Enhancing image quality in computed tomography angiography follow-ups after endovascular aneurysm repair: a comparative study of reconstruction techniques.

BACKGROUND: The image quality of computed tomography angiography (CTA) images following endovascular aneurysm repair (EVAR) is not satisfactory, since artifacts resulting from metallic implants obstruct the clear depiction of stent and isolation lumens, and also adjacent soft tissues. However, current techniques to reduce these artifacts still need further advancements due to higher radiation doses, longer processing times and so on. Thus, the aim of this study is to assess the impact of utilizing Single-Energy Metal Artifact Reduction (SEMAR) alongside a novel deep learning image reconstruction technique, known as the Advanced Intelligent Clear-IQ Engine (AiCE), on image quality of CTA follow-ups conducted after EVAR. MATERIALS: This retrospective study included 47 patients (mean age ± standard deviation: 68.6 ± 7.8 years; 37 males) who underwent CTA examinations following EVAR. Images were reconstructed using four different methods: hybrid iterative reconstruction (HIR), AiCE, the combination of HIR and SEMAR (HIR + SEMAR), and the combination of AiCE and SEMAR (AiCE + SEMAR). Two radiologists, blinded to the reconstruction techniques, independently evaluated the images. Quantitative assessments included measurements of image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), the longest length of artifacts (AL), and artifact index (AI). These parameters were subsequently compared across different reconstruction methods. RESULTS: The subjective results indicated that AiCE + SEMAR performed the best in terms of image quality. The mean image noise intensity was significantly lower in the AiCE + SEMAR group (25.35 ± 6.51 HU) than in the HIR (47.77 ± 8.76 HU), AiCE (42.93 ± 10.61 HU), and HIR + SEMAR (30.34 ± 4.87 HU) groups (p < 0.001). Additionally, AiCE + SEMAR exhibited the highest SNRs and CNRs, as well as the lowest AIs and ALs. Importantly, endoleaks and thrombi were most clearly visualized using AiCE + SEMAR. CONCLUSIONS: In comparison to other reconstruction methods, the combination of AiCE + SEMAR demonstrates superior image quality, thereby enhancing the detection capabilities and diagnostic confidence of potential complications such as early minor endleaks and thrombi following EVAR. This improvement in image quality could lead to more accurate diagnoses and better patient outcomes.

Quantitative assessment of brown adipose tissue whitening in a high-fat-diet murine model using synthetic magnetic resonance imaging.

Purpose: This study aimed to quantitatively evaluate the whitening process of brown adipose tissue (BAT) in mice using synthetic magnetic resonance imaging (SyMRI) and analyzed the correlation between SyMRI quantitative measurements of BAT and serum lipid profiles. Methods: Fifteen C57BL/6 mice were divided into three groups and fed different diets as follows: normal chow diet for 12 weeks, NCD group; high-fat diet (HFD) for 12 weeks, HFD-12w group; and HFD for 36 weeks, HFD-36w group. Mice were scanned using 3.0 T SyMRI. T1 and T2 values of BAT and interscapular BAT (iBAT) volume were measured. After sacrifice, the body weight of mice, lipid profiles, BAT morphology, and uncoupling protein 1 (UCP1) levels were determined. Statistical analysis was performed using one-way analysis of variance or Kruskal-Wallis test followed by Bonferroni correction for pairwise comparisons. Bonferroni-adjusted significance level was set at P < 0.017 (alpha: 0.05/3 = 0.017). Results: T2 values of BAT in the HFD-12w group were significantly higher than those in the NCD group (P < 0.001), and those in the HFD-36w group were significantly higher than those in the other two groups (both P < 0.001). The iBAT volume in the HFD-36w group was significantly higher than that in the HFD-12w (P = 0.013) and NCD groups (P = 0.005). T2 values of BAT and iBAT volume were significantly correlated with serum lipid profiles and mouse body weight. Conclusions: SyMRI can noninvasively evaluate the whitening process of BAT using T2 values and iBAT volume, thereby facilitating the visualization of the whitening process.

Quantitative assessment of inflammation and evaluation of spatial heterogeneity for non-alcoholic fatty liver disease in mice based on iron-adjustive T1.

Background: A sensitive and non-invasive method is necessary to diagnose non-alcoholic fatty liver disease (NAFLD). We explored the iron-adjustive T1 (aT1) ability to quantify the degree of liver inflammation and evaluate the spatial heterogeneity. Methods: Male C57BL/6J mice were randomly categorized as the NAFLD model (n=40), NAFLD-related liver cirrhosis model (n=20), and normal mice (n=10). T1 and T2* maps were acquired using a 3.0T scanner of magnetic resonance imaging (MRI) and aT1 maps through post-processing corrected iron's effect on T1 using T2*. Pathological changes in the left and right liver lobes were assessed using the Non-alcoholic Steatohepatitis-Clinical Research Network scoring system, though hepatic ballooning lesion were rare in models. Spearman's and partial correlation analyses were used to evaluate correlations, and the receiver operating characteristic curve was used to analyze the diagnostic performance. Results: aT1 was highly correlated with NAFLD activity score (NAS) (r=0.747, P<0.001) but not with the fibrosis stage when adjusted by NAS (r=-0.135, P=0.147). The area under the curve (AUC) of the aT1 value distinguishing groups with 0< NAS <4 and NAS ≥4 was 0.802. On analyzing the histogram features of aT1, the entropy, interquartile range, range, and variance were significantly different between the groups with 0< NAS <4 and NAS ≥4 (P<0.05). The entropy was the risk factor of NAS ≥4. Conclusions: aT1 could help evaluate the inflammatory activity in NAFLD mice unaffected by mild fibrosis, and the higher the degree of inflammation, the higher the heterogeneity of the aT1 map.

Evaluation of Hypoxia in Hepatocellular Carcinoma Using Quantitative MRI: Significances, Challenges, and Advances.

This review aimed to perform a scoping review of promising MRI methods in assessing tumor hypoxia in hepatocellular carcinoma (HCC). The hypoxic microenvironment and upregulated hypoxic metabolism in HCC are determining factors of poor prognosis, increased metastatic potential, and resistance to chemotherapy and radiotherapy. Assessing hypoxia in HCC is essential for personalized therapy and predicting prognoses. Oxygen electrodes, protein markers, optical imaging, and positron emission tomography can evaluate tumor hypoxia. These methods lack clinical applicability because of invasiveness, tissue depth, and radiation exposure. MRI methods, including blood oxygenation level-dependent, dynamic contrast-enhanced MRI, diffusion-weighted imaging, MRI spectroscopy, chemical exchange saturation transfer MRI, and multinuclear MRI, are promising noninvasive methods that evaluate the hypoxic microenvironment by observing biochemical processes in vivo, which may inform on therapeutic options. This review summarizes the recent challenges and advances in MRI techniques for assessing hypoxia in HCC and highlights the potential of MRI methods for examining the hypoxic microenvironment via specific metabolic substrates and pathways. Although the utilization of MRI methods for evaluating hypoxia in patients with HCC is increasing, rigorous validation is needed in order to translate these MRI methods into clinical use. Due to the limited sensitivity and specificity of current quantitative MRI methods, their acquisition and analysis protocols require further improvement. EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 4.

Feasibility of Imaging-Based Quantitative Hepatic Function Assessment: Gd-EOB-DTPA-Enhanced Liver MR Imaging Combined with T1 Mapping.

OBJECTIVE: to investigate the feasibility of gadoxetic acid (Gd-EOB-DTPA) enhanced MRI combined with T1 mapping in quantitative hepatic function assessment. METHODS: this study retrospectively enrolled 94 patients with Gd-EOB-DTPA enhanced MRI combined with T1 mapping, divided into group A (grade A, n=73), group B (grade B, n=14) and group C (grade C, n=7) based on Child-Pugh classification. Liver T1 relaxation times on plain scan (T1P) and hepatocellular phase (T1E) were measured. Decrease in T1 (T1D) and the percentage of decrease in T1 (T1D%) were calculated as follows: T1D=T1P-T1E, T1D%= T1D/T1P×100%. The relationship between T1P, T1E, T1D, T1D% and liver function classification was analyzed. RESULTS: T1P, T1D, and T1D% in group A were significantly higher than those of group B and C. T1E in group A was lower than those of group B and C. T1D% was significantly different between group B and C. There was no significant difference in T1P, T1E, T1D between groups B and C. T1E was positively correlated with liver function levels, T1P and T1D had a negative correlation with liver function levels. T1P, T1E, T1D, T1D% were significantly different between cirrhotic and non-cirrhotic groups. T1D% of less than 70% suggests liver dysfunction. CONCLUSION: Gd-EOB-DTPA enhanced liver MRI combined with T1 mapping is feasible for quantitative assessment of hepatic function.

Utility of Quantitative Metrics From Dual-Layer Spectral-Detector CT for Differentiation of Pancreatic Neuroendocrine Tumor and Neuroendocrine Carcinoma.

BACKGROUND. The 2019 WHO classification of digestive system tumors separates neuroendocrine neoplasms (NENs) into neuroendocrine tumors (NETs) and neuroendocrine carcinomas (NECs), which are considered to represent pathologically distinct entities warranting different management approaches. Dual-layer spectral-detector CT (DLCT) may aid their differentiation through specific material decomposition. OBJECTIVE. The purpose of this study was to assess the utility of quantitative metrics derived from DLCT for the differentiation of pancreatic NET and NEC. METHODS. This retrospective study included 104 patients (mean age, 51 ± 13 [SD] years; 52 women, 52 men) with pathologically confirmed NEN (89 NET, including 22 grade 1, 48 grade 2, and 19 grade 3; 15 NEC) who underwent multiphase DLCT within 15 days before biopsy or resection. Two radiologists independently placed ROIs to record tumor attenuation, iodine concentration (IC), and effective atomic number (Zeff) across phases and assessed qualitative features (composition, homogeneity, margins, calcifications, main pancreatic duct dilatation, vascular invasion, lymphadenopathy). Interobserver agreement was assessed. Mean and median values of both readers' measurements were obtained for quantitative measures; consensus was reached for qualitative features. NET and NEC were compared using multivariable regression analysis and ROC analysis. RESULTS. Interobserver agreement, expressed as intraclass correlation coefficients, ranged from 0.869 to 0.992 for quantitative metrics and, expressed as kappa coefficients, ranged from 0.723 to 0.816 for qualitative features. In multivariable analysis of qualitative and quantitative features, significant independent predictors of NEC (p < .05) were IC in the portal venous phase (median, 1.3 mg/mL for NEC vs 2.7 mg/mL for NET), Zeff in the portal venous phase (median, 8.1 vs 8.6), and attenuation in the portal venous phase (median, 78.2 vs 113.5 HU). AUC for predicting NEC was 0.897 for IC, 0.884 for Zeff, 0.921 for combination of IC and Zeff, and 0.855 for attenuation. Predicted probability based on a combination of IC and Zeff achieved sensitivity of 93.33% and specificity of 80.90% for predicting NEC. Significant independent predictors (p < .05) for differentiating grade 3 NET and NEC were IC (median, 2.0 vs 1.3 mg/mL; AUC = 0.789) and attenuation (mean, 90.3 vs 78.2 HU; AUC = 0.647), both measured in the portal venous phase. CONCLUSION. Incorporation of DLCT metrics improves differentiation of NET and NEC compared with conventional CT attenuation and qualitative features. CLINICAL IMPACT. DLCT may help select patients with pancreatic NENs for platinum-based chemotherapies.

Quantification of brown adipose tissue in vivo using synthetic magnetic resonance imaging: an experimental study with mice model.

BACKGROUND: The white adipose tissue (WAT) and brown adipose tissue (BAT) are associated with the development of several obesity-associated disorders. The use of imaging techniques to differentiate BAT from WAT and quantify BAT volume remains challenging, due to limitations such as spatial resolution and magnetic field inhomogeneity. This study aimed to investigate the feasibility for differentiating BAT from WAT, and quantify the BAT volume in vivo using synthetic magnetic resonance imaging (MRI). METHODS: A total of 16 C57BL/6 mice were scanned using synthetic MRI. Quantitative longitudinal relaxation time (T1) and transverse relaxation time (T2) maps were obtained from the original synthetic MRI data using the synthetic MRI software offline. The T1 and T2 values of interscapular BAT (IBAT) and dorsal subcutaneous WAT were measured. The IBAT volume was calculated using synthetic MRI-derived T2-weighted images (T2WIs) based on its morphological characteristics and quantitative tissue values. The body weight of mice was measured, and the IBAT specimens were excised and weighted. The correlation between IBAT volume and the weight of IBAT gross specimen and between IBAT volume and mouse body weight was analyzed. RESULTS: The T1 values of BAT (330.3±19.57 ms) were higher than those of WAT (304.42±4.14 ms) (P<0.001), whereas the T2 values of BAT (66.06±5.06 ms) were lower than those of WAT (88.23±7.68 ms) (P<0.001). The area under the curve (AUC) values of the T1 and T2 for differentiating BAT from WAT was 0.942 and 0.995, respectively. The AUC of the T2 values was higher than that of T1 (P=0.04) using the DeLong test. The optimal cut-off value for T2 was 76 ms for differentiating BAT from WAT (100% sensitivity, 93.7% specificity). A moderate correlation was observed between IBAT volume and the weight of the IBAT gross specimen (r=0.662, P=0.014), and between IBAT volume and mouse body weight (r=0.653, P=0.016). CONCLUSIONS: The quantitative parameters derived using synthetic MRI may be used to detect and differentiate BAT from WAT in vivo. Synthetic MRI may help quantify BAT volume in vivo.

Prediction of Early Treatment Response to Initial Conventional Transarterial Chemoembolization Therapy for Hepatocellular Carcinoma by Machine-Learning Model Based on Computed Tomography.

PURPOSE: The treatment response to initial conventional transarterial chemoembolization (cTACE) is essential for the prognosis of patients with hepatocellular carcinoma (HCC). This study explored and verified the feasibility of machine-learning models based on clinical data and contrast-enhanced computed tomography (CT) image findings to predict early responses of HCC patients after initial cTACE treatment. PATIENTS AND METHODS: Overall, 110 consecutive unresectable HCC patients who were treated with cTACE for the first time were retrospectively enrolled. Clinical data and imaging features based on contrast-enhanced CT were collected for the selection of characteristics. Treatment responses were evaluated based on the modified Response Evaluation Criteria in Solid Tumors (mRECIST) by postoperative CT examination within 2 months after the procedure. Python (version 3.70) was used to develop machine learning models. Least absolute shrinkage and selection operator (LASSO) algorithm was applied to select features with the impact on predicting treatment response after the first TACE procedure. Six machine learning algorithms were used to build predictive models, including XGBoost, decision tree, support vector machine, random forest, k-nearest neighbor, and fully convolutional networks, and their performances were compared using receiver operator characteristic (ROC) curves to determine the best performing model. RESULTS: Following TACE, 31 patients (28.2%) were described as responsive to TACE, while 72 patients (71.8%) were nonresponsive to TACE. Portal vein tumor thrombosis type, albumin level, and distribution of tumors within the liver were selected for predictive model building. Among the models, the RF model showed the best performance, with area under the curve (AUC), accuracy, sensitivity, and specificity of 0.802, 0.784, 0.904, and 0.480, respectively. CONCLUSION: Machine learning models can provide an accurate prediction of the early response of initial TACE treatment for HCC, which can help in individualizing clinical decision-making and modification of further treatment strategies for patients with unresectable HCC.

Considerable effects of imaging sequences, feature extraction, feature selection, and classifiers on radiomics-based prediction of microvascular invasion in hepatocellular carcinoma using magnetic resonance imaging.

BACKGROUND: Microvascular invasion (MVI) has a significant effect on the prognosis of hepatocellular carcinoma (HCC), but its preoperative identification is challenging. Radiomics features extracted from medical images, such as magnetic resonance (MR) images, can be used to predict MVI. In this study, we explored the effects of different imaging sequences, feature extraction and selection methods, and classifiers on the performance of HCC MVI predictive models. METHODS: After screening against the inclusion criteria, 69 patients with HCC and preoperative gadoxetic acid-enhanced MR images were enrolled. In total, 167 features were extracted from the MR images of each sequence for each patient. Experiments were designed to investigate the effects of imaging sequence, number of gray levels (Ng), quantization algorithm, feature selection method, and classifiers on the performance of radiomics biomarkers in the prediction of HCC MVI. We trained and tested these models using leave-one-out cross-validation (LOOCV). RESULTS: The radiomics model based on the images of the hepatobiliary phase (HBP) had better predictive performance than those based on the arterial phase (AP), portal venous phase (PVP), and pre-enhanced T1-weighted images [area under the receiver operating characteristic (ROC) curve (AUC) =0.792 vs. 0.641/0.634/0.620, P=0.041/0.021/0.010, respectively]. Compared with the equal-probability and Lloyd-Max algorithms, the radiomics features obtained using the Uniform quantization algorithm had a better performance (AUC =0.643/0.666 vs. 0.792, P=0.002/0.003, respectively). Among the values of 8, 16, 32, 64, and 128, the best predictive performance was achieved when the Ng was 64 (AUC =0.792 vs. 0.584/0.697/0.677/0.734, P<0.001/P=0.039/0.001/0.137, respectively). We used a two-stage feature selection method which combined the least absolute shrinkage and selection operator (LASSO) and recursive feature elimination (RFE) gradient boosting decision tree (GBDT), which achieved better stability than and outperformed LASSO, minimum redundancy maximum relevance (mRMR), and support vector machine (SVM)-RFE (stability =0.967 vs. 0.837/0.623/0.390, respectively; AUC =0.850 vs. 0.792/0.713/0.699, P=0.142/0.007/0.003, respectively). The model based on the radiomics features of HBP images using the GBDT classifier showed a better performance for the preoperative prediction of MVI compared with logistic regression (LR), SVM, and random forest (RF) classifiers (AUC =0.895 vs. 0.850/0.834/0.884, P=0.558/0.229/0.058, respectively). With the optimal combination of these factors, we established the best model, which had an AUC of 0.895, accuracy of 87.0%, specificity of 82.5%, and sensitivity of 93.1%. CONCLUSIONS: Imaging sequences, feature extraction and selection methods, and classifiers can have a considerable effect on the predictive performance of radiomics models for HCC MVI.

Regional liver function analysis with gadoxetic acid-enhanced MRI and virtual hepatectomy: prediction of postoperative short-term outcomes for HCC.

OBJECTIVES: To explore the role of quantitative regional liver function assessed by preoperative gadoxetic acid-enhanced MRI with computer-aided virtual hepatectomy to predict short-term outcomes after major hepatectomy for HCC. METHODS: We retrospectively reviewed the records of 133 consecutive patients with HCC who underwent preoperative gadoxetic acid-enhanced MRI and indocyanine green (ICG) test. Forty-five patients received open major hepatectomy. Liver function reserve and the future liver remnant were evaluated by computer-aided virtual hepatectomy. Global liver functional parameters included the T1 relaxation time reduction rate (T1ratio) and functional liver volume (FV), whereas regional parameters included the rT1pos, rT1ratio, remnant FV (rFV), and remnant FV ratio (rFVratio) of the remnant liver. The functional parameters of the MRI and ICG were used to predict the short-term outcomes (liver failure and major complications) after major hepatectomy. RESULTS: The T1ratio and FV were correlated with the ICG test (rho = - 0.304 and - 0.449, p < 0.05). FV < 682.8 ml indicated preoperative ICG-R15 ≥ 14% with 0.765 value of the area under the curve (AUC). No patient who underwent major resection with good liver functional reserve (ICG < 14%) and enough future remnant volume (> 30% standard LV) developed liver failure. Low rT1ratio (< 66.5%) and high rT1pos (> 217.5 ms) may predict major complications (AUC = 0.831 and 0.756, respectively; p < 0.05). The rT1ratio was an independent risk factor for postoperative major complications (odds ratio [OR] = 0.845, 95% CI, 0.736-0.966; p < 0.05). CONCLUSION: Preoperative gadoxetic acid-enhanced MRI with computer-aided virtual hepatectomy may facilitate optimal assessment of regional liver functional reserve to predict short-term outcomes after major hepatectomy for HCC. KEY POINTS: • Preoperative gadoxetic acid-enhanced MRI with virtual hepatectomy and volumetric analysis can provide precise liver volume and regional functional assessment. • Quantitative regional liver function assessed by gadoxetic acid-enhanced MRI can predict the short-term outcomes after major hepatectomy in patients with HCC. • The regional liver function assessed by gadoxetic acid-enhanced MRI is an independent risk factor for postoperative major complications.

A Case of a Huge Inferior Vena Cava Leiomyosarcoma: Precise Preoperative Evaluation with Gadobutrol-Enhanced MRI.

BACKGROUND: Leiomyosarcoma of the inferior vena cava (IVC) is a rare malignant tumour with poor prognosis. Surgical resection is the first line of treatment to achieve the best possible outcome. However, precise preoperative evaluation is essential to guide therapeutic decisions. Here, the preoperative evaluation potential of gadobutrol-enhanced magnetic resonance imaging (MRI) was assessed in the management of a 42-year-old patient with a large IVC mass. METHODS: The patient first underwent enhanced computed tomography (CT), but the relationship between the left renal vein and the mass in the dilated IVC was ambiguous, and it remained unclear whether the right hepatic vein was invaded by the lesion. To make a precise assessment of the tumour, the patient subsequently underwent high-resolution MRI angiography examination combined with high-concentration contrast medium gadobutrol. RESULTS: MRI demonstrated the integrity of the right hepatic vein and the left renal vein. Following a multidisciplinary consultation, a complicated surgery including complete resection of the mass, artificial vessel replacement of IVC, total hepatectomy, and bilateral nephrectomy with liver and kidney auto-transplantation was performed successfully. The surgical plan formulated after reviewing the MRI preoperatively was adhered to the course of the surgery. Postoperative CT re-examination showed that the blood flow of the artificial blood vessel and the right hepatic vein was unobstructed. Histopathological examination confirmed the tumour to be a leiomyosarcoma. CONCLUSION: Preoperative imaging diagnosis and assessment have important implications for the surgical planning of IVC leiomyosarcoma. High-resolution MRI angiography examination with high concentration contrast medium gadobutrol may be of particular benefit in IVC tumours.

Diffusion Kurtosis MR Imaging versus Conventional Diffusion-Weighted Imaging for Distinguishing Hepatocellular Carcinoma from Benign Hepatic Nodules.

Objectives: To assess the efficacy of diffusion kurtosis imaging (DKI) and compare DKI-derived parameters with conventional diffusion-weighted imaging (DWI) for distinguishing hepatocellular carcinoma (HCC) from benign hepatic nodules including focal nodular hyperplasia (FNH), hemangioma, and hepatocellular adenoma (HCA). Materials and Methods: 151 patients with 182 hepatic nodules (114 HCCs and 68 benign nodules including 33 FNHs, 29 hemangiomas, and 6 HCAs) were analyzed. Preoperative MRI examinations including DKI (b values: 0, 200, 500, 800, 1500, and 2000 sec/mm2) were performed, and kurtosis (K), diffusivity (D), and apparent diffusion coefficient (ADC) were calculated. The efficacy of DKI-derived parameters K, D, and ADC for distinguishing HCC from these benign nodules was analyzed. Results: ROC (receiver operating characteristic curve) analysis showed the optimal cutoff values of ADC, D, and K for identification of these benign nodules, and HCCs were 1.295 (area under the curve (AUC): 0.826; sensitivity 80.6%; specificity 70.8%), 1.787 (AUC: 0.770; sensitivity 83.6%; specificity 59.6%), and 1.002 (AUC: 0.761; sensitivity 65.5%; specificity 79.0%), respectively. Statistically significant differences were found in ADC, D, and K values between groups of HCC-FNH and HCC-hemangioma (P < 0.05). There were significant differences in K and ADC values between groups of FNH-hemangioma and HCA-hemangioma (P < 0.05), respectively. Using logistic regression analysis, a regression equation was obtained: Logit(P)=-1.982X 1+1.385X 3+1.948(X 1: ADC; X 3: K), and odds ratios (OR) were 0.138 (95% confidence interval (CI): 0.052, 0.367), and 8.996 (95% CI: 0.970, 16.460), respectively. Conclusion: Both ADC value and DKI-derived parameters K and D values have demonstrated a higher preoperative efficacy in distinguishing HCC from FNH, hemangioma, and HCA. No evidence was shown to suggest D or K value was superior to the ADC value.

Prediction of type 2 diabetes mellitus using noninvasive MRI quantitation of visceral abdominal adiposity tissue volume.

BACKGROUND: The correlation between visceral adipose tissue volume (VATV), hepatic proton-density fat fraction (PDFF), and pancreatic PDFF has been previously studied to predict the presence of type 2 diabetes mellitus (T2DM). This study investigated VATV quantitation in patients with T2DM, prediabetes, and normal glucose tolerance (NGT) using MRI to assess the roles of VATV, hepatic, and pancreatic PDFF in predicting the presence of T2DM. METHODS: Forty-eight patients with a new clinical diagnosis of T2DM (n=15), prediabetes (n=17), or NGT (n=16) were included and underwent abdominal magnetic resonance imaging (MRI) scanning with the iterative decomposition of water and fat with echo asymmetry and least square estimation image quantification (IDEAL-IQ) sequencing. VATV was obtained at the level of the 2nd and 3rd lumbar vertebral bodies (VATV L2 and VATV L3) where the sum of VATV L2 and VATV L3 (total VATV) were computed, respectively. Also, pancreatic and hepatic fat content was quantified by measuring the PDFF. The receiver operating characteristic (ROC) curve and binary logistics regression model analysis were employed to evaluate their ability to predict the presence of T2DM. RESULTS: The VATV L2, VATV L3, and total VATV values of the T2DM group were significantly higher than the prediabetes and NGT groups (P<0.05). There was no statistically significant difference between the values of VATV L2, VATV L3, and total VATV between the prediabetes and NGT groups (P>0.05). The ROC curve showed the areas under the curve for VATV L2, VATV L3, total VATV, hepatic PDFF, and pancreatic PDFF were 0.76, 0.80, 0.80, 0.79, and 0.75, respectively, in predicting the presence of T2DM (P<0.01). The ROC curves of VATV L2, VATV L3, total VATV, hepatic PDFF, and pancreatic PDFF failed to predict the presence of prediabetes and NGT (P>0.05). The binary logistics regression model analysis revealed that only VATV L3 was independently associated with the incidence of T2DM (P=0.01 and OR =1.01). The sensitivity, specificity, and total accuracy were 80.00%, 88.20%, and 84.40%, respectively. CONCLUSIONS: Compared with hepatic PDFF, pancreatic PDFF, VAVT L2, and total VATV, VAVT L3 was the better predictor of T2DM.

Imaging biomarkers for well and moderate hepatocellular carcinoma: preoperative magnetic resonance image and histopathological correlation.

BACKGROUND: Our aim of the study is to investigate the feasibility of preoperative prediction for hepatocellular carcinoma (HCC) histological grading using gadoxetic acid-enhanced magnetic resonance imaging (MRI). METHODS: This study included one hundred and fifty-six patients with solitary HCC. Preoperative gadoxetic acid-enhanced MRI findings were retrospectively analyzed. MRI qualitative features such as tumor size, margin, capsule status, signal homogeneity, intratumoral vessels, peritumoral enhancement during mid-arterial phase, peritumoral hypointensity during the hepatobiliary phase (HBP) were investigated. Apparent diffusion coefficients (ADCs), T1 reduction ratio of pre- and post-contrast enhanced images of the tumors were calculated. HCC histological grading in surgical specimens were confirmed by Edmonson's criteria. Correlations between these MRI features and HCC histological grading were analyzed using multivariate logistic regression. The receiver operating characteristic (ROC) curve was used to assess the predictive efficacy of the model. RESULTS: Univariate analysis showed that maximum tumor diameter (p = 0.004), tumor margin (p = 0.006), intratumoral vessels (p = 0.001) and peritumoral hypointensity during HBP (p = 0.000), were significantly correlated with HCC histological grading. There was no relationship between capsule, tumor signal, venous thrombosis, peritumoral enhancement during mid-arterial phase, ADC value, T1 reduction ratio, and HCC histological grading. Multivariate logistic regression analysis demonstrated that the maximum tumor diameter (p = 0.012, odds ratio = 1.002, 95% confidence interval: 1.007-1.046)) was an independent risk factor for high grade HCC. CONCLUSIONS: Greater tumor size, a more irregular margin, presence of intratumoral vessels, and peritumoral hypointensity during HBP were indicators for high grade HCC. The maximum tumor diameter was an independent risk factor for high grade HCC.

MRI T2-Weighted Imaging and Fat-Suppressed T2-Weighted Imaging Image Fusion Technology Improves Image Discriminability for the Evaluation of Anal Fistulas.

OBJECTIVE: To explore whether MRI fusion technology (combined T2-weighted imaging [T2WI] and fat-suppressed T2WI [T2WI-FS]) improves signal differences between anal fistulas and surrounding structures. MATERIALS AND METHODS: A total of 32 patients with confirmed diagnoses of anal fistula were retrospectively studied. All available T2WI and T2WI-FS images for each patient were used to generate fusion image (T2WI-Fusion) based on the addition of gray values obtained from each pixel via an MR post-processing work station. The discriminability of fistula, perianal sphincter, and perianal fat in T2WI, T2WI-FS, and T2WI-Fusion images was quantified with Fisher's scoring algorithm. For subjective visual image assessment by researchers, five-point scale scores were determined using a modified double-stimulus continuous quality-scale test to evaluate T2WI-FS, T2WI, enhanced axial three-dimensional-volumetric interpolated breath-hold examination (3D-VIBE), and T2WI-Fusion sequence images. The differences were subsequently compared. RESULTS: Mean Fisher scores for fistulas vs. sphincters obtained from T2WI-Fusion (FFusion-fistula = 6.56) were significantly higher than those from T2WI (FT2WI-fistula = 3.35) (p = 0.001). Mean Fisher scores for sphincters vs. fat from T2WI-Fusion (FFusion-sphincter = 10.84) were significantly higher than those from T2WI-FS (FSFS-sphincter = 2.57) (p = 0.001). In human assessment, T2WI-Fusion showed the same fistula discriminability as T2WI-FS, and better sphincter discriminability than T2WI. Overall, T2WI-Fusion showed better discriminability than T2WI, T2WI-FS, and enhanced 3D-VIBE images. CONCLUSION: T2WI and T2WI-FS fusion technology improves signal differences between anal fistulas and surrounding structures, and may facilitate better evaluation of anal fistulas and sphincters.

Prediction of sorafenib treatment-related gene expression for hepatocellular carcinoma: preoperative MRI and histopathological correlation.

PURPOSE: To investigate the feasibility of prediction for targeted therapy-related gene expression in hepatocellular carcinoma (HCC) using preoperative gadoxetic acid-enhanced magnetic resonance imaging (MRI). MATERIALS AND METHODS: Ninety-one patients (81 men, mean age 53.9 ± 12 years) with solitary HCC who underwent preoperative enhanced MRI were retrospectively analyzed. Features including tumor size, signal homogeneity, tumor capsule, tumor margin, intratumoral vessels, peritumor enhancement, peritumor hypointensity, signal intensity ratio on DWI, T1 relaxation times, and the reduction rate between pre- and post-contrast enhancement images were assessed. The operation and histopathological evaluation were performed within 2 weeks after MRI examination (mean time 7 days). The expression levels of BRAF, RAF1, VEGFR2, and VEGFR3 were evaluated. The associations between these imaging features and gene expression levels were investigated. RESULTS: Tumor incomplete capsules or non-capsules (p = 0.001) and intratumoral vessels (p = 0.002) were significantly associated with BRAF expression, and tumor incomplete capsules or non-capsules (p = 0.001) and intratumoral vessels (p = 0.013) with RAF1 expression. There was no significant association between the expression of VEGFR2, VEGFR3, and all examined MRI features. Multivariate logistic regression showed that incomplete tumor capsule (p = 0.002) and non-capsule (p = 0.004) were independent risk factors of HCC with high BRAF expression; incomplete tumor capsule (p < 0.001) and non-capsule (p = 0.040) were independent risk factors of HCC with high RAF1 expression. CONCLUSION: The presence of incomplete capsule or intratumoral vessels and the absence of capsule are potential indicators of high BRAF and RAF1 expression. Gadoxetic acid-enhanced MRI may facilitate the choice of gene therapy for patients with HCC. KEY POINTS: • Incomplete tumor capsule and non-capsule were independent risk factors of HCC with high BRAF and RAF1 expression. • The presence of intratumoral vessels was a potential indicator of high BRAF and RAF1 expression. • Gadoxetic acid-enhanced MRI may be a predictor of efficacy of treatment with sorafenib.

[Changes in diameter of superior mesenteric vein and gastrocolic trunk in patients with cecum-ascending colon cancer].

OBJECTIVE: To compare the difference of the diameters of superior mesenteric vein (SMV) and gastrocolic trunk (GCT) between patients with cecum-ascending colon cancer and normal individuals, and to assess the diagnostic value of the diameters of SMV and GCT in cecum-ascending colon cancer. METHODS: Preoperative imaging data of 60 patients with primary cecum-ascending colon cancer confirmed by postoperative pathology at the First Affiliated Hospital of Sun Yat-sen University from June 2014 to December 2016 were retrospectively analyzed. The diameters of SMV and GCT were measured on preoperative CT images. SMV was measured at about 2 cm below the junction of SMV and splenic vein. GCT was measured at 1 cm near the proximal junction of right colon vein, right gastroepiploic vein and anterior pancreaticoduodenal vein. Another 60 people receiving pelvic CT examination without organ illness were collected as control. The diameter differences of SMV and GCT between cancer group and control group were compared. The diagnostic value of the diameters of SMV and GCT in cecum-ascending colon cancer was evaluated by receiver operating characteristic (ROC) curves. RESULTS: Among 60 cases of cecum-ascending colon cancer, 36 were males and 24 were females with median age of 48 years (range 28-84); 13 were cecum cancer, 47 were ascending colon cancer; 11 had no lymph node and liver metastasis, 40 had lymph node metastasis, 9 had liver metastasis (all with lymph node metastasis). Compared to control group, the diameters of SMV and GCT in cancer group were significantly longer [SMV:(11.2±1.3) mm vs. (9.5±1.7) mm, t=6.04, P<0.001; GCT:(5.5±0.9) mm vs. (3.5±1.0) mm, t=11.51, P<0.001]. However, there were no statistically significant differences in diameters of SMV and GCT among hepatic metastasis, lymph node metastasis and no metastasis cancer groups (all P>0.05). The ROC curve analysis showed that the area under the curve of SMV diameter was 0.777, and the optimal cut-off point was 10.5 mm in the diagnosis of cecum-ascending colon cancer, with the sensitivity and specificity of 95.0%(57/60) and 46.7%(28/60) respectively. The area under the curve of GCT diameter was 0.923, and the optimal cut-off point was 4.5 mm in the diagnosis of cecum-ascending colon cancer, with sensitivity and specificity of 88.3%(53/60) and 85.0%(51/60) respectively. CONCLUSION: The dilation of the SMV and GCT may be used as warning factors for cecum-ascending colon cancer, especially the diameter of GCT.

CT evaluation of response in advanced gastroenteropancreatic neuroendocrine tumors treated with long-acting-repeatable octreotide: what is the optimal size variation threshold?

OBJECTIVE: To identify a reliable early indicator of deriving progression-free survival (PFS) benefit in patients with advanced gastroenteropancreatic neuroendocrine tumors (GEP-NETs) treated with octreotide long-acting repeatable (LAR). METHODS: We investigated the images of 50 patients with well-differentiated advanced GEP-NETs treated with LAR octreotide and underwent baseline and follow-up thoracic, abdominal, and pelvic computed tomography. Receiver-operating characteristic (ROC) analysis and the Kaplan-Meier method were used to identify the optimal threshold to distinguish between those with and without significant improvement of PFS. RESULTS: The optimal threshold for determining a response to octreotide LAR was -10% ΔSLD, with a sensitivity and specificity of 85.7% and 80%, respectively. At this threshold, 19 patients were responders and 31 were non-responders; the median PFS was 20.2 and 7.6 months in responders and non-responders (hazard ratio, 2.66; 95% confidence interval, 1.32-5.36). CONCLUSION: A 10% shrinkage in tumor size is an optimal early predictor of response to octreotide LAR in advanced GEP-NETs. KEY POINTS: • Octreotide LAR can significantly prolong PFS among patients with well-differentiated advanced GEP-NETs. • No optimal tumor size-based response criteria are reported in GEP-NETs with octreotide. • Ten percent tumor shrinkage is a reliable indicator of the response to octreotide for advanced GEP-NETs.

Prediction of Microvascular Invasion in Hepatocellular Carcinoma: Preoperative Gd-EOB-DTPA-Dynamic Enhanced MRI and Histopathological Correlation.

Objective: To investigate the imaging features observed in preoperative Gd-EOB-DTPA-dynamic enhanced MRI and correlated with the presence of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) patients. Methods: 66 HCCs in 60 patients with preoperative Gd-EOB-DTPA-dynamic enhanced MRI were retrospectively analyzed. Features including tumor size, signal homogeneity, tumor capsule, tumor margin, peritumor enhancement during mid-arterial phase, peritumor hypointensity during hepatobiliary phase, signal intensity ratio on DWI and apparent diffusion coefficients (ADC), T1 relaxation times, and the reduction rate between pre- and postcontrast enhancement images were assessed. Correlation between these features and histopathological presence of MVI was analyzed to establish a prediction model. Results: Histopathology confirmed that MVI were observed in 17 of 66 HCCs. Univariate analysis showed tumor size (p = 0.003), margin (p = 0.013), peritumor enhancement (p = 0.001), and hypointensity during hepatobiliary phase (p = 0.004) were associated with MVI. A multiple logistic regression model was established, which showed tumor size, margin, and peritumor enhancement were combined predictors for the presence of MVI (α = 0.1). R2 of this prediction model was 0.353, and the sensitivity and specificity were 52.9% and 93.0%, respectively. Conclusion: Large tumor size, irregular tumor margin, and peritumor enhancement in preoperative Gd-EOB-DTPA-dynamic enhanced MRI can predict the presence of MVI in HCC.

Diffusion-Weighted and Magnetization Transfer Imaging in Testicular Spermatogenic Function Evaluation: Preliminary Results.

PURPOSE: To assess the use of functional magnetic resonance imaging (MRI), including diffusion-weighted MRI (DWI) and magnetization transfer MRI (MTI) in evaluating male infertility. MATERIALS AND METHODS: Sixteen men with testicular spermatogenesis hypofunction confirmed by percutaneous testis biopsy and 31 volunteers (control group B, age range: 20-40 years) with normal semen analysis including younger (By, n = 15, age range: 20-30 years) and older (Bo, n = 16, age range: 31-40 years) men underwent pelvic 3T MRI, including DWI and MTI. Apparent diffusion coefficient (ADC) and magnetization transfer ratio (MTR) were compared. RESULTS: The ADCs in 32 testes of 16 patients (0.497 ± 0.037 × 10-3 mm2 /s) were significantly (P < 0.001) higher than that of control group B (0.460 ± 0.031 × 10-3 mm2 /s), group By (0.453 ± 0.018 × 10-3 mm2 /s), and group Bo (0.461 ± 0.034 × 10-3 mm2 /s), whereas the MTRs were significantly lower than that of group B (16.14 ± 4.20), group By (17.88 ± 2.00), and group Bo (15.09 ± 4.28) (P < 0.001). CONCLUSION: Functional MRI, including DWI and MTI, appears promising for evaluating male infertility with higher ADC and lower MTR in testicular spermatogenesis hypofunction. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:186-190.