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.

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.

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.

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.

Diagnostic value of renal perfusion in patients with chronic kidney disease using 3D arterial spin labeling.

PURPOSE: To evaluate the diagnostic value of 3D arterial spin labeling (ASL) for noninvasive quantification of renal blood flow (RBF) in patients with chronic kidney disease (CKD). MATERIALS AND METHODS: CKD patients (n = 27) and healthy volunteers (n = 36) underwent renal 3T ASL magnetic resonance imaging, with inversion times from 1200 to 2000 msec for volunteers in the preliminary test, and 1800 to 2000 msec for volunteers and CKD patients in the formal experiments. The cortical RBFs were compared, and a correlation between RBF and estimated glomerular filtration rate (eGFR) was evaluated. RESULTS: For healthy volunteers, RBF values increased with TIs from 1200 to 1600 msec, but were almost constant at TIs from 1600 to 2000 msec. The cortical RBF values of CKD patients were lower than that of healthy volunteers at TIs from 1800 to 2000 msec. In addition, the CKD patients had lower cortical RBF values than the healthy volunteers (P < 0.01 for both), and their RBF values positively correlated with eGFR. CONCLUSION: 3D ASL is a potential noninvasive method for measuring renal perfusion that can provide valuable information for clinical CKD diagnosis. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 3 J. MAGN. RESON. IMAGING 2017;46:589-594.

Dopamine release from transplanted neural stem cells in Parkinsonian rat striatum in vivo.

Embryonic stem cell-based therapies exhibit great potential for the treatment of Parkinson's disease (PD) because they can significantly rescue PD-like behaviors. However, whether the transplanted cells themselves release dopamine in vivo remains elusive. We and others have recently induced human embryonic stem cells into primitive neural stem cells (pNSCs) that are self-renewable for massive/transplantable production and can efficiently differentiate into dopamine-like neurons (pNSC-DAn) in culture. Here, we showed that after the striatal transplantation of pNSC-DAn, (i) pNSC-DAn retained tyrosine hydroxylase expression and reduced PD-like asymmetric rotation; (ii) depolarization-evoked dopamine release and reuptake were significantly rescued in the striatum both in vitro (brain slices) and in vivo, as determined jointly by microdialysis-based HPLC and electrochemical carbon fiber electrodes; and (iii) the rescued dopamine was released directly from the grafted pNSC-DAn (and not from injured original cells). Thus, pNSC-DAn grafts release and reuptake dopamine in the striatum in vivo and alleviate PD symptoms in rats, providing proof-of-concept for human clinical translation.

Assessment of renal allograft function early after transplantation with isotropic resolution diffusion tensor imaging.

OBJECTIVES: To investigate the value of diffusion tensor imaging (DTI) and tractography in renal allografts at the early stage after kidney transplantation. METHODS: This study was approved by the institutional ethical review committee, and written informed consent was obtained. A total of 54 renal allograft recipients 2-3 weeks after transplantation and 26 age-matched healthy volunteers underwent renal DTI with a 3.0-T magnetic resonance imaging (MRI) system. Recipients were divided into three groups according to the estimated glomerular filtration rate (eGFR). Apparent diffusion coefficient (ADC) and fractional anisotropy (FA) of the cortex and medulla were measured and compared among the groups. Whole-kidney tractography was performed. Correlation of eGFR with diffusion parameters was evaluated. RESULTS: In allografts with stable function, the medullary ADC was higher and the cortical FA was lower (p < 0.001) than in healthy kidneys. The cortical ADC, medullary ADC and FA decreased as the allograft function declined, and with a positive correlation with eGFR (p < 0.001); cortical FA did not. Tractography demonstrated a decrease of tract density in impaired functional allografts. CONCLUSIONS: Renal DTI produces reliable results to assess renal allograft function at the early stage after transplantation. KEY POINTS: • DTI and tractography can evaluate renal allograft function at an early stage • Medullary FA, cortical and medullary ADC can effectively evaluate allograft function • Medullary FA, cortical and medullary ADC are correlated with eGFR in renal allografts • Medullary ADC increased and cortical FA decreased in stable allografts compared to control subjects • Medullary FA, cortical and medullary ADC decreased and allograft function declined.

[Chemical Exchange Saturation Transfer Imaging of Creatine Metabolites: a 3.0 T MRI Pilot].

OBJECTIVE: To determine the feasibility of using chemical exchange saturation transfer (CEST) imaging to measure creatine (Cr) metabolites with 3.0 T MR. METHODS: Phantoms containing different concentrations of Cr under various pH conditions were studied with CEST sequence on 3.0 T MR imaging. CEST effect and Z spectra were analyzed. RESULTS: Cr exhibited significant CEST effect (± 1.8 ppm, F = 99.08, P < 0.001) on 3.0 T MR imaging, and positive correlation was found between the signal intensity and concentration of Cr (r = 0.963, P < 0.001). The CEST effect showed pH dependency of Cr (r = 0.41, P = 0.035). CONCLUSION: Creatine CEST imaging can be performed on 3.0 T MR imaging. Creatine concentrations and pH influence CEST effect.

Striatal dopamine release in a schizophrenia mouse model measured by electrochemical amperometry in vivo.

Schizophrenia is a severely devastating mental disorder, the pathological process of which is proposed to be associated with the dysfunction of dopaminergic transmission. Our previous results have demonstrated slower kinetics of transmitter release (glutamate release in hippocampus and norepinephrine release in adrenal slice) in a schizophrenia model, dysbindin null-sandy mice. However, whether dopaminergic transmission in the nigrostriatal pathway contributes to the pathology of dysbindin-/- mice remains unknown. Here, we have provided a step-by-step protocol to be applied in the in vivo amperometric recording of dopamine (DA) release from the mouse striatum evoked by an action potential (AP) pattern. With this protocol, AP pattern-dependent DA release was recorded from dysbindin-/- mice striatum in vivo. On combining amperometric recording in slices and electrophysiology, we found that in dysbindin-/- mice, (1) presynaptically, AP-pattern dependent dopamine overflow and uptake were intact in vivo; (2) the recycling of the dopamine vesicle pool remained unchanged. (3) Postsynaptically, the excitability of medium spiny neuron (MSN) was also normal, as revealed by patch-clamp recordings in striatal slices. Taken together, in contrast to reduced norepinephrine release in adrenal chromaffin cells, the dopaminergic transmission remains unchanged in the nigrostriatal pathway in dysbindin-/- mice, providing a new insight into the functions of the schizophrenia susceptibility gene dysbindin.

Liver transplantation nearly normalizes brain spontaneous activity and cognitive function at 1 month: a resting-state functional MRI study.

To investigate the short-term brain activity changes in cirrhotic patients with Liver transplantation (LT) using resting-state functional MRI (fMRI) with regional homogeneity (ReHo) method. Twenty-six cirrhotic patients as transplant candidates and 26 healthy controls were included in this study. The assessment was repeated for a sub-group of 12 patients 1 month after LT. ReHo values were calculated to evaluate spontaneous brain activity and whole brain voxel-wise analysis was carried to detect differences between groups. Correlation analyses were performed to explore the relationship between the change of ReHo with the change of clinical indexes pre- and post-LT. Compared to pre-LT, ReHo values increased in the bilateral inferior frontal gyrus (IFG), right inferior parietal lobule (IPL), right supplementary motor area (SMA), right STG and left middle frontal gyrus (MFG) in patients post-LT. Compared to controls, ReHo values of post-LT patients decreased in the right precuneus, right SMA and increased in bilateral temporal pole, left caudate, left MFG, and right STG. The changes of ReHo in the right SMA, STG and IFG were correlated with change of digit symbol test (DST) scores (P < 0.05 uncorrected). This study found that, at 1 month after LT, spontaneous brain activity of most brain regions with decreased ReHo in pre-LT was substantially improved and nearly normalized, while spontaneous brain activity of some brain regions with increased ReHo in pre-LT continuously increased. ReHo may provide information on the neural mechanisms of LT' effects on brain function.

Temporal components of cholinergic terminal to dopaminergic terminal transmission in dorsal striatum slices of mice.

Striatal dopamine (DA) is critically involved in major brain functions such as motor control and deficits such as Parkinson's disease. DA is released following stimulation by two pathways: the nigrostriatal pathway and the cholinergic interneuron (ChI) pathway. The timing of synaptic transmission is critical in striatal circuits, because millisecond latency changes can reverse synaptic plasticity from long-term potentiation to long-term depression in a DA-dependent manner. Here, we determined the temporal components of ChI-driven DA release in striatal slices from optogenetic ChAT-ChR2-EYFP mice. After a light stimulus at room temperature, ChIs fired an action potential with a delay of 2.8 ms. The subsequent DA release mediated by nicotinic acetylcholine (ACh) receptors had a total latency of 17.8 ms, comprising 7.0 ms for cholinergic transmission and 10.8 ms for the downstream terminal DA release. Similar latencies of DA release were also found in striatal slices from wild-type mice. The latency of ChI-driven DA release was regulated by inhibiting the presynaptic vesicular ACh release. Moreover, we describe the time course of recovery of DA release via the two pathways and that of vesicle replenishment in DA terminals. Our work provides an example of unravelling the temporal building blocks during fundamental synaptic terminal-terminal transmission in motor regulation.

[Quantitative T2 mapping evaluates the repaired articular cartilage].

OBJECTIVE: To evaluate the value of T2 mapping in monitoring the repaired cartilage after matrix-associated autologous chondrocyte implantation/transplantation (MACI/MACT). METHODS: Four patients (10 plug cartilages) were examined three times by T2 mapping at 1, 3, and 6 months using a 3.0 Tesla MR scan system. Quantitative mean (full-thickness) T2 values were calculated in the transplanted area and control cartilage. Paired t-tests were used to compare the T2 values between transplanted and control cartilage. For analysis of longitudinal T2 values, one-way analyses of variance were performed among 1, 3, and 6 months after MACI. RESULTS: The mean T2 values of the transplanted area at 1, 3, and 6 months after MACI were (82.40±15.23), (71.09±13.06), (53.80±4.86) ms, respectively. There were significant differences between the transplanted and control cartilage at 1 and 3 months (both P<0.01) after MACI, but not at 6 months (P=0.196). There were significant differences among T2 values of 1, 3, and 6 months after MACI in transplanted area (P=0.03). CONCLUSION: T2 mapping provides a useful tool for monitoring the biochemical development of the transplanted cartilage and can be used to evaluate the cartilage repair noninvasively.

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.

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.

Action potential bursts enhance transmitter release at a giant central synapse.

Patterns of action potentials (APs), often in the form of bursts, are critical for coding and processing information in the brain. However, how AP bursts modulate secretion at synapses remains elusive. Here, using the calyx of Held synapse as a model we compared synaptic release evoked by AP patterns with a different number of bursts while the total number of APs and frequency were fixed. The ratio of total release produced by multiple bursts to that by a single burst was defined as 'burst-effect'.We found that four bursts of 25 stimuli at 100 Hz increased the totalcharge of EPSCs to 1.47 ± 0.04 times that by a single burst of 100 stimuli at the same frequency.Blocking AMPA receptor desensitization and saturation did not alter the burst-effect, indicating that it was mainly determined by presynaptic mechanisms. Simultaneous dual recordings of presynaptic membrane capacitance (Cm) and EPSCs revealed a similar burst-effect, being 1.58±0.13by Cm and 1.49±0.05 by EPSCs. Reducing presynapticCa2+ influx by lowering extracellular Ca2+concentration or buffering residual intracellular Ca2+ with EGTA inhibited the burst-effect. We further developed a computational model largely recapitulating the burst-effect and demonstrated that this effect is highly sensitive to dynamic change in availability of the releasable pool of synaptic vesicles during various patterns of activities. Taken together, we conclude that AP bursts modulate synaptic output mainly through intricate interaction between depletion and replenishment of the large releasable pool. This burst-effect differs from the somatic burst-effect previously described from adrenal chromaffin cells, which substantially depends on activity-induced accumulation of Ca2+ to facilitate release of a limited number of vesicles in the releasable pool. Hence, AP bursts may play an important role in dynamically regulating synaptic strength and fidelity during intense neuronal activity at central synapses.

Role of vesicle pools in action potential pattern-dependent dopamine overflow in rat striatum in vivo.

Action potential (AP) patterns and dopamine (DA) release are known to correlate with rewarding behaviors, but how codes of AP bursts translate into DA release in vivo remains elusive. Here, a given AP pattern was defined by four codes, termed total AP number, frequency, number of AP bursts, and interburst time [N, f, b, i].. The 'burst effect' was calculated by the ratio (γ) of DA overflow by multiple bursts to that of a single burst when total AP number was fixed. By stimulating the medial forebrain bundle using AP codes at either physiological (20 Hz) or supraphysiological (80 Hz) frequencies, we found that DA was released from two kinetically distinct vesicle pools, the fast-releasable pool (FRP) and prolonged-releasable pool (PRP), in striatal dopaminergic terminals in vivo. We examined the effects of vesicle pools on AP-pattern dependent DA overflow and found, with given 'burst codes' [b=8, i=0.5 s], a large total AP number [N = 768, f = 80 Hz] produced a facilitating burst-effect (γ[b8/b1] = 126 ± 3%), while a small total AP number [N=96, 80 Hz] triggered a depressing-burst-effect (γ[b8/b1] = 29 ± 4%). Furthermore, we found that the PRP (but not the FRP) predominantly contributed to the facilitating-burst-effect and the FRP played an important role in the depressing-burst effect. Thus, our results suggest that striatal DA release captures pre-synaptic AP pattern information through different releasable pools.

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.

[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.