Radiomics Analysis to Predict Lymphovascular Invasion of Gastric Cancer Based on Iodine-Based Material Decomposition Images and Virtual Monoenergetic Images.

OBJECTIVE: This study aimed to investigate the utility of virtual monoenergetic images (VMIs) and iodine-based material decomposition images (IMDIs) in the assessment of lymphovascular invasion (LVI) in gastric cancer (GC) patients. METHODS: A total of 103 GC patients who underwent dual-energy spectral computed tomography preoperatively were enrolled. The LVI status was confirmed by pathological analysis. The radiomics features obtained from the 70 keV VMI and IMDI were used to build radiomics models. Independent clinical factors for LVI were identified and used to build the clinical model. Then, combined models were constructed by fusing clinical factors and radiomics signatures. The predictive performance of these models was evaluated. RESULTS: The computed tomography-reported N stage was an independent predictor of LVI, and the areas under the curve (AUCs) of the clinical model in the training group and testing group were 0.750 and 0.765, respectively. The radiomics models using the VMI signature and IMDI signature and combining these 2 signatures outperformed the clinical model, with AUCs of 0.835, 0.855, and 0.924 in the training set and 0.838, 0.825, and 0.899 in the testing set, respectively. The model combined with the computed tomography-reported N stage and the 2 radiomics signatures achieved the best performance in the training (AUC, 0.925) and testing (AUC, 0.961) sets, with a good degree of calibration and clinical utility for LVI prediction. CONCLUSIONS: The preoperative assessment of LVI in GC is improved by radiomics features based on VMI and IMDI. The combination of clinical, VMI-, and IMDI-based radiomics features effectively predicts LVI and provides support for clinical treatment decisions.

Radiomics analysis of lesion-specific pericoronary adipose tissue to predict major adverse cardiovascular events in coronary artery disease.

OBJECTIVE: To investigate the prognostic performance of radiomics analysis of lesion-specific pericoronary adipose tissue (PCAT) for major adverse cardiovascular events (MACE) with the guidance of CT derived fractional flow reserve (CT-FFR) in coronary artery disease (CAD). MATERIALS AND METHODS: The study retrospectively analyzed 608 CAD patients who underwent coronary CT angiography. Lesion-specific PCAT was determined by the lowest CT-FFR value and 1691 radiomic features were extracted. MACE included cardiovascular death, nonfatal myocardial infarction, unplanned revascularization and hospitalization for unstable angina. Four models were generated, incorporating traditional risk factors (clinical model), radiomics score (Rad-score, radiomics model), traditional risk factors and Rad-score (clinical radiomics model) and all together (combined model). The model performances were evaluated and compared with Harrell concordance index (C-index), area under curve (AUC) of the receiver operator characteristic. RESULTS: Lesion-specific Rad-score was associated with MACE (adjusted HR = 1.330, p = 0.009). The combined model yielded the highest C-index of 0.718, which was higher than clinical model (C-index = 0.639), radiomics model (C-index = 0.653) and clinical radiomics model (C-index = 0.698) (all p < 0.05). The clinical radiomics model had significant higher C-index than clinical model (p = 0.030). There were no significant differences in C-index between clinical or clinical radiomics model and radiomics model (p values were 0.796 and 0.147 respectively). The AUC increased from 0.674 for clinical model to 0.721 for radiomics model, 0.759 for clinical radiomics model and 0.773 for combined model. CONCLUSION: Radiomics analysis of lesion-specific PCAT is useful in predicting MACE. Combination of lesion-specific Rad-score and CT-FFR shows incremental value over traditional risk factors.

Gd-EOB-DTPA enhanced MRI nomogram model to differentiate hepatocellular carcinoma and focal nodular hyperplasia both showing iso- or hyperintensity in the hepatobiliary phase.

BACKGROUND: To develop and validate a nomogram model based on Gd-EOB-DTPA enhanced MRI for differentiation between hepatocellular carcinoma (HCC) and focal nodular hyperplasia (FNH) showing iso- or hyperintensity in the hepatobiliary phase (HBP). METHODS: A total of 75 patients with 49 HCCs and 26 FNHs randomly divided into a training cohort (n = 52: 34 HCC; 18 FNH) and an internal validation cohort (n = 23: 15 HCC; 8 FNH). A total of 37 patients (n = 37: 25 HCC; 12 FNH) acted as an external test cohort. The clinical and imaging characteristics between HCC and FNH groups in the training cohort were compared. The statistically significant parameters were included into the FAE software, and a multivariate logistic regression classifier was used to identify independent predictors and establish a nomogram model. Receiver operating characteristic (ROC) curves were used to evaluate the prediction ability of the model, while the calibration and decision curves were used for model validation. Subanalysis was used to compare qualitative and quantitative characteristics of patients with chronic hepatitis and cirrhosis between the HCC and FNH groups. RESULTS: In the training cohort, gender, age, enhancement rate in the arterial phase (AP), focal defects in uptake were significant predictors for HCC showing iso- or hyperintensity in the HBP. In the training cohort, area under the curve (AUC), sensitivity and specificity of the nomogram model were 0.989(95%CI: 0.967-1.000), 97.1% and 94.4%. In the internal validation cohort, the above three indicators were 0.917(95%CI: 0.782-1.000), 93.3% and 87.5%. In the external test cohort, the above three indicators were 0.960(95%CI: 0.905-1.000), 84.0% and 100.0%. The results of subanalysis showed that age was the independent predictor in the patients with chronic hepatitis and cirrhosis between HCC and FNH groups. CONCLUSIONS: Gd-EOB-DTPA enhanced MRI nomogram model may be useful for discriminating HCC and FNH showing iso- or hyperintensity in the HBP before surgery.

Quorum sensing effect of chiral d-glutamine on the modulation of the intestinal microbiota of mice by Lactiplantibacillus plantarum A3.

BACKGROUND: Amino acids (AAs) are the building blocks of proteins, but they also serve as biological compounds in biochemical processes, and d-AA isomers are increasingly being recognized as important signaling molecules. As the main organic substrate used by cells in the intestinal tract, the role of the chiral specificity of glutamine is still largely ignored. RESULTS: In a previous study, we found that d-glutamine affected the quorum sensing of Lactiplantibacillus plantarum A3, promoted the release of signaling molecule AI-2 and up-regulated the expression of the LuxS gene. The results showed that when d-glutamine and L. plantarum A3 were simultaneously applied to a mouse model, the diversity and abundance of intestinal flora in both male and female mice were increased. Interestingly, the simultaneous effect of d-glutamine and L. plantarum A3 on the bacterial diversity and abundance of male mice was significantly higher than that of female mice. In addition, the combination of d-glutamine and L. plantarum A3 can improve the host microecology by enhancing the population of Firmicutes such as Lactobacillus and Lachnospiraceae, reducing the population of Fusobacterium and Bacteroides and affecting metabolic pathways such as AA metabolism and transporter transport. CONCLUSION: d-Glutamine, as a signaling molecule, can better stimulate the endogenous d-glutamine synthesis in mice and be utilized by L. plantarum A3. Furthermore, sex differences in the changes of intestinal microflora are also found in this research. This research sheds some light on the adoption of d-AAs combined with lactic acid bacteria in intestinal tract health treatment. © 2024 Society of Chemical Industry.

The Use of Diffusion Tensor Imaging in the Identification of Acute Rejection and Chronic Allograft Nephropathy After Renal Transplantation.

BACKGROUND: Identifying the cause of renal allograft dysfunction is important for the clinical management of kidney transplant recipients. PURPOSE: To evaluate the diagnostic efficiency of diffusion tensor imaging (DTI) for identifying allografts with acute rejection (AR) and chronic allograft nephropathy (CAN). STUDY TYPE: Prospective. SUBJECTS: Seventy-seven renal transplant patients (aged 42.5 ± 9.5 years), including 29 patients with well-functioning stable allografts (Control group), 25 patients diagnosed with acute rejection (AR group), and 23 patients diagnosed with chronic allograft nephropathy (CAN group). FIELD STRENGTH/SEQUENCE: 1.5 T/T2-weighted imaging and DTI. ASSESSMENT: The serum creatinine, proteinuria, pathologic results, and fractional anisotropy (FA) values were obtained and compared among the three groups. STATISTICAL TEST: One-way analysis of variance; correlation analysis; independent-sample t-test; intraclass correlation coefficients and receiver operating characteristic curves. Statistical significance was set to a P-value <0.05. RESULTS: The AR and CAN groups presented with significantly elevated serum creatinine as compared with the Control group (191.8 ± 181.0 and 163.1 ± 115.8 µmol/L vs. 82.3 ± 20.9 µmol/L). FA decreased in AR group (cortical/medullary: 0.13 ± 0.02/0.31 ± 0.07) and CAN group (cortical/medullary: 0.11 ± 0.02/0.27 ± 0.06), compared with the Control group (cortical/medullary: 0.15 ± 0.02/0.35 ± 0.05). Cortical FA in the AR group was higher than in the CAN group. The area under the curve (AUC) for identifying AR from normal allografts was 0.756 and 0.744 by cortical FA and medullary FA, respectively. The AUC of cortical FA and medullary FA for differentiating CAN from normal allografts was 0.907 and 0.830, respectively. The AUC of cortical FA and medullary FA for distinguishing AR and CAN from normal allografts was 0.828 and 0.785, respectively. Cortical FA was able to distinguish between AR and CAN with an AUC of 0.728. DATA CONCLUSION: DTI was able to detect patients with dysfunctional allografts. Cortical FA can further distinguish between AR and CAN. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 2.

Assessment of Cerebral White Matter Involvement in Amyotrophic Lateral Sclerosis Patients With Disease Progression and Cognitive Impairment by Fixel-Based Analysis and Neurite Orientation Dispersion and Density Imaging.

BACKGROUND: Previous studies using emerging diffusion MRI techniques have revealed damage to the white matter (WM) microstructure in amyotrophic lateral sclerosis (ALS), particularly the influence of crossed fibers, but there is a lack of subgroup analyses. PURPOSE: To detect WM microstructural changes in ALS patients using fixel-based analysis (FBA) and neurite orientation dispersion and density imaging (NODDI) MRI. STUDY TYPE: Prospective. POPULATION: Thirty-six ALS patients (aged 60.50 ± 9.5 years) and 25 healthy controls (HCs) (aged 58.90 ± 8.1 years). FIELD STRENGTH/SEQUENCE: 3 T; NODDI and FBA (b-values = 0, 1000, and 2500 seconds/mm2 ). ASSESSMENT: Subgroups were performed according to progression rate and cognition, including fast and slow progression (FP/SP), ALS with and without cognitive impairment (ALS-ci/ALS-nci). Fiber density (FD), fiber-bundle cross-section (FC), combined fiber density and cross-section (FDC), neurite density index (NDI), orientation dispersion index (ODI), isotropic volume fraction (ISO), and fractional anisotropy (FA) were calculated and their correlation with clinical variables examined. STATISTICAL TESTING: Chi-square test, Mann-Whitney U test, two-sample t test, partial correlation analysis, and false discovery rate (FDR) corrected. A P-value <0.05 was considered significant. RESULTS: ALS patients had lower FD and FDC values predominantly in the corticospinal tract (CST) and corpus callosum (CC) regions, as well as lower NDI value in the CC, radial crown, and internal capsule compared to HCs. Subgroup analysis based on progression rate and cognitive function showed significant differences in FBA results. The FC in the right CST region was significantly lower in the FP than SP, and the FD in the CC region was significantly lower in the ALS-ci than ALS-nci. Furthermore, a negative correlation was found between the mean FC value and the rate of progression in ALS patients (r = -0.408). DATA CONCLUSION: FBA is a powerful tool for detecting complex cerebral WM microstructural damage for evaluating ALS cognition and disease progression.

Preoperative Diagnosis of Dual-Phenotype Hepatocellular Carcinoma Using Enhanced MRI Radiomics Models.

BACKGROUND: Dual-phenotype hepatocellular carcinoma (DPHCC) is highly aggressive and difficult to distinguish from hepatocellular carcinoma (HCC). PURPOSE: To develop and validate clinical and radiomics models based on contrast-enhanced MRI for the preoperative diagnosis of DPHCC. STUDY TYPE: Retrospective. POPULATION: A total of 87 patients with DPHCC and 92 patients with non-DPHCC randomly divided into a training cohort (n = 125: 64 non-DPHCC; 61 DPHCC) and a validation cohort (n = 54: 28 non-DPHCC; 26 DPHCC). FIELD STRENGTH/SEQUENCE: A 3.0 T; dynamic contrast-enhanced MRI with time-resolved T1-weighted imaging sequence. ASSESSMENT: In the clinical model, the maximum tumor diameter and hepatitis B virus (HBV) were independent risk factors of DPHCC. In the radiomics model, a total of 1781 radiomics features were extracted from tumor volumes of interest (VOIs) in the arterial phase (AP) and portal venous phase (PP) images. For feature reduction and selection, Pearson correlation coefficient (PCC) and recursive feature elimination (RFE) were used. Clinical, AP, PP, and combined radiomics models were established using machine learning algorithms (support vector machine [SVM], logistic regression [LR], and logistic regression-least absolute shrinkage and selection operator [LR-LASSO]) and their discriminatory efficacy assessed and compared. STATISTICAL TESTS: The independent sample t test, Mann-Whitney U test, Chi-square test, regression analysis, receiver operating characteristic curve (ROC) analysis, Pearson correlation analysis, the Delong test. A P value < 0.05 was considered statistically significant. RESULTS: In the validation cohort, the combined radiomics model (area under the curve [AUC] = 0.908, 95% confidence interval [CI]: 0.831-0.985) showed the highest diagnostic performance. The AUCs of the PP (AUC = 0.879, 95% CI: 0.779-0.979) and combined radiomics models were significantly higher than that of clinical model (AUC = 0.685, 95% CI: 0.526-0.844). There were no significant differences in AUC between AP or PP radiomics model and combined radiomics model (P = 0.286, 0.180 and 0.543). CONCLUSION: MRI radiomics models may be useful for discriminating DPHCC from non-DPHCC before surgery. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 2.

Gd-EOB-DTPA-enhanced MRI radiomics to predict vessels encapsulating tumor clusters (VETC) and patient prognosis in hepatocellular carcinoma.

OBJECTIVES: The study was to develop a Gd-EOB-DTPA-enhanced MRI radiomics model for preoperative prediction of VETC and patient prognosis in hepatocellular cancer (HCC). METHODS: The study included 182 (training cohort: 128; validation cohort: 54) HCC patients who underwent preoperative Gd-EOB-DTPA-enhanced MRI. Volumes of interest including intratumoral and peritumoral regions were manually delineated in the hepatobiliary phase images, from which 1316 radiomics features were extracted. The least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression were used to select the useful features. Clinical, intratumoral, peritumoral, combined radiomics, and clinical radiomics models were established using machine learning algorithms. The Kaplan-Meier survival analysis was used to assess early recurrence and progression-free survival (PFS) in the VETC + and VETC- patients. RESULTS: In the validation cohort, the area under the curves (AUCs) of radiomics models were higher than that of the clinical model using random forest (all p < 0.05). The peritumoral radiomics model (AUC = 0.972;95% confidence interval [CI]:0.887-0.998) had significantly higher AUC than intratumoral model (AUC = 0.919; 95% CI: 0.811-0.976) (p = 0.044). There were no significant differences in AUC between intratumoral or peritumoral radiomics model (PR) and combined radiomics model (p > 0.05). Early recurrence and PFS were significantly different between the PR-predicted VETC + and VETC- HCC patients (p < 0.05). PR-predicted VETC was independent predictor of early recurrence (hazard ratio [HR]: 2.08[1.31-3.28]; p = 0.002) and PFS (HR: 1.95[1.20-3.17]; p = 0.007). CONCLUSIONS: The intratumoral or peritumoral radiomics model may be useful in predicting VETC and patient prognosis preoperatively. The peritumoral radiomics model may yield an incremental value over intratumoral model. KEY POINTS: • Radiomics models are useful for predicting vessels encapsulating tumor clusters (VETC) and patient prognosis preoperatively. • Peritumoral radiomics model may yield an incremental value over intratumoral model in prediction of VETC. • Peritumoral radiomics-model-predicted VETC was an independent predictor of early recurrence and progression-free survival.

The added value of radiomics from dual-energy spectral CT derived iodine-based material decomposition images in predicting histological grade of gastric cancer.

BACKGROUND: The histological differentiation grades of gastric cancer (GC) are closely related to treatment choices and prognostic evaluation. Radiomics from dual-energy spectral CT (DESCT) derived iodine-based material decomposition (IMD) images may have the potential to reflect histological grades. METHODS: A total of 103 patients with pathologically proven GC (low-grade in 40 patients and high-grade in 63 patients) who underwent preoperative DESCT were enrolled in our study. Radiomic features were extracted from conventional polychromatic (CP) images and IMD images, respectively. Three radiomic predictive models (model-CP, model-IMD, and model-CP-IMD) based on solely CP selected features, IMD selected features and CP coupled with IMD selected features were constructed. The clinicopathological data of the enrolled patients were analyzed. Then, we built a combined model (model-Combine) developed with CP-IMD and clinical features. The performance of these models was evaluated and compared. RESULTS: Model-CP-IMD achieved better AUC results than both model-CP and model-IMD in both cohorts. Model-Combine, which combined CP-IMD radiomic features, pT stage, and pN stage, yielded the highest AUC values of 0.910 and 0.912 in the training and testing cohorts, respectively. Model-CP-IMD and model-Combine outperformed model-CP according to decision curve analysis. CONCLUSION: DESCT-based radiomics models showed reliable diagnostic performance in predicting GC histologic differentiation grade. The radiomic features extracted from IMD images showed great promise in terms of enhancing diagnostic performance.

Radiomic analysis of Gd-EOB-DTPA-enhanced MRI predicts Ki-67 expression in hepatocellular carcinoma.

BACKGROUND: Nuclear protein Ki-67 indicates the status of cell proliferation and has been regarded as an attractive biomarker for the prognosis of HCC. The aim of this study is to investigate which radiomics model derived from different sequences and phases of gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI was superior to predict Ki-67 expression in hepatocellular carcinoma (HCC), then further to validate the optimal model for preoperative prediction of Ki-67 expression in HCC. METHODS: This retrospective study included 151 (training cohort: n = 103; validation cohort: n = 48) pathologically confirmed HCC patients. Radiomics features were extracted from the artery phase (AP), portal venous phase (PVP), hepatobiliary phase (HBP), and T2-weighted (T2W) images. A logistic regression with the least absolute shrinkage and selection operator (LASSO) regularization was used to select features to build a radiomics score (Rad-score). A final combined model including the optimal Rad-score and clinical risk factors was established. Receiver operating characteristic (ROC) curve analysis, Delong test and calibration curve were used to assess the predictive performance of the combined model. Decision cure analysis (DCA) was used to evaluate the clinical utility. RESULTS: The AP radiomics model with higher decision curve indicating added more net benefit, gave a better predictive performance than the HBP and T2W radiomic models. The combined model (AUC = 0.922 vs. 0.863) including AP Rad-score and serum AFP levels improved the predictive performance more than the AP radiomics model (AUC = 0.873 vs. 0.813) in the training and validation cohort. Calibration curve of the combined model showed a good agreement between the predicted and the actual probability. DCA of the validation cohort revealed that at a range threshold probability of 30-60%, the combined model added more net benefit compared with the AP radiomics model. CONCLUSIONS: A combined model including AP Rad-score and serum AFP levels based on enhanced MRI can preoperatively predict Ki-67 expression in HCC.

Nomogram to identify severe coronavirus disease 2019 (COVID-19) based on initial clinical and CT characteristics: a multi-center study.

BACKGROUND: To develop and validate a nomogram for early identification of severe coronavirus disease 2019 (COVID-19) based on initial clinical and CT characteristics. METHODS: The initial clinical and CT imaging data of 217 patients with COVID-19 were analyzed retrospectively from January to March 2020. Two hundred seventeen patients with 146 mild cases and 71 severe cases were randomly divided into training and validation cohorts. Independent risk factors were selected to construct the nomogram for predicting severe COVID-19. Nomogram performance in terms of discrimination and calibration ability was evaluated using the area under the curve (AUC), calibration curve, decision curve, clinical impact curve and risk chart. RESULTS: In the training cohort, the severity score of lung in the severe group (7, interquartile range [IQR]:5-9) was significantly higher than that of the mild group (4, IQR,2-5) (P < 0.001). Age, density, mosaic perfusion sign and severity score of lung were independent risk factors for severe COVID-19. The nomogram had a AUC of 0.929 (95% CI, 0.889-0.969), sensitivity of 84.0% and specificity of 86.3%, in the training cohort, and a AUC of 0.936 (95% CI, 0.867-1.000), sensitivity of 90.5% and specificity of 88.6% in the validation cohort. The calibration curve, decision curve, clinical impact curve and risk chart showed that nomogram had high accuracy and superior net benefit in predicting severe COVID-19. CONCLUSION: The nomogram incorporating initial clinical and CT characteristics may help to identify the severe patients with COVID-19 in the early stage.

Spectral Computed Tomography Imaging in the Differential Diagnosis of Lung Cancer and Inflammatory Myofibroblastic Tumor.

OBJECTIVE: The aim of this study was to explore the value of spectral computed tomography (CT) imaging in differentiating lung cancer from inflammatory myofibroblastic tumor (IMT). METHODS: One hundred twelve patients with 96 lung cancers and 16 IMTs underwent spectral CT during arterial phase (AP) and venous phase (VP). The normalized iodine concentration in AP (NICAP) and VP (NICVP), slope of spectral Hounsfield unit curve in AP (λAP) and VP (λVP), and normalized iodine concentration difference between AP and VP (ICD) were calculated. The 2-sample t test compared quantitative parameters. Two readers qualitatively assessed lesion types according to imaging features. Receiver operating characteristic curves were generated to calculate sensitivity and specificity. Sensitivity and specificity of the qualitative and quantitative studies were compared. RESULTS: The patients with IMT had significantly higher NICAP, NICVP, λAP, λVP, and ICD than did the patients with lung cancer (P < 0.05). The threshold NICVP of 0.425 would yield the highest sensitivity and specificity of 92.7% and 81.3%, respectively, for differentiating lung cancer from IMT. The logistic regression model produced from combining quantitative parameters NICAP, NICVP, λAP, and λVP provided a sensitivity and specificity of 100% and 81.3%, respectively, for differentiating lung cancer from IMT. CONCLUSIONS: Spectral CT imaging with the quantitative analysis may help to increase the accuracy of differentiating lung cancer from IMT.

Arterial spin labeling combined with T1 mapping for assessment of kidney function and histopathology in patients with long-term renal transplant survival after kidney transplantation.

Background: The long-term survival of kidney transplants is often influenced by various factors, among which renal allograft rejection is the most notable factor. A noninvasive and reliable imaging biomarker correlating with kidney function and histopathology would facilitate longitudinal long-term follow-up of renal allografts. The aim of the study is to investigate the value of arterial spin labeling (ASL) combined with T1 mapping for assessing kidney function in patients with long-term renal transplant survival, and to establish radiological and histopathologic correlations between the magnetic resonance imaging (MRI) measurements and kidney allograft biopsy findings. Methods: Kidney transplant recipients who were admitted to the Department of Urology in First Affiliated Hospital of Soochow University between January and December 2022 were prospectively consecutively recruited [group A, estimated glomerular filtration rate (eGFR) ≥60 mL/min/1.73 m2; group B, 30≤ eGFR <60 mL/min/1.73 m2; group C, eGFR <30 mL/min/1.73 m2], and part of them underwent biopsies. All patients underwent ASL and T1 mapping. MRI parameters were calculated and analyzed. Results: A total of 63 patients (Group A, 30 cases; Group B, 20 cases; and Group C, 13 cases) were included in this cross-sectional study. Cortical T1 increased, whereas renal blood flow (RBF) and ΔT1 [100% × (cortical T1 - medullary T1)/cortical T1] decreased with the decrease of eGFR. The RBF, cortical T1, and ΔT1 values were moderately correlated with eGFR (r=0.569, -0.573, and 0.672, respectively). The MRI parameters were moderately correlated with Banff scores, which determined renal allograft rejection and chronicity. The area under the curve (AUC) for the discrimination of groups A versus B and groups A versus C were 0.740 [95% confidence interval (CI): 0.597-0.854, P=0.004] and 0.923 (95% CI: 0.800-0.982, P<0.001), respectively, using ASL; 0.873 (95% CI: 0.749-0.950, P<0.001) and 0.926 (95% CI: 0.803-0.983, P<0.001), respectively, using T1 mapping; and 0.892 (95% CI: 0.771-0.962, P<0.001) and 0.956 (95% CI: 0.846-0.995, P<0.001), respectively, using multi-parameter MRI. The AUC for discrimination between groups B and C was 0.729 (95% CI: 0.546-0.868, P=0.02) using ASL. Conclusions: The RBF, cortical T1, and ΔT1 can serve as new imaging biomarkers of kidney function and histopathological microstructure.

Clinical­imaging­radiomic nomogram based on unenhanced CT effectively predicts adrenal metastases in patients with lung cancer with small hyperattenuating adrenal incidentalomas.

The aim of the present study was to develop and evaluate a clinical-imaging-radiomic nomogram based on pre-enhanced computed tomography (CT) for pre-operative differentiation lipid-poor adenomas (LPAs) from metastases in patients with lung cancer with small hyperattenuating adrenal incidentalomas (AIs). A total of 196 consecutive patients with lung cancer, who underwent initial chest or abdominal pre-enhanced CT scan with small hyperattenuating AIs, were included. The patients were randomly divided into a training cohort with 71 cases of LPAs and 66 cases of metastases, and a testing cohort with 31 cases of LPAs and 28 cases of metastases. Plain CT radiological and clinical features were evaluated, including sex, age, size, pre-enhanced CT value (CTpre), shape, homogeneity and border. A total of 1,316 radiomic features were extracted from the plain CT images of the AIs, and the significant features selected by the least absolute shrinkage and selection operator were used to establish a Radscore. Subsequently, a clinical-imaging-radiomic model was developed by multivariable logistic regression incorporating the Radscore with significant clinical and imaging features. This model was then presented as a nomogram. The performance of the nomogram was assessed by calibration curves and decision curve analysis (DCA). A total of 4 significant radiomic features were incorporated in the Radscore, which yielded notable area under the receiver operating characteristic curves (AUCs) of 0.920 in the training dataset and 0.888 in the testing dataset. The clinical-imaging-radiomic nomogram incorporating the Radscore, CTpre, sex and age revealed favourable differential diagnostic performance (AUC: Training, 0.968; testing, 0.915) and favourable calibration curves. The nomogram was revealed to be more useful than the Radscore and the clinical-imaging model in clinical practice by DCA. The clinical-imaging-radiomics nomogram based on initial plain CT images by integrating the Radscore and clinical-imaging factors provided a potential tool to effectively differentiate LPAs from metastases in patients with lung cancer with small hyperattenuating AIs.

Hepatocellular carcinoma and focal nodular hyperplasia of the liver: differentiation with CT spectral imaging.

OBJECTIVES: To investigate the value of CT spectral imaging in differentiating hepatocellular carcinoma (HCC) from focal nodular hyperplasia (FNH) during the arterial phase (AP) and portal venous phase (PP). METHODS: Fifty-eight patients with 42 HCCs and 16 FNHs underwent spectral CT during AP and PP. The lesion-liver contrast-to-noise ratio (CNR) at different energy levels, normalised iodine concentrations (NIC) and the lesion-normal parenchyma iodine concentration ratio (LNR) were calculated. The two-sample t test compared quantitative parameters. Two readers qualitatively assessed lesion types according to imaging features. Sensitivity and specificity of the qualitative and quantitative studies were compared. RESULTS: In general, CNRs at low energy levels (40-70 keV) were higher than those at high energy levels (80-140 keV). NICs and LNRs for HCC differed significantly from those of FNH: mean NICs were 0.25 mg/mL ± 0.08 versus 0.42 mg/mL ± 0.12 in AP and 0.52 mg/mL ± 0.14 versus 0.86 mg/mL ± 0.18 in PP. Mean LNRs were 2.97 ± 0.50 versus 6.15 ± 0.62 in AP and 0.99 ± 0.12 versus 1.22 ± 0.26 in PP. NICs and LNRs for HCC were lower than those of FNH. LNR in AP had the highest sensitivity and specificity in differentiating HCC from FNH. CONCLUSIONS: CT spectral imaging may help to increase detectability of lesions and accuracy of differentiating HCC from FNH. KEY POINTS: • CT spectral imaging may help to detect hepatocellular carcinoma (HCC). • CT spectral imaging may help differentiate HCC from focal nodular hyperplasia. • Quantitative analysis of iodine concentration provides greater diagnostic confidence. • Treatment can be given with greater confidence.

An Assessment of the Pathological Classification and Postoperative Outcome of Focal Cortical Dysplasia by Simultaneous Hybrid PET/MRI.

OBJECTIVES: The purpose of this research was to investigate whether MRI and Simultaneous Hybrid PET/MRI images were consistent in the histological classification of patients with focal cortical dysplasia. Additionally, this research aimed to evaluate the postoperative outcomes with the MRI and Simultaneous Hybrid PET/MRI images of focal cortical dysplasia. METHODS: A total of 69 cases in this research were evaluated preoperatively for drug-resistant seizures, and then surgical resection procedures of the epileptogenic foci were performed. The postoperative result was histopathologically confirmed as focal cortical dysplasia, and patients then underwent PET and MRI imaging within one month of the seizure. In this study, head MRI was performed using a 3.0 T magnetic resonance scanner (Philips) to obtain 3D T1WI images. The Siemens Biograph 16 scanner was used for a routine scanning of the head to obtain PET images. BrainLAB's iPlan software was used to fuse 3D T1 images with PET images to obtain PET/MRI images. RESULTS: Focal cortical dysplasia was divided into three types according to ILAE: three patients were classified as type I, twenty-five patients as type II, and forty-one patients as type III. Patients age of onset under 18 and age of operation over 18 had a longer duration (p = 0.036, p = 0.021). MRI had a high lesion detection sensitivity of type III focal cortical dysplasia (p = 0.003). Simultaneous Hybrid PET/MRI showed high sensitivity in detecting type II and III focal cortical dysplasia lesions (p = 0.037). The lesions in Simultaneous Hybrid PET/MRI-positive focal cortical dysplasia patients were mostly located in the temporal and multilobar (p = 0.005, 0.040). CONCLUSION: Simultaneous Hybrid PET/MRI has a high accuracy in detecting the classification of focal cortical dysplasia. The results of this study indicate that patients with focal cortical dysplasia with positive Simultaneous Hybrid PET/MRI have better postoperative prognoses.

Dual-energy CT in assessment of thrombus perviousness and its application in predicting outcomes after intravenous thrombolysis in acute ischemic stroke.

PURPOSE: To evaluate the feasibility of using iodine overlay maps reconstructed from dual-energy CT (DECT) to assess thrombus perviousness and investigate its value in predicting outcomes after intravenous thrombolysis in patients with acute ischemic stroke. METHOD: 86 patients with proximal intracranial occlusions of the anterior circulation who underwent intravenous thrombolysis were included in this study. Thrombus iodine concentrations (ICthrombus) and normalized iodine concentrations (NICthrombus) were compared to conventional perviousness parameters (thrombus attenuation increase, TAI; void fraction, ε and CTA-index). The associations between perviousness parameters and outcomes were analyzed by Spearman's correlation and regression analysis. RESULTS: ICthrombus and NICthrombus were significantly correlated with conventional perviousness parameters (P < 0.001). The median ICthrombus was 6.81 (interquartile range [IQR], 4.76-8.73) mg/ml in the favorable functional outcome group, which was higher than 3.52 (IQR, 2.08-6.86) mg/ml in the unfavorable outcome group (P = 0.001). The median NICthrombus was 0.095 (IQR, 0.068-0.116) and 0.054 (IQR, 0.031-0.083) in the favorable and unfavorable outcome groups, respectively (P < 0.001). NICthrombus predicted favorable outcome with a higher area under the curve (AUC) of 0.755 than any conventional perviousness parameter (P < 0.05). In the multivariable regression model, ICthrombus was independently associated with favorable outcome (odds ratio [OR] = 1.472, 95 % CI: 1.154-1.877, P = 0.002) and successful recanalization (OR = 1.356, 95 % CI: 1.093-1.681, P = 0.006). ICthrombus was negatively correlated with the final infarct volume (FIV) (r = -0.262, P = 0.020). Results for NICthrombus were similar. CONCLUSIONS: DECT is of great value in assessing thrombus perviousness. NICthrombus is a meaningful predictor of stroke prognosis and recanalization after intravenous thrombolysis in acute ischemic stroke.

Preliminary Findings on the Potential Use of Magnetic Resonance Elastography to Diagnose Lacunar Infarction.

Purpose: Lacunar infarction is usually diagnosed by conventional technologies, such as CT and diffusion weighted imaging (DWI). To improve the accuracy of diagnosis, neurocognitive screening is still needed. Therefore, additional imaging methods that can assist and provide more accurate and rapid diagnostics are urgently needed. As an initial step towards potentially using MR elastography (MRE) for such diagnostic purposes, we tested the hypothesis that the mechanical properties of tissue in the vicinity of cerebral vasculature change following lacunar infarction in a way that can be quantified using MRE. Patients and Methods: MRE and MR angiography (MRA) images from 51 patients diagnosed with lacunar infarction and 54 healthy volunteers were acquired on a 3T scanner. All diagnoses were confirmed by matching neurocognitive test results to locations of flow obstruction in MRA. ROIs of the cerebral vessels segmented on the MRA images were mapped to the MRE images. Interpolation-based inversion was applied to estimate the regional biomechanical properties of ROIs that included cerebral vessels. The effects of lacunar infarction, sex, and age were analyzed using analysis of covariance (ANOCOVA). Results: Shear moduli over vessel ROIs were significantly lower for the lacunar infarction group than those of the healthy control group. A positive correlation between modulus over vessel ROIs and age was observed. However, no significant correlation was found between sex and the regional biomechanical properties of the vessel ROIs. Conclusion: Results supported the hypothesis and suggest that biomechanical properties may be of utility in diagnosis of lacunar infarction.

Adhesion properties of cell surface proteins in Lactobacillus strains in the GIT environment.

As a broadly defined member of lactic acid bacteria (LAB), the Lactobacillus strain is well characterized in food fermentation and specific strains can enhance the intestinal barrier function and be recognized as the probiotic strain. In recent years, many molecules of the cell surface are thought to be related to the adhesion property in the gastrointestinal mucosa. Mucus layer-related proteins, extracellular matrix proteins, and immunoglobulins also exhibit immunity regulation and protection of the intestinal epithelial barrier function. Meanwhile, the effects of bile and the low pH of the gastrointestinal tract (GIT) on Lactobacillus colonization are also needed to be considered. Furthermore, LAB can adhere and aggregate in the GIT to promote the maturity of biofilm and the extracellular matrix secreting through the signal molecules in the quorum sensing (QS) system. Therefore, it is of great interest to use the QS system to regulate the initial adhesion ability of Lactobacillus and further enhance the probiotic effect of the biofilm formation of beneficial bacteria. This review summarizes the adhesion properties of cell surface proteins derived from Lactobacillus strains in recent studies and provides valuable information on the QS effect on the adhesion property of Lactobacillus strains in the GIT environment.

Amino Acid-Derived Quorum Sensing Molecule Alanine on the Gastrointestinal Tract Tolerance of the Lactobacillus Strains in the Cocultured Fermentation Model.

More and more people are aware of the importance of intestinal flora to human health, and people are interested in the regulation of intestinal flora and its interaction with the host. The survival status of the probiotics in the gastrointestinal environment and the microbial interactions between the lactic acid bacteria have also received considerable attention. In this study, the gastrointestinal environment tolerance, adhesion ability, and biofilm formation of the Lactobacillus strain in the coculture system were explored through the real-time fluorescence-based quantitative PCR, UPLC-MS/MS metabolic profiling analysis, and Live/Dead BacLight cell staining methods. The results showed that the coculture system could promote the release of signal molecules autoinducer-2 and effectively protect the viability of the Lactobacillus acidophilus in the gastrointestinal environment. Meanwhile, amino acid-derived characteristic metabolite l-alanine (1%) could effectively enhance the communication of the cells in the complex fermentation model, which led to an increase in the tolerance ability of the L. acidophilus by 28% in the gastrointestinal-like environment. IMPORTANCE It was deduced from the study that amino acid-derived metabolites play an important role in cell communication in the gastrointestinal tract (GIT) environment, thus enhancing the communication of Lactobacillus strains in the complex fermentation model. Meanwhile, the viability of Lactobacillus acidophilus can be increased in the coculture system during the gastrointestinal stress environment treated with the amino acid-derived quorum sensing (QS) molecule l-alanine. It will shed some light on the application of amino acid-derived QS molecules in the fermentation stater industry.