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Objective: To investigate the value of radiomics analysis of dual-layer spectral-detector computed tomography (DLSCT)-derived iodine maps for predicting tumor deposits (TDs) preoperatively in patients with colorectal cancer (CRC).A total of 264 pathologically confirmed CRC patients (TDs + (n = 80); TDs − (n = 184)) who underwent preoperative DLSCT from two hospitals were retrospectively enrolled, and divided into training (n = 124), testing (n = 54), and external validation cohort (n = 86). Conventional CT features and iodine concentration (IC) were analyzed and measured. Radiomics features were derived from venous phase iodine maps from DLSCT. The least absolute shrinkage and selection operator (LASSO) was performed for feature selection. Finally, a support vector machine (SVM) algorithm was employed to develop clinical, radiomics, and combined models based on the most valuable clinical parameters and radiomics features. Area under receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis were used to evaluate the model’s efficacy.The combined model incorporating the valuable clinical parameters and radiomics features demonstrated excellent performance in predicting TDs in CRC (AUCs of 0.926, 0.881, and 0.887 in the training, testing, and external validation cohorts, respectively), which outperformed the clinical model in the training cohort and external validation cohorts (AUC: 0.839 and 0.695; p: 0.003 and 0.014) and the radiomics model in two cohorts (AUC: 0.922 and 0.792; p: 0.014 and 0.035).Radiomics analysis of DLSCT-derived iodine maps showed excellent predictive efficiency for preoperatively diagnosing TDs in CRC, and could guide clinicians in making individualized treatment strategies.The radiomics model based on DLSCT iodine maps has the potential to aid in the accurate preoperative prediction of TDs in CRC patients, offering valuable guidance for clinical decision-making.Accurately predicting TDs in CRC patients preoperatively based on conventional CT features poses a challenge.The Radiomics model based on DLSCT iodine maps outperformed conventional CT in predicting TDs.The model combing DLSCT iodine maps radiomics features and conventional CT features performed excellently in predicting TDs.Accurately predicting TDs in CRC patients preoperatively based on conventional CT features poses a challenge.The Radiomics model based on DLSCT iodine maps outperformed conventional CT in predicting TDs.The model combing DLSCT iodine maps radiomics features and conventional CT features performed excellently in predicting TDs.Materials and methods: To investigate the value of radiomics analysis of dual-layer spectral-detector computed tomography (DLSCT)-derived iodine maps for predicting tumor deposits (TDs) preoperatively in patients with colorectal cancer (CRC).A total of 264 pathologically confirmed CRC patients (TDs + (n = 80); TDs − (n = 184)) who underwent preoperative DLSCT from two hospitals were retrospectively enrolled, and divided into training (n = 124), testing (n = 54), and external validation cohort (n = 86). Conventional CT features and iodine concentration (IC) were analyzed and measured. Radiomics features were derived from venous phase iodine maps from DLSCT. The least absolute shrinkage and selection operator (LASSO) was performed for feature selection. Finally, a support vector machine (SVM) algorithm was employed to develop clinical, radiomics, and combined models based on the most valuable clinical parameters and radiomics features. Area under receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis were used to evaluate the model’s efficacy.The combined model incorporating the valuable clinical parameters and radiomics features demonstrated excellent performance in predicting TDs in CRC (AUCs of 0.926, 0.881, and 0.887 in the training, testing, and external validation cohorts, respectively), which outperformed the clinical model in the training cohort and external validation cohorts (AUC: 0.839 and 0.695; p: 0.003 and 0.014) and the radiomics model in two cohorts (AUC: 0.922 and 0.792; p: 0.014 and 0.035).Radiomics analysis of DLSCT-derived iodine maps showed excellent predictive efficiency for preoperatively diagnosing TDs in CRC, and could guide clinicians in making individualized treatment strategies.The radiomics model based on DLSCT iodine maps has the potential to aid in the accurate preoperative prediction of TDs in CRC patients, offering valuable guidance for clinical decision-making.Accurately predicting TDs in CRC patients preoperatively based on conventional CT features poses a challenge.The Radiomics model based on DLSCT iodine maps outperformed conventional CT in predicting TDs.The model combing DLSCT iodine maps radiomics features and conventional CT features performed excellently in predicting TDs.Accurately predicting TDs in CRC patients preoperatively based on conventional CT features poses a challenge.The Radiomics model based on DLSCT iodine maps outperformed conventional CT in predicting TDs.The model combing DLSCT iodine maps radiomics features and conventional CT features performed excellently in predicting TDs.Results: To investigate the value of radiomics analysis of dual-layer spectral-detector computed tomography (DLSCT)-derived iodine maps for predicting tumor deposits (TDs) preoperatively in patients with colorectal cancer (CRC).A total of 264 pathologically confirmed CRC patients (TDs + (n = 80); TDs − (n = 184)) who underwent preoperative DLSCT from two hospitals were retrospectively enrolled, and divided into training (n = 124), testing (n = 54), and external validation cohort (n = 86). Conventional CT features and iodine concentration (IC) were analyzed and measured. Radiomics features were derived from venous phase iodine maps from DLSCT. The least absolute shrinkage and selection operator (LASSO) was performed for feature selection. Finally, a support vector machine (SVM) algorithm was employed to develop clinical, radiomics, and combined models based on the most valuable clinical parameters and radiomics features. Area under receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis were used to evaluate the model’s efficacy.The combined model incorporating the valuable clinical parameters and radiomics features demonstrated excellent performance in predicting TDs in CRC (AUCs of 0.926, 0.881, and 0.887 in the training, testing, and external validation cohorts, respectively), which outperformed the clinical model in the training cohort and external validation cohorts (AUC: 0.839 and 0.695; p: 0.003 and 0.014) and the radiomics model in two cohorts (AUC: 0.922 and 0.792; p: 0.014 and 0.035).Radiomics analysis of DLSCT-derived iodine maps showed excellent predictive efficiency for preoperatively diagnosing TDs in CRC, and could guide clinicians in making individualized treatment strategies.The radiomics model based on DLSCT iodine maps has the potential to aid in the accurate preoperative prediction of TDs in CRC patients, offering valuable guidance for clinical decision-making.Accurately predicting TDs in CRC patients preoperatively based on conventional CT features poses a challenge.The Radiomics model based on DLSCT iodine maps outperformed conventional CT in predicting TDs.The model combing DLSCT iodine maps radiomics features and conventional CT features performed excellently in predicting TDs.Accurately predicting TDs in CRC patients preoperatively based on conventional CT features poses a challenge.The Radiomics model based on DLSCT iodine maps outperformed conventional CT in predicting TDs.The model combing DLSCT iodine maps radiomics features and conventional CT features performed excellently in predicting TDs.Conclusion: To investigate the value of radiomics analysis of dual-layer spectral-detector computed tomography (DLSCT)-derived iodine maps for predicting tumor deposits (TDs) preoperatively in patients with colorectal cancer (CRC).A total of 264 pathologically confirmed CRC patients (TDs + (n = 80); TDs − (n = 184)) who underwent preoperative DLSCT from two hospitals were retrospectively enrolled, and divided into training (n = 124), testing (n = 54), and external validation cohort (n = 86). Conventional CT features and iodine concentration (IC) were analyzed and measured. Radiomics features were derived from venous phase iodine maps from DLSCT. The least absolute shrinkage and selection operator (LASSO) was performed for feature selection. Finally, a support vector machine (SVM) algorithm was employed to develop clinical, radiomics, and combined models based on the most valuable clinical parameters and radiomics features. Area under receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis were used to evaluate the model’s efficacy.The combined model incorporating the valuable clinical parameters and radiomics features demonstrated excellent performance in predicting TDs in CRC (AUCs of 0.926, 0.881, and 0.887 in the training, testing, and external validation cohorts, respectively), which outperformed the clinical model in the training cohort and external validation cohorts (AUC: 0.839 and 0.695; p: 0.003 and 0.014) and the radiomics model in two cohorts (AUC: 0.922 and 0.792; p: 0.014 and 0.035).Radiomics analysis of DLSCT-derived iodine maps showed excellent predictive efficiency for preoperatively diagnosing TDs in CRC, and could guide clinicians in making individualized treatment strategies.The radiomics model based on DLSCT iodine maps has the potential to aid in the accurate preoperative prediction of TDs in CRC patients, offering valuable guidance for clinical decision-making.Accurately predicting TDs in CRC patients preoperatively based on conventional CT features poses a challenge.The Radiomics model based on DLSCT iodine maps outperformed conventional CT in predicting TDs.The model combing DLSCT iodine maps radiomics features and conventional CT features performed excellently in predicting TDs.Accurately predicting TDs in CRC patients preoperatively based on conventional CT features poses a challenge.The Radiomics model based on DLSCT iodine maps outperformed conventional CT in predicting TDs.The model combing DLSCT iodine maps radiomics features and conventional CT features performed excellently in predicting TDs.Clinical relevance statement: To investigate the value of radiomics analysis of dual-layer spectral-detector computed tomography (DLSCT)-derived iodine maps for predicting tumor deposits (TDs) preoperatively in patients with colorectal cancer (CRC).A total of 264 pathologically confirmed CRC patients (TDs + (n = 80); TDs − (n = 184)) who underwent preoperative DLSCT from two hospitals were retrospectively enrolled, and divided into training (n = 124), testing (n = 54), and external validation cohort (n = 86). Conventional CT features and iodine concentration (IC) were analyzed and measured. Radiomics features were derived from venous phase iodine maps from DLSCT. The least absolute shrinkage and selection operator (LASSO) was performed for feature selection. Finally, a support vector machine (SVM) algorithm was employed to develop clinical, radiomics, and combined models based on the most valuable clinical parameters and radiomics features. Area under receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis were used to evaluate the model’s efficacy.The combined model incorporating the valuable clinical parameters and radiomics features demonstrated excellent performance in predicting TDs in CRC (AUCs of 0.926, 0.881, and 0.887 in the training, testing, and external validation cohorts, respectively), which outperformed the clinical model in the training cohort and external validation cohorts (AUC: 0.839 and 0.695; p: 0.003 and 0.014) and the radiomics model in two cohorts (AUC: 0.922 and 0.792; p: 0.014 and 0.035).Radiomics analysis of DLSCT-derived iodine maps showed excellent predictive efficiency for preoperatively diagnosing TDs in CRC, and could guide clinicians in making individualized treatment strategies.The radiomics model based on DLSCT iodine maps has the potential to aid in the accurate preoperative prediction of TDs in CRC patients, offering valuable guidance for clinical decision-making.Accurately predicting TDs in CRC patients preoperatively based on conventional CT features poses a challenge.The Radiomics model based on DLSCT iodine maps outperformed conventional CT in predicting TDs.The model combing DLSCT iodine maps radiomics features and conventional CT features performed excellently in predicting TDs.Accurately predicting TDs in CRC patients preoperatively based on conventional CT features poses a challenge.The Radiomics model based on DLSCT iodine maps outperformed conventional CT in predicting TDs.The model combing DLSCT iodine maps radiomics features and conventional CT features performed excellently in predicting TDs.Key Points: To investigate the value of radiomics analysis of dual-layer spectral-detector computed tomography (DLSCT)-derived iodine maps for predicting tumor deposits (TDs) preoperatively in patients with colorectal cancer (CRC).A total of 264 pathologically confirmed CRC patients (TDs + (n = 80); TDs − (n = 184)) who underwent preoperative DLSCT from two hospitals were retrospectively enrolled, and divided into training (n = 124), testing (n = 54), and external validation cohort (n = 86). Conventional CT features and iodine concentration (IC) were analyzed and measured. Radiomics features were derived from venous phase iodine maps from DLSCT. The least absolute shrinkage and selection operator (LASSO) was performed for feature selection. Finally, a support vector machine (SVM) algorithm was employed to develop clinical, radiomics, and combined models based on the most valuable clinical parameters and radiomics features. Area under receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis were used to evaluate the model’s efficacy.The combined model incorporating the valuable clinical parameters and radiomics features demonstrated excellent performance in predicting TDs in CRC (AUCs of 0.926, 0.881, and 0.887 in the training, testing, and external validation cohorts, respectively), which outperformed the clinical model in the training cohort and external validation cohorts (AUC: 0.839 and 0.695; p: 0.003 and 0.014) and the radiomics model in two cohorts (AUC: 0.922 and 0.792; p: 0.014 and 0.035).Radiomics analysis of DLSCT-derived iodine maps showed excellent predictive efficiency for preoperatively diagnosing TDs in CRC, and could guide clinicians in making individualized treatment strategies.The radiomics model based on DLSCT iodine maps has the potential to aid in the accurate preoperative prediction of TDs in CRC patients, offering valuable guidance for clinical decision-making.Accurately predicting TDs in CRC patients preoperatively based on conventional CT features poses a challenge.The Radiomics model based on DLSCT iodine maps outperformed conventional CT in predicting TDs.The model combing DLSCT iodine maps radiomics features and conventional CT features performed excellently in predicting TDs.Accurately predicting TDs in CRC patients preoperatively based on conventional CT features poses a challenge.The Radiomics model based on DLSCT iodine maps outperformed conventional CT in predicting TDs.The model combing DLSCT iodine maps radiomics features and conventional CT features performed excellently in predicting TDs. [ABSTRACT FROM AUTHOR]

Abstract: Objective: To investigate the improvement in postprocessing time and image quality of 3D negative-contrast CT cholangiography (nCTC) with minimum intensity projection (minIP). Methods: Thirty-eight patients with suspected biliary obstruction who underwent portal phase helical CT were retrospectively studied. Three-dimensional nCTC was generated using 3D tools with manual cut function in three methods: one-slice, two-slice, and vari-slice cut slab, respectively. After adjusting the threshold level to about −20 HU, the erosion function with a value of one voxel was employed each time for further cleaning up hypodense interfering voxels such as fat and air that remained and the procedure was stopped before the biliary and pancreatic ducts were overeroded. Results: Mean values of the total postprocessing time of the three manual cut methods for 3D nCTC were 57.8, 29.9, and 20.6 min, respectively. After the first erosion, interfering voxels were removed in more than half of the cases and they could be primarily eliminated following the second erosion. Meanwhile, the pancreaticobiliary ducts showed obvious erosion findings at the third erosion. Conclusions: The vari-slice manual cut method was shown to be the most timesaving postprocessing method and the image quality for 3D nCTC can be improved when one to two frequencies of erosion are applied. [Copyright &y& Elsevier]

Objective: To access the effect of iodinate contrast agent (ICA) on DNA double-stand breaks (DSBs) in human peripheral blood lymphocytes during computed tomography (CT) examinations.Materials and Methods: This present study was approved by the institutional ethics committee; written informed patient consent was obtained from 70 patients. A total of 48 patients underwent computed tomography urography (CTU), in which only one time CT scanning was examined after injecting ICA, and 22 patients received unenhanced whole abdominal CT, among them 10 patients were selected to get ICA injection immediately after irradiation. Blood samples were taken from all patients prior to and immediately after CT scan, as well as 8min after the injection of ICA. The lymphocytes in these blood samples were separated by using density-gradient centrifugation, fixed and immunostained with γH2AX antibody. The average number of phosphorylated histone H2AX (γH2AX) foci per lymphocyte was counted under a fluorescence microscopy. Differences in the number of γH2AX-foci were statistically analyzed using independent sample t test and one way ANOVA.Result: The three patient groups had no significant differences in the baseline foci numbers(P>0.05). The γH2AX-focus levels increased in both groups after CT scan. Patients who underwent CTU examinations had a greater DSBs level (mean±standard error of mean, 0.945±0.184 foci per cell) than those who received unenhanced whole abdominal CT scan (mean±standard error of mean, 0.700±0.112 foci per cell), increasing by about 37.9%; The ICA injected before CT scan itself had an effect on the DSBs, which increased DSBs level by approximately 90.3% (0.059±0.018vs 0.031±0.025, P<0.05), but no significant difference was found if added after irradiation, increasing DSBs level only by 3.2% approximately (0.711±0.091vs 0.689±0.108, P=0.499).Conclusion: The iodinated contrast agent itself can lead to an increase in the level of DSBs as assessed with γH2AX foci formation, and the application of ICA can amplify DNA damage induced by diagnostic x-ray procedures such as whole abdominal CT. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the relationship between myocardial extracellular volume (ECV) fraction measured using dual-energy computed tomography with late iodine enhancement (LIE-DECT) and risk of heart failure (HF) in patients without coronary artery disease (CAD), and to evaluate the relationship between ECV and left ventricular structure and function.Materials and Methods: Sixty consecutive HF patients without CAD and 60 consecutive participants without heart disease who underwent coronary CT angiography (CCTA) following LIE-DECT were included. ECV of the left ventricle was calculated from the iodine maps and hematocrit levels using the American Heart Association (AHA) 16-segment model. Cardiac structural and functional parameters were collected including left ventricular end-systolic volume (LVESV), left ventricular end-diastolic volume (LVEDV), left ventricular ejection fraction (LVEF), left atrial volume (LAV), interventricular septal thickness (IVST), and N-terminal pro-brain natriuretic peptide (NT-pro-BNP).Results: ECV in HF patients without CAD (31.3 ± 4.0 %) was significantly higher than that in healthy subjects (27.1 ± 3.7 %) (P < 0.001). Multivariate linear analysis revealed that ECV was associated with age (β = 0.098, P = 0.010) and hypertension (β = 2.093, P = 0.011) in all participants. Binary logistic regression analysis showed that after adjusting for age, sex, body mass index (BMI), smoking, and drinking, ECV was a risk factor affecting the occurrence of HF in those without CAD (OR = 1.356, 95 %CI：1.178-1.561, P < 0.001). A positive correlation was found between ECV and NT-pro-BNP, LVEDV, LVESV, and LAV (r = 0.629, 0.329, 0.346, and 0.338, respectively; all P < 0.001) in all participants.Conclusions: ECV could be measured using LIE-DECT iodine maps. ECV elevation was a risk factor for HF without CAD and correlated with cardiac structure and function. [ABSTRACT FROM AUTHOR]