has the potential to detect lung cancer more accurately.We aimed to conduct a meta-analysis to estimate the accuracy of population-based screening studies primarily assessing baseline LDCT screening for lung cancer.Methods:MEDLINE,Excerpta Medica Database,and Web of Science were searched for articles published up to April 10,2022.According to the inclusion and exclusion criteria,the data of true positives,false-positives,false negatives,and true negatives in the screening test were extracted.Quality Assessment of Diagnostic Accuracy Studies-2 was used to evaluate the quality of the literature.A bivariate random effects model was used to estimate pooled sensitivity and specificity.The area under the curve(AUC)was calculated by using hierarchical summary receiver-operating characteristics analysis.Heterogeneity between studies was measured using the Higgins I 2 statistic,and publication bias was evaluated using a Deeks’funnel plot and linear regression test.Results:A total of 49 studies with 157,762 individuals were identified for the final qualitative synthesis;most of them were from Europe and America(38 studies),ten were from Asia,and one was from Oceania.The recruitment period was 1992 to 2018,and most of the subjects were 40 to 75 years old.The analysis showed that the AUC of lung cancer screening by LDCT was 0.98(95%CI:0.96-0.99),and the overall sensitivity and specificity were 0.97(95%CI:0.94-0.98)and 0.87(95%CI:0.82-0.91),respectively.The funnel plot and test results showed that there was no significant publication bias among the included studies.Conclusions:Baseline LDCT has high sensitivity and specificity as a screening technique for lung cancer.However,long-term follow-up of the whole study population(including those with a negative baseline screening result)should be performed to enhance the accuracy of LDCT screening.

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.

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.

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.

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.