Radiomic analysis will add differential diagnostic value of benign and malignant pulmonary nodules: a hybrid imaging study based on [18F]FDG and [18F]FLT PET/CT.

PURPOSE: To investigate the clinical value of radiomic analysis on [18F]FDG and [18F]FLT PET on the differentiation of [18F]FDG-avid benign and malignant pulmonary nodules (PNs). METHODS: Data of 113 patients with inconclusive PNs based on preoperative [18F]FDG PET/CT who underwent additional [18F]FLT PET/CT scans within a week were retrospectively analyzed in the present study. Three methods of analysis including visual analysis, radiomic analysis based on [18F]FDG PET/CT images alone, and radiomic analysis based on dual-tracer PET/CT images were evaluated for differential diagnostic value of benign and malignant PNs. RESULTS: A total of 678 radiomic features were extracted from volumes of interest (VOIs) of 123 PNs. Fourteen valuable features were thereafter selected. Based on a visual analysis of [18F]FDG PET/CT images, the diagnostic accuracy, sensitivity, and specificity were 61.6%, 90%, and 28.8%, respectively. For the test set, the area under the curve (AUC), sensitivity, and specificity of the radiomic models based on [18F]FDG PET/CT plus [18F]FLT signature were equal or better than radiomics based on [18F]FDG PET/CT only (0.838 vs 0.810, 0.778 vs 0.778, 0.750 vs 0.688, respectively). CONCLUSION: Radiomic analysis based on dual-tracer PET/CT images is clinically promising and feasible for the differentiation between benign and malignant PNs. CLINICAL RELEVANCE STATEMENT: Radiomic analysis will add differential diagnostic value of benign and malignant pulmonary nodules: a hybrid imaging study based on [18F]FDG and [18F]FLT PET/CT. KEY POINTS: • Radiomics brings new insights into the differentiation of benign and malignant pulmonary nodules beyond the naked eyes. • Dual-tracer imaging shows the biological behaviors of cancerous cells from different aspects. • Radiomics helps us get to the histological view in a non-invasive approach.

A Computed Tomography Radiomics Nomogram in Differentiating Chordoma From Giant Cell Tumor in the Axial Skeleton.

OBJECTIVE: The aim of the study is to develop and validate a computed tomography (CT) radiomics nomogram for preoperatively differentiating chordoma from giant cell tumor (GCT) in the axial skeleton. METHODS: Seventy-three chordomas and 38 GCTs in axial skeleton were retrospectively included and were divided into a training cohort (n = 63) and a test cohort (n = 48). The radiomics features were extracted from CT images. A radiomics signature was developed by using the least absolute shrinkage and selection operator model, and a radiomics score (Rad-score) was acquired. By combining the Rad-score with independent clinical risk factors using multivariate logistic regression model, a radiomics nomogram was established. Calibration and receiver operator characteristic curves were used to assess the performance of the nomogram. RESULTS: Five features were selected to construct the radiomics signature. The radiomics signature showed favorable discrimination in the training cohort (area under the curve [AUC], 0.860; 95% confidence interval [CI], 0.760-0.960) and the test cohort (AUC, 0.830; 95% CI, 0.710-0.950). Age and location were the independent clinical factors. The radiomics nomogram combining the Rad-score with independent clinical factors showed good discrimination capability in the training cohort (AUC, 0.930; 95% CI, 0.880-0.990) and the test cohort (AUC, 0.980; 95% CI, 0.940-1.000) and outperformed the radiomics signature ( z = 2.768, P = 0.006) in the test cohort. CONCLUSIONS: The CT radiomics nomogram shows good predictive efficacy in differentiating chordoma from GCT in the axial skeleton, which might facilitate clinical decision making.

Prognostic Value of the Radiomics-Based Model in the Disease-Free Survival of Pretreatment Uveal Melanoma: An Initial Result.

OBJECTIVE: The aim of this study was to develop a pretreatment magnetic resonance imaging (MRI)-based radiomics model for disease-free survival (DFS) prediction in patients with uveal melanoma (UM). METHODS: We randomly assigned 85 patients with UM into 2 cohorts: training (n = 60) and validation (n = 25). The radiomics model was built from significant features that were selected from the training cohort by applying a least absolute shrinkage and selection operator to pretreatment MRI scans. Least absolute shrinkage and selection operator regression and the Cox proportional hazard model were used to construct a radiomics score (rad-score). Patients were divided into a low- or a high-risk group based on the median of the rad-score. The Kaplan-Meier analysis was used to evaluate the association between the rad-score and DFS. A nomogram incorporating the rad-score and MRI features was plotted to individually estimate DFS. The model's discrimination power was assessed using the concordance index. RESULTS: The radiomics model with 15 optimal radiomics features based on MRI performed well in stratifying patients into the high- or a low-risk group of DFS in both the training and validation cohorts (log-rank test, P = 0.009 and P = 0.02, respectively). Age, basal diameter, and height were selected as significant clinical and MRI features. The nomogram showed good predictive performance with concordance indices of 0.741 (95% confidence interval, 0.637-0.845) and 0.912 (95% confidence interval, 0.847-0.977) in the training and validation cohorts, respectively. Calibration curves demonstrated good agreement. CONCLUSION: The developed clinical-radiomics model may be a powerful predictor of the DFS of patients with UM, thereby providing evidence for preoperative risk stratification.

The value of 18F-FDG PET/CT-based radiomics in predicting perineural invasion and outcome in non-metastatic colorectal cancer.

PURPOSE: Perineural invasion (PNI) has been recognized as an important prognosis factor in patients with colorectal cancer (CRC). The purpose of this retrospective study was to investigate the value of 18F-FDG PET/CT-based radiomics integrating clinical information, PET/CT features, and metabolic parameters for preoperatively predicting PNI and outcome in non-metastatic CRC and establish an easy-to-use nomogram. METHODS: A total of 131 patients with non-metastatic CRC who undergo PET/CT scan were retrospectively enrolled. Univariate analysis was used to compare the differences between PNI-present and PNI-absent groups. Multivariate logistic regression was performed to select the independent predictors for PNI status. Akaike information criterion (AIC) was used to select the best prediction models for PNI status. CT radiomics signatures (RSs) and PET-RSs were selected by maximum relevance minimum redundancy (mRMR) and the least absolute shrinkage and selection operator algorithm (LASSO) regression and radiomics scores (Rad-scores) were calculated for each patient. The prediction models with or without Rad-score were established. According to the nomogram, nomogram scores (Nomo-scores) were calculated for each patient. The performance of different models was assessed with the area under the curve (AUC), specificity, and sensitivity. The clinical usefulness was assessed by decision curve (DCA). Multivariate Cox regression was used to selected independent predictors of progression-free survival (PFS). RESULTS: Among all the clinical information, PET/CT features, and metabolic parameters, CEA, lymph node metastatic on PET/CT (N stage), and total lesion glycolysis (TLG) were independent predictors for PNI (p < 0.05). Six CT-RSs and 12 PET-RSs were selected as the most valuable factors to predict PNI. The Rad-score calculated with these RSs was significantly different between PNI-present and PNI-absent groups (p < 0.001). The AUC of the constructed model was 0.90 (95%CI: 0.83-0.97) in the training cohort and 0.80 (95%CI: 0.65-0.95) in the test cohort. The nomogram's predicting sensitivity was 0.84 and the specificity was 0.83 in the training cohort. The clinical model's predicting sensitivity and specificity were 0.66 and 0.85 in the training cohort, respectively. Besides, DCA showed that patients with non-metastatic CRC could get more benefit with our model. The results also indicated that N stage, PNI status, and the Nomo-score were independent predictors of PFS in patients with non-metastatic CRC. CONCLUSION: The nomogram, integrating clinical data, PET/CT features, metabolic parameters, and radiomics, performs well in predicting PNI status and is associated with the outcome in patients with non-metastatic CRC.

Multiparametric MRI-based machine learning models for preoperatively predicting rectal adenoma with canceration.

OBJECTIVES: To propose multiparametric MRI-based machine learning models and assess their ability to preoperatively predict rectal adenoma with canceration. MATERIALS AND METHODS: A total of 53 patients with postoperative pathology confirming rectal adenoma (n = 29) and adenoma with canceration (n = 24) were enrolled in this retrospective study. All patients were divided into a training cohort (n = 42) and a test cohort (n = 11). All patients underwent preoperative pelvic MR examination, including high-resolution T2-weighted imaging (HR-T2WI) and diffusion-weighted imaging (DWI). A total of 1396 radiomics features were extracted from the HR-T2WI and DWI sequences, respectively. The least absolute shrinkage and selection operator (LASSO) was utilized for feature selection from the radiomics feature sets from the HR-T2WI and DWI sequences and from the combined feature set with 2792 radiomics features incorporating two sequences. Five-fold cross-validation and two machine learning algorithms (logistic regression, LR; support vector machine, SVM) were utilized for model construction in the training cohort. The diagnostic performance of the models was evaluated by sensitivity, specificity and area under the curve (AUC) and compared with the Delong's test. RESULTS: Ten, 8, and 25 optimal features were selected from 1396 HR-T2WI, 1396 DWI and 2792 combined features, respectively. Three group models were constructed using the selected features from HR-T2WI (ModelT2), DWI (ModelDWI) and the two sequences combined (Modelcombined). Modelcombined showed better prediction performance than ModelT2 and ModelDWI. In Modelcombined, there was no significant difference between the LR and SVM algorithms (p = 0.4795), with AUCs in the test cohort of 0.867 and 0.900, respectively. CONCLUSIONS: Multiparametric MRI-based machine learning models have the potential to predict rectal adenoma with canceration. Compared with ModelT2 and ModelDWI, Modelcombined showed the best performance. Moreover, both LR and SVM have equal excellent performance for model construction.

MRI radiomics-based nomogram for individualised prediction of synchronous distant metastasis in patients with clear cell renal cell carcinoma.

OBJECTIVE: To evaluate the performance of a multiparametric MRI radiomics-based nomogram for the individualised prediction of synchronous distant metastasis (SDM) in patients with clear cell renal cell carcinoma (ccRCC). METHODS: Two-hundred and one patients (training cohort: n = 126; internal validation cohort: n = 39; external validation cohort: n = 36) with ccRCC were retrospectively enrolled between January 2013 and June 2019. In the training cohort, the optimal MRI radiomics features were selected and combined to calculate the radiomics score (Rad-score). Incorporating Rad-score and SDM-related clinicoradiologic characteristics, the radiomics-based nomogram was established by multivariable logistic regression analysis, then the performance of the nomogram (discrimination and clinical usefulness) was evaluated and validated subsequently. Moreover, the prediction efficacy for SDM in ccRCC subgroups of different sizes was also assessed. RESULTS: Incorporating Rad-score derived from 9 optimal MR radiomics features (age, pseudocapsule and regional lymph node), the radiomics-based nomogram was capable of predicting SDM in the training cohort (area under the ROC curve (AUC) = 0.914) and validated in both the internal and external cohorts (AUC = 0.854 and 0.816, respectively) and also showed a convincing predictive power in ccRCC subgroups of different sizes (≤ 4 cm, AUC = 0.875; 4-7 cm, AUC = 0.891; 7-10 cm, 0.908; > 10 cm, AUC = 0.881). Decision curve analysis indicated that the radiomics-based nomogram is of clinical usefulness. CONCLUSIONS: The multiparametric MRI radiomics-based nomogram could achieve precise individualised prediction of SDM in patients with ccRCC, potentially improving the management of ccRCC. KEY POINTS: • Radiomics features derived from multiparametric magnetic resonance images showed relevant association with synchronous distant metastasis in clear cell renal cell carcinoma. • MRI radiomics-based nomogram may serve as a potential tool for the risk prediction of synchronous distant metastasis in clear cell renal cell carcinoma.

Quantitative measurement of cartilage volume with automatic cartilage segmentation in knee osteoarthritis.

PURPOSE: To determine the reproducibility of the automatic cartilage segmentation method using a prototype KneeCaP software (version 1.3; Siemens Healthcare, Erlangen, Germany) and to compare the difference in cartilage volume (CV) between the normal knee joint and knee osteoarthritis (KOA) of different degrees by using the above software. MATERIALS AND METHODS: The study included 62 subjects with knee OA and 29 healthy control subjects. The cartilage lesion patients were divided into a mild-to-moderate OA group (n = 29) and severe OA group (n = 33). Automatic cartilage segmentation was performed on all the subjects, and among them, 19 knee cases were randomly selected to also do the manual cartilage segmentation. Statistical significance was determined with one-way analysis of variance (ANOVA), intraclass correlation coefficient (ICC), and Pearson correlation coefficient. Automatic segmentation was compared with the manual one. The relative cartilage volume percentages of the femur, tibia, and patella in the normal control/mild-to-moderate/severe OA groups were assessed. RESULTS: Comparing the cartilage volumes derived by manual and automatic segmentation, the ICC value for the knee joint, patella, femur, or tibia was 0.784, 0.815, 0.740, and 0.797. The relative cartilage volume percentages of the femur, tibia, and patella in the normal control/mild-to-moderate/severe OA groups were 57.28%/59.30%/62.45% (femur), 25.35%/23.46%/21.84% (tibia), and 17.37%/17.24%/15.71% (patella), respectively. Compared with the normal control group, the relative tibia cartilage volume percentage was lower in the mild-to-moderate OA group and the severe OA group. Corresponding index showed a similar difference between the mild-to-moderate OA group and the severe OA group (p < 0.001). CONCLUSION: This study demonstrated that the relative cartilage volume percentage is correlated with the semi-quantitative systems and may be a preferred outcome measure in clinical studies of OA. Automatic cartilage segmentation using KneeCaP delivered reliable results on high-spatial-resolution 3 T MR images for the healthy, mild-moderate OA patients. Key Points • The cartilage automatic segmentation has excellent reproducibility and was not affected by inter-observer variation. • The relative cartilage volume percentage is correlated with the semi-quantitative systems and may be a preferred outcome measure in clinical studies of OA.

Value of MR-based radiomics in differentiating uveal melanoma from other intraocular masses in adults.

PURPOSE: To assess the performance of machine learning (ML)-based magnetic resonance imaging (MRI) radiomics analysis for discriminating between uveal melanoma (UM) and other intraocular masses. METHODS: This retrospective study analyzed 245 patients with intraocular masses (165 UMs and 80 other intraocular masses). Radiomics features were extracted from T1WI, T2WI, and contrast enhanced T1-weighted images (CET1WI), respectively. The intraclass correlation coefficient (ICC) was calculated to quantify the reproducibility of features. The training and test sets consisted of 195 and 50 cases. Least absolute shrinkage and selection operator (LASSO) regression method was employed for feature selection. The ML classifiers were logistic regression (LR), multilayer perceptron (MLP), and support vector machine (SVM). The performance of classifiers was evaluated by ROC analysis, and was compared to the performance of visual assessment by DeLong test. RESULTS: The optimal radiomics feature set was 10, 15, 15, and 24 for T1W, T2W, CET1W, and joint T2W and CET1W images, respectively. The accuracy of T1WI, T2WI, CET1WI, and the joint T2WI and CET1WI models ranged from 72.0 %-78.0 %, from 79.6 %-81.6 %, from 74.0 %-82.0 %, and from 76.0 %-86.0 % in the test set. In the test set, the AUC for T1WI, T2WI, CET1WI, joint T2WI, and CET1WI models ranged from 0.775 to 0.829, 0.816 to 0.826, 0.836 to 0.861, and 0.870 to 0.877, respectively. In the combined model, the performance of ML classifiers was better than the performance of visual assessment in the training set and in all patients (p＜0.05). CONCLUSIONS: Radiomics analysis represents a promising tool in separating UM from other intraocular masses.

[Radiomics models based on non-enhanced MRI can differentiate chondrosarcoma from enchondroma].

OBJECTIVE: To develop and validate radiomics models based on non-enhanced magnetic resonance (MR) imaging for differentiating chondrosarcoma from enchondroma. METHODS: We retrospectively evaluated a total of 68 patients (including 27 with chondrosarcoma and 41 with enchondroma), who were randomly divided into training group (n=46) and validation group (n=22). Radiomics features were extracted from T1WI and T2WI-FS sequences of the whole tumor by two radiologists independently and selected by Low Variance, Univariate feature selection, and least absolute shrinkage and selection operator (LASSO). Radiomics models were constructed by multivariate logistic regression analysis based on the features from T1WI and T2WI-FS sequences. The receiver-operating characteristics (ROC) curve and intraclass correlation coefficient (ICC) analyses of the radiomics models and conventional MR imaging were performed to determine their diagnostic accuracy. RESULTS: The ICC value for interreader agreement of the radiomics features ranged from 0.779 to 0.923, which indicated good agreement. Ten and 11 features were selected from the T1WI and T2WI-FS sequences to construct radiomics models, respectively. The areas under the curve (AUCs) of T1WI and T2WI-FS models were 0.990 and 0.925 in training group and 0.915 and 0.855 in the validation group, respectively, showing no significant differences between the two sequence-based models (P>0.05). In all the cases, the AUCs of the two radiomics models based on T1WI and T2WI-FS sequences and conventional MR imaging were 0.955, 0.901 and 0.569, respectively, demonstrating a significantly higher diagnostic accuracy of the two sequence-based radiomics models than conventional MR imaging (P<0.01). CONCLUSIONS: The radiomics models based on T1WI and T2WI-FS non-enhanced MR imaging can be used for the differentiation of chondrosarcoma from enchondroma.

Fully automatic segmentation of type B aortic dissection from CTA images enabled by deep learning.

PURPOSE: This study sought to establish a robust and fully automated Type B aortic dissection (TBAD) segmentation method by leveraging the emerging deep learning techniques. METHODS: Preoperative CTA images of 276 patients with TBAD were retrospectively collected from January 2011 to December 2018. Using a reproducible manual segmentation protocol of three labels (whole aorta, true lumen (TL), and false lumen (FL)), a ground truth database (n = 276) was established and randomly divided into training and testing sets in a rough 8:1 ratio. Three convolutional neural network (CNN) models were developed on the training set (n = 246): single one-task (CNN1), single multi-task (CNN2), and serial multi-task (CNN3) models. Performance was evaluated using the Dice coefficient score (DCS) and lumen volume accuracy on the testing set (n = 30). Pearson correlation, Intra-class correlation coefficients and Bland-Altman plots were used to evaluate the inter-observer measurement agreement. RESULTS: CNN3 performed the best, with mean DCSs of 0.93 ±â€¯0.01, 0.93 ±â€¯0.01 and 0.91 ±â€¯0.02 for the whole aorta, TL, and FL, respectively (p < 0.05). Each label volume from CNN3 showed excellent agreement with the ground truth, with mean volume differences of -31.05 (-82.76 to 20.65) ml, 4.79 (-11.04 to 20.63) ml, and 8.67(-11.40 to 28.74) ml for the whole aorta, TL, and FL, respectively. The segmentation speed of CNN3 was 0.038 ±â€¯0.006 s/image. CONCLUSION: Deep learning-based model provides a promising approach for accurate and efficient segmentation of TBAD and makes it possible for automated measurements of TBAD anatomical features.

Radiomics for diagnosis of dual-phenotype hepatocellular carcinoma using Gd-EOB-DTPA-enhanced MRI and patient prognosis.

PURPOSE: To describe the clinical characteristics and outcomes of patients with dual-phenotype hepatocellular carcinoma (DPHCC) and investigate the use of radiomics to establish an image-based signature for preoperative differential diagnosis. METHODS: This study included 50 patients with a postoperative pathological diagnosis of DPHCC (observation group) and 50 patients with CK7- and CK19-negative HCC (control group) who attended our hospital between January 2015 and December 2018. All patients underwent Gd-EOB-DTPA-enhanced MRI within 1 month before surgery. Arterial phase (AP), portal venous phase (PVP), delayed phase (DP) and hepatobiliary phase (HBP) images were transferred into a radiomics platform. Volumes of interest covered the whole tumor. The dimensionality of the radiomics features were reduced using LASSO. Four classifiers, including multi-layer perceptron (MLP), support vector machines (SVM), logistic regression (LR) and K-nearest neighbor (KNN) were used to distinguish DPHCC from CK7- and CK19-negative HCC. Kaplan-Meier survival analysis was used to assess 1-year disease-free survival (DFS) and overall survival (OS) in the observation and control groups. RESULTS: The best preoperative diagnostic power for DPHCC will likely be derived from a combination of different phases and classifiers. The sensitivity, specificity and accuracy of LR in PVP (0.740, 0.780, 0.766), DP (0.893, 0.700, 0.798), HBP (0.800, 0.720, 0.756) and MLP in PVP (0.880, 0.720, 0.798) were better performance. The 1-year DFS and OS of the patients in the observation group were 69% and 78%, respectively. The 1-year DFS and OS of the patients in the control group were 83% and 85%, respectively. Kaplan-Meier survival analysis showed no statistical difference in DFS and OS between groups (P = 0.231 and 0.326), but DFS and OS were numerically lower in patients with DPHCC. CONCLUSION: The radiomics features extracted from Gd-EOB-DTPA-enhanced MR images can be used to diagnose preoperative DPHCC. DPHCC is more likely to recur and cause death than HCC, suggesting that active postoperative management of patients with DPHCC is required.

CT-based radiomics features analysis for predicting the risk of anterior mediastinal lesions.

BACKGROUND: To retrospectively validate CT-based radiomics features for predicting the risk of anterior mediastinal lesions. METHODS: A retrospective study was performed through February 2013 to March 2018 on 298 patients who had pathologically confirmed anterior mediastinal lesions. The patients all underwent CT scans before their treatment, including 130 unenhanced computed tomography (UECT) and 168 contrast-enhanced CT (CECT) scans. The lesion areas were delineated, and a total of 1,029 radiomics features were extracted. The least absolute shrinkage and selection operator (Lasso) algorithm method was used to select the radiomics features significantly associated with discrimination of high-risk from low-risk lesions in the anterior mediastinum. Then, 8-fold and 3-fold cross-validation logistic regression (LR) models were taken as the feature selection classifiers to build the radiomics models for UECT and CECT scan respectively. The predictive performance of the radiomics features was evaluated based on the receiver operating characteristics (ROC) curve. RESULTS: Each of the two radiomics classifiers included the optimal 12 radiomic features. In terms of the area under ROC curve, using the radiomics model in discriminating high-risk lesions from the low-risks, CECT images accounted for 74.1% with a sensitivity of 66.67% and specificity of 64.81%. Meanwhile, UECT images were 84.2% with a sensitivity of 71.43% and specificity of 74.07%. CONCLUSIONS: The association of the two proposed CT-based radiomics features with the discrimination of high and low-risk lesions in anterior mediastinum was confirmed, and the radiomics features of the UECT scan were proven to have better prediction performance than the CECT's in risk grading.

Accelerated Time-of-Flight Magnetic Resonance Angiography with Sparse Undersampling and Iterative Reconstruction for the Evaluation of Intracranial Arteries.

OBJECTIVE: To compare the image quality of three-dimensional time-of-flight (TOF) magnetic resonance angiography (MRA) with sparse undersampling and iterative reconstruction (sparse TOF) with that of conventional TOF MRA. MATERIALS AND METHODS: This study included 56 patients who had undergone sparse TOF MRA for intracranial artery evaluation on a 3T MR scanner. Conventional TOF MRA scans were also acquired from 29 patients with matched acquisition times and another 27 patients with matched scanning parameters. The image quality was scored using a five-point scale based on the delineation of arterial vessel segments, artifacts, overall vessel visualization, and overall image quality by two radiologists independently, and the data were analyzed using the non-parametric Wilcoxon signed-rank test. Contrast ratios (CRs) of vessels were compared using the paired t test. Interobserver agreement was calculated using the kappa test. RESULTS: Compared with conventional TOF at the same spatial resolution, sparse TOF with an acceleration factor of 3.5 could reduce acquisition time by 40% and showed comparable image quality. In addition, when compared with conventional TOF with the same acquisition time, sparse TOF with an acceleration factor of 5 could also achieve higher spatial resolution, better delineation of vessel segments, fewer artifacts, higher image quality, and a higher CR (p < 0.05). Good-to-excellent interobserver agreement (κ: 0.65-1.00) was obtained between the two radiologists. CONCLUSION: Compared with conventional TOF, sparse TOF can achieve equivalent image quality in a reduced duration. Furthermore, using the same acquisition time, sparse TOF could improve the delineation of vessels and decrease image artifacts.

Multidelay multiparametric arterial spin labeling perfusion MRI and mild cognitive impairment in early stage Parkinson's disease.

Mild cognitive impairment (MCI), a well-defined nonmotor manifestation of Parkinson's disease (PD), greatly impairs functioning and quality of life. However, the contribution of cerebral perfusion, quantified by arterial spin labeling (ASL), to MCI in PD remains poorly understood. The selection of an optimal delay time is difficult for single-delay ASL, a problem which is avoided by multidelay ASL. This study uses a multidelay multiparametric ASL to investigate cerebral perfusion including cerebral blood flow (CBF) and arterial transit time (ATT) in early stage PD patients exhibiting MCI using a voxel-based brain analysis. Magnetic resonance imaging data were acquired on a 3.0 T system at rest in 39 early stage PD patients either with MCI (PD-MCI, N = 22) or with normal cognition (PD-N, N = 17), and 36 age- and gender-matched healthy controls (HCs). CBF and ATT were compared among the three groups with SPM using analysis of variance followed by post hoc analyses to define regional differences and examine their relationship to clinical data. PD-MCI showed prolonged ATT in right thalamus compared to both PD-N and HC, and in right supramarginal gyrus compared to HC. PD-N showed shorter ATT in left superior frontal cortex compared to HC. Prolonged ATT in right thalamus was negatively correlated with the category fluency test (p = .027, r = -0.495) in the PD-MCI group. This study shows that ATT may be a more sensitive marker than CBF for the MCI, and highlights the potential role of thalamus and inferior parietal region for MCI in early stage PD.

Lipo-prostaglandin E1 improves renal hypoxia evaluated by BOLD-MRI in patients with diabetic kidney disease.

OBJECTIVE: To evaluate the effect of lipo-PGE1 on renal hypoxia in patients with DKD by BOLD-MRI. MATERIALS AND METHODS: All patients were divided into DKD group and CKD-without-diabetes group. All patients received intravenous 10 µg lipo-PGE1 once daily for 14 days. BOLD-MRI was performed before and after lipo-PGE1 administration to acquire renal CR2* and MR2* values. RESULTS: Renal MR2* value in DKD group after lipo-PGE1 treatment were significantly decreased compared with the baseline. However, no significant differences in MR2* values were found in the CKD-without-diabetes group. CONCLUSIONS: Lipo-PGE1 was shown to improve kidney medullary oxygenation in patients with DKD.

Quantification of liver iron concentration using the apparent susceptibility of hepatic vessels.

BACKGROUND: The quantification of liver iron concentration (LIC) is important for the monitoring of the body iron level in patients with iron overload. Conventionally, LIC is quantified through R2 or R2* mapping using MRI. In this paper, we demonstrate an alternative approach for LIC quantification through measuring the apparent susceptibility of hepatic vessels using quantitative susceptibility mapping (QSM). METHODS: QSM was performed in the liver region with the iterative susceptibility weighted imaging and mapping (iSWIM) algorithm, using the geometry of the vessels extracted from magnitude images as constraints. The susceptibilities of liver tissue were estimated from the apparent susceptibility of the hepatic veins and then converted to LIC. The accuracy of the proposed method was first validated using simulations, and then confirmed using in vivo data collected on 8 healthy controls and 11 patients at 3T. The effects of data acquisition parameters were studied using simulations, and the LICs estimated using QSM were compared with those estimated using R2* mapping. RESULTS: Simulation results showed that the use of a 3D data acquisition protocol with higher image resolution led to improved accuracy in LIC quantification using QSM. Both simulations and in vivo data results demonstrated that the LICs estimated using the proposed QSM method agreed well with those estimated using R2* mapping. With the shortest echo time being 2.5ms in the multi-echo gradient echo sequence, simulations results showed that LIC up to 12.45 mg iron/g dry tissue can be quantified using the proposed QSM method. For the in vivo data, the highest LIC measured was 11.32 mg iron/g dry tissue. CONCLUSIONS: The proposed method offers a reliable and flexible way to quantify LIC and has the potential to extend the range of LIC that can be accurately measured using R2* and QSM.

Comparisons of slice-encoding metal artifact correction and view-angle tilting magnetic resonance imaging and traditional digital radiography in evaluating chronic hip pain after total hip arthroplasty.

PURPOSE: The aims of this study were (1) to compare the areas of metal-induced artifacts and definition of periprosthetic structures between patients scanned with the slice-encoding metal artifact correction and view-angle tilting (SEMAC-VAT) turbo-spin-echo (TSE) prototype and those scanned with the standard TSE magnetic resonance (MR) sequences and (2) to further clarify the superiority of the SEMAC-VAT MR imaging technique at detecting lesions in patients after total hip arthroplasty (THA), compared with digital radiography (DR). MATERIALS AND METHODS: A total of 38 consecutive patients who underwent THA were referred to MR imaging at our institution. All patients suffered from chronic hip pain postoperatively. Twenty-three patients of the 38 were examined with a 1.5-T MR scanner using a SEMAC-VAT TSE prototype and standard TSE sequence, and the remaining 15 patients were examined with the same 1.5-T MR scanner, but using the SEMAC-VAT TSE prototype only. The traditional DR imaging was also performed for all patients. Two radiologists then independently measured the area of metal-induced artifacts and evaluated the definition of both the acetabular and femoral zones based on a three-point scale. Finally, the positive findings of chronic hip pain after THA based on SEMAC-VAT TSE MR imaging and traditional DR imaging were compared and analysed. RESULTS: The areas of metal-induced artifacts were significantly smaller in the SEMAC-VAT TSE sequences than those in the standard TSE sequences for both the T1-weighted (p < 0.001) and T2-weighted (p < 0.001) turbo inversion recovery magnitude images. In addition, 28 patients showed a series of positive signs in the SEMAC-VAT images that were not observed in the traditional DR images. CONCLUSION: Compared with the standard TSE MR imaging, SEMAC-VAT MR imaging significantly reduces metal-induced artifacts and might successfully detect most positive signs missed in the traditional DR images. TRANSLATIONAL POTENTIAL OF THIS ARTICLE: The main objective of this research was to show that MR sequences from the SEMAC-VAT TSE prototype provide a significant advantage at detecting lesions in patients after THA because of the excellent soft-tissue resolution of the MR imaging. SEMAC-VAT MR can evaluate chronic hip pain after THA and determine the cause, which can help the clinician decide on whether a surgical revision is needed.

Synaptotagmin-11 is a critical mediator of parkin-linked neurotoxicity and Parkinson's disease-like pathology.

Loss-of-function mutations in Parkin are the most common causes of autosomal recessive Parkinson's disease (PD). Many putative substrates of parkin have been reported; their pathogenic roles, however, remain obscure due to poor characterization, particularly in vivo. Here, we show that synaptotagmin-11, encoded by a PD-risk gene SYT11, is a physiological substrate of parkin and plays critical roles in mediating parkin-linked neurotoxicity. Unilateral overexpression of full-length, but not C2B-truncated, synaptotagmin-11 in the substantia nigra pars compacta (SNpc) impairs ipsilateral striatal dopamine release, causes late-onset degeneration of dopaminergic neurons, and induces progressive contralateral motor abnormalities. Mechanistically, synaptotagmin-11 impairs vesicle pool replenishment and thus dopamine release by inhibiting endocytosis. Furthermore, parkin deficiency induces synaptotagmin-11 accumulation and PD-like neurotoxicity in mouse models, which is reversed by SYT11 knockdown in the SNpc or knockout of SYT11 restricted to dopaminergic neurons. Thus, PD-like neurotoxicity induced by parkin dysfunction requires synaptotagmin-11 accumulation in SNpc dopaminergic neurons.

Quantitative free-breathing dynamic contrast-enhanced MRI in hepatocellular carcinoma using gadoxetic acid: correlations with Ki67 proliferation status, histological grades, and microvascular density.

PURPOSE: To validate a free-breathing dynamic contrast-enhanced-MRI (DCE-MRI) in hepatocellular carcinoma (HCC) patients using gadoxetic acid, and to determine the relationship between DCE-MRI parameters and histological results. METHODS: Thirty-four HCC patients were included in this prospective study. Free-breathing DCE-MRI data was acquired preoperatively on a 3.0 Tesla scanner. Perfusion parameters (K trans, K ep, V e and the semi-quantitative parameter of initial area under the gadolinium concentration-time curve, iAUC) were calculated and compared with tumor enhancement at contrast-enhanced CT. The relationship between DCE-MRI parameters and Ki67 indices, histological grades and microvascular density (MVD) was determined by correlation analysis. Differences of perfusion parameters between different histopathological groups were compared. Receiver operation characteristic (ROC) analysis of discriminating high-grades (grade III and IV) from low-grades (grade I and II) HCC was performed for perfusion parameters. RESULTS: Significant relationship was found between DCE-MRI and CT results. The DCE-MRI derived K trans were significantly negatively correlated with Ki-67 indices (rho = - 0.408, P = 0.017) and the histological grades (rho = - 0.444, P = 0.009) of HCC, and K ep and V e were significantly related with tumor MVD (rho = - 0.405, P = 0.017 for K ep; and rho = 0.385, P = 0.024 for V e). K trans, K ep, and iAUC demonstrated moderate diagnostic performance (iAUC = 0.78, 0.77 and 0.80, respectively) for discriminating high-grades from low-grades HCC without significant differences. CONCLUSIONS: The DCE-MRI derived parameters demonstrated weak but significant correlations with tumor proliferation status, histological grades or microvascular density, respectively. This free-breathing DCE-MRI is technically feasible and offers a potential avenue toward non-invasive evaluation of HCC malignancy.