The application of machine learning on brain imaging features of different narcolepsy subtypes.

STUDY OBJECTIVES: Narcolepsy is a central hypersomnia disorder, and differential diagnoses between its subtypes can be difficult. Hence, we applied machine learning to analyze the positron emission tomography (PET) data of patients with type 1 or type 2 narcolepsy, and patients with type 1 narcolepsy and comorbid schizophrenia, to construct predictive models to facilitate the diagnosis. METHODS: This is a retrospective and prospective case-control study of adolescent and young adult patients with type 1 or type 2 narcolepsy, and type 1 narcolepsy and comorbid schizophrenia. All participants received 18-F-fluorodeoxy glucose PET, sleep studies, neurocognitive tests, sleep questionnaires, and human leukocyte antigen typing. The collected PET data were analyzed by feature selections and classification methods in machine learning to construct predictive models. RESULTS: A total of 314 participants with narcolepsy were enrolled; 204 had type 1 narcolepsy, 90 had type 2 narcolepsy, and 20 had type 1 narcolepsy and comorbid schizophrenia. We used three filter methods for feature selection followed by a comparative analysis of classification methods. To apply a small number of regions of interest (ROI) and high classification accuracy, the Naïve Bayes classifier with the Term Variance as feature selection achieved the goal with only three ROIs (left basal ganglia, left Heschl, and left striatum) and produced an accuracy of higher than 99%. CONCLUSIONS: The accuracy of our predictive model of PET data are promising and can aid clinicians in the diagnosis of narcolepsy subtypes. Future research with a larger sample size could further refine the predictive model of narcolepsy.

Identifying Lymph Nodes and Their Statuses from Pretreatment Computer Tomography Images of Patients with Head and Neck Cancer Using a Clinical-Data-Driven Deep Learning Algorithm.

BACKGROUND: Head and neck cancer is highly prevalent in Taiwan. Its treatment mainly relies on clinical staging, usually diagnosed from images. A major part of the diagnosis is whether lymph nodes are involved in the tumor. We present an algorithm for analyzing clinical images that integrates a deep learning model with image processing and attempt to analyze the features it uses to classify lymph nodes. METHODS: We retrospectively collected pretreatment computed tomography images and surgery pathological reports for 271 patients diagnosed with, and subsequently treated for, naïve oral cavity, oropharynx, hypopharynx, and larynx cancer between 2008 and 2018. We chose a 3D UNet model trained for semantic segmentation, which was evaluated for inference in a test dataset of 29 patients. RESULTS: We annotated 2527 lymph nodes. The detection rate of all lymph nodes was 80%, and Dice score was 0.71. The model has a better detection rate at larger lymph nodes. For those identified lymph nodes, we found a trend where the shorter the short axis, the more negative the lymph nodes. This is consistent with clinical observations. CONCLUSIONS: The model showed a convincible lymph node detection on clinical images. We will evaluate and further improve the model in collaboration with clinical physicians.

Machine learning-based monosaccharide profiling for tissue-specific classification of Wolfiporia extensa samples.

Machine learning (ML) has been used for many clinical decision-making processes and diagnostic procedures in bioinformatics applications. We examined eight algorithms, including linear discriminant analysis (LDA), logistic regression (LR), k-nearest neighbor (KNN), random forest (RF), gradient boosting machine (GBM), support vector machine (SVM), Naïve Bayes classifier (NB), and artificial neural network (ANN) models, to evaluate their classification and prediction capabilities for four tissue types in Wolfiporia extensa using their monosaccharide composition profiles. All 8 ML-based models were assessed as exemplary models with AUC exceeding 0.8. Five models, namely LDA, KNN, RF, GBM, and ANN, performed excellently in the four-tissue-type classification (AUC > 0.9). Additionally, all eight models were evaluated as good predictive models with AUC value > 0.8 in the three-tissue-type classification. Notably, all 8 ML-based methods outperformed the single linear discriminant analysis (LDA) plotting method. For large sample sizes, the ML-based methods perform better than traditional regression techniques and could potentially increase the accuracy in identifying tissue samples of W. extensa.

Primary Graviceptive System and Astasia: A Case Report and Literature Review.

Astasia refers to the inability to maintain upright posture during standing, despite having full motor strength. Impairment of the vestibulocerebellar pathway, graviceptive system, and cingulate motor area have been proposed to be related to astasia. However, the responsible neural pathways remain unclear. We hypothesize that there is a common neural network behind astasia. To test the hypothesis, we reviewed all reported cases with astasia, including ours, and focused on the correlation between anatomical destruction and symptom presentation. A total of 26, including ours, non-psychogenic astasia patients were identified in the English literature. Seventy-three percent of them were associated with other neurologic symptoms and sixty-two percent of reported lesions were on the right side. Contralateral lateropulsion was very common, followed by retropulsion, when describing astasia. Infarction (54%) was the most reported cause. The thalamus (65%) was the most reported location. Infarctions were the fastest to recover (mean: 10.6 days), while lesions at the brainstem needed a longer time (mean: 61.6 days). By combining the character of lateropulsion in astasia and the presentation of an interrupted graviceptive system, we concluded that the primary graviceptive system may be the common neural network behind astasia. Future studies on astasia should focus on the pathological changes in the perception of verticality in the visual world and the body.

Field test of quantum key distribution with high key creation efficiency.

Quantum key distribution (QKD) promises unconditional security for communication. However, the random choices of the measurement basis in QKD usually result in low key creation efficiency. This drawback is overcome in the differential-phase-shift QKD, provided that each photon can be prepared in a large number of time slots with a proper waveform. In this work we develop a miniature room-temperature 1550-nm single-photon source to generate narrowband single photon in 50 time slots with a nearly optimal waveform for achieving unity key creation efficiency. By utilizing these single photons in the field test, we demonstrate the differential-phase-shift QKD with a key creation efficiency of 97%. Our work shows that the practical QKD can benefit from the narrowband single photons with controllable waveforms.

Fully Convolutional Network for the Semantic Segmentation of Medical Images: A Survey.

There have been major developments in deep learning in computer vision since the 2010s. Deep learning has contributed to a wealth of data in medical image processing, and semantic segmentation is a salient technique in this field. This study retrospectively reviews recent studies on the application of deep learning for segmentation tasks in medical imaging and proposes potential directions for future development, including model development, data augmentation processing, and dataset creation. The strengths and deficiencies of studies on models and data augmentation, as well as their application to medical image segmentation, were analyzed. Fully convolutional network developments have led to the creation of the U-Net and its derivatives. Another noteworthy image segmentation model is DeepLab. Regarding data augmentation, due to the low data volume of medical images, most studies focus on means to increase the wealth of medical image data. Generative adversarial networks (GAN) increase data volume via deep learning. Despite the increasing types of medical image datasets, there is still a deficiency of datasets on specific problems, which should be improved moving forward. Considering the wealth of ongoing research on the application of deep learning processing to medical image segmentation, the data volume and practical clinical application problems must be addressed to ensure that the results are properly applied.

Breast-conserving surgery with whole breast radiation therapy has a subsequent lower mood disorder incidence rate than total mastectomy in early-stage breast cancer patients: a nationwide population-based longitudinal study.

PURPOSE: Breast-conserving surgery (BCS) followed by whole breast radiation therapy (BCS-WBRT) or total mastectomy without WBRT (TM-no-WBRT) is the primary treatment for early stage breast cancer patients. Our study aimed to identify which early stage breast cancer treatment strategies had a subsequent lower incidence rate of mood disorder over a period of 10 years after the primary treatment. METHODS: This retrospective cohort study consisted of newly diagnosed early stage breast cancer patients in Taiwan from 2000 to 2013 using the National Health Insurance Research Database in Taiwan. We used a 1:1 propensity score matching by age to enrol patients into the BCS-WBRT and TM-no-WBRT groups. Statistical analyses were performed to calculate the hazard ratio and cumulative incidence rate. RESULTS: Our study consisted of 876 BCS-WBRT patients and 1949 TM-no-WBRT patients. After propensity score matching, each study group included 876 patients. The results showed that the mood disorder incidence rate was lower in the BCS-WBRT group than in the TM-no-WBRT group. Multivariate Cox regression analysis revealed that the BCS-WBRT group had a decreased risk of developing mood disorder (adjusted hazard ratio 0.69, 95% CI 0.53-0.90, p < 0.01). Furthermore, the Kaplan-Meier analysis showed that the BCS-WBRT group had a lower cumulative incidence rate of mood disorder, especially depression, after undergoing 10 years of primary treatment (p = 0.004). CONCLUSION: Our results indicated that BCS-WBRT was associated with a lower risk of development of mood disorder over a 10-year period compared to TM-no-WBRT in early stage breast cancer patients. Our findings may provide helpful information, along with other clinical data, for breast cancer patients as they choose the type of appropriate surgery for treatment.

Individual cerebrocerebellar functional network analysis decoding symptomatologic dynamics of postoperative cerebellar mutism syndrome.

Introduction: Postoperative cerebellar mutism syndrome (pCMS) consists of three types of symptoms (motoric, linguistic, and neurobehavioral) in patients with posterior fossa pathologies. The evolutional mechanism of this high cognitive syndromic complex from cerebellar origin remains unconfirmed. Previous studies analyzing CMS patients mostly focused on the association between structural abnormalities that occur during CMS, of which proximal efferent cerebellar pathway (pECP) injury appears to be the most common pathogenesis. However, structural imaging may not be sensitive enough to determine the dynamic course of CMS, since the symptomatology is primarily an output of cerebral operation. Method: We took a network approach in a child during her course of development and recovery of the pCMS. On the other hand, a network neuroscience approach using a mathematical model to extract information from functional imaging to generate interregional connectivity provides abundant evidence that the cerebellum is influential in modulating cerebral functions. Result: This study applied a network approach to children with pCMS. An individual cerebrocerebellar functional network analysis using graph theory was then performed to determine the network dynamics during CMS. Cross-validation of clinical neurophysiology and functional neuroscience suggested the critical role of the pECP within CMS from the network analysis. Conclusion: The employed approach was therefore useful in determining the complex clinical symptoms using individual functional network analysis, which bridges the gap between structural neuroimaging and clinical neurophysiology.

Aspirin and Primary Cancer Risk Reduction in Ischemic Cardiac or Cerebrovascular Disease Survivors: A Nationwide Population-Based Propensity-Matched Cohort Study.

Ischemic cardiac or cerebrovascular disease (ICCD) survivors represent a subpopulation with a high cancer risk. Antiplatelet medications, such as aspirin, remain a fundamental therapy for the secondary prevention of ischemic attack in these patients. We conducted a population-based cohort study to investigate the association of long-term low-dose aspirin use with the risk of primary cancer in ICCD survivors. Patients aged ≥20 years with newly diagnosed ICCD (n = 98,519) between January 2000 and December 2013 were identified from the Taiwan National Health Insurance Research Database. The aspirin user and nonuser groups (each n = 24,030) were propensity-matched (1:1) for age, sex, comorbidities, prior medications, ICCD diagnosis year, and year of index dates. The incidence rate of primary cancer was significantly lower in the user group (6.49/1000 person-years) than in the nonuser group (14.04/1000 person-years). Multivariate Cox regression analysis indicated that aspirin use was an independent factor associated with a reduced risk of primary cancer (aHR (95% confidence interval) = 0.42 (0.38−0.45)) after adjustment. Kaplan−Meier curve analysis revealed that the cumulative incidence rate of primary cancer was significantly lower (p < 0.0001) in the user group than in the nonuser group over the 14-year follow-up period. Subgroup analyses demonstrated that this anticancer effect increased with duration of treatment and with similar estimates in women and men. In addition, aspirin use was associated with a reduced risk for seven out of the ten most common cancers in Taiwan. These findings suggest the anticancer effect of aspirin in ICCD survivors and provide information for assessing the benefit-to-risk profile of aspirin as an antiplatelet medication in these patients.

Androgen deprivation therapy and the risk of subsequent keratitis.

OBJECTIVES: The objective of the study was to determine the risk of subsequent keratitis in prostate cancer (PCa) patients treated with androgen deprivation therapy (ADT). MATERIALS AND METHODS: Three thousand three hundred and nine patients with PCa were identified using data from Taiwan's National Health Insurance Research Database for 2001 through 2013. Among those patients, 856 treated with ADT comprised the study group, while 856 non-ADT-treated patients matched with 1:1 propensity-score-matched analysis comprised the control group. The demographic characteristics and comorbidities of all the patients were analyzed, and Cox proportional hazards regression was utilized to determine the hazard ratios (HRs) for subsequent keratitis. RESULTS: A total of 157 (9.2%) patients had newly diagnosed keratitis. Compared to the non-ADT-treated patients, the ADT-treated patients had a reduced risk of subsequent keratitis, with an adjusted HR of 0.38 (95% confidence interval: 0.27-0.55; P < 0.001). CONCLUSION: ADT treatment apparently decreased the risk of subsequent keratitis in the investigated PCa patients, but the clinical significance of this finding should be further assessed in additional studies.

A Novel Individual Metabolic Brain Network for 18F-FDG PET Imaging.

INTRODUCTION: Metabolic brain network analysis based on graph theory using FDG PET imaging is potentially useful for investigating brain activity alternation due to metabolism changes in different stages of Alzheimer's disease (AD). Most studies on metabolic network construction have been based on group data. Here a novel approach in building an individual metabolic network was proposed to investigate individual metabolic network abnormalities. METHOD: First, a weighting matrix was calculated based on the interregional effect size difference of mean uptake between a single subject and average normal controls (NCs). Then the weighting matrix for a single subject was multiplied by a group-based connectivity matrix from an NC cohort. To study the performance of the proposed individual metabolic network, inter- and intra-hemispheric connectivity patterns in the groups of NC, sMCI (stable mild cognitive impairment), pMCI (progressive mild cognitive impairment), and AD using the proposed individual metabolic network were constructed and compared with those from the group-based results. The network parameters of global efficiency and clustering coefficient and the network density score (NDS) in the default-mode network (DMN) of generated individual metabolic networks were estimated and compared among the disease groups in AD. RESULTS: Our results show that the intra- and inter-hemispheric connectivity patterns estimated from our individual metabolic network are similar to those from the group-based method. In particular, the key patterns of occipital-parietal and occipital-temporal inter-regional connectivity deficits detected in the groupwise network study for differentiating different disease groups in AD were also found in the individual network. A reduction trend was observed for network parameters of global efficiency and clustering coefficient, and also for the NDS from NC, sMCI, pMCI, and AD. There was no significant difference between NC and sMCI for all network parameters. CONCLUSION: We proposed a novel method in constructing the individual metabolic network using a single-subject FDG PET image and a group-based NC connectivity matrix. The result has shown the effectiveness and feasibility of the proposed individual metabolic network in differentiating disease groups in AD. Future studies should include investigation of inter-individual variability and the correlation of individual network features to disease severities and clinical performance.

Dose escalation (81 Gy) with image-guided radiation therapy and volumetric-modulated arc therapy for localized prostate cancer: A retrospective preliminary result.

OBJECTIVES: The objective of the study is to report the acute and late toxicity and preliminary results of localized prostate cancer treated with high-dose radiation therapy (RT). MATERIALS AND METHODS: Between March 2010 and October 2018, a total of 53 patients with clinically localized prostate cancer were treated with definitive RT at our institution. All patients were planned to receive a total dose of 81 Gy with the volumetric-modulated arc therapy technique. Patients were stratified by prognostic risk groups based on the National Comprehensive Cancer Network risk classification criteria. Acute and late toxicities were scored by the Radiation Therapy Oncology Group morbidity grading scales. The definition of biochemical failure was using the 2005 ASTRO Phoenix consensus definition. Median follow-up time was 46.5 months (range: 4.7-81.0 months). RESULTS: The 3-year biochemical failure-free survival rates for low-, intermediate-, and high-risk group patients were 100%, 87.5%, and 84%, respectively. The 3- and 5-year overall survival rates were 83% and 62%, respectively. Three (5.6%) patients developed Grade II acute gastrointestinal (GI) toxicity. Four (7.5%) patients developed Grade II acute genitourinary (GU) toxicity, and none experienced Grade III or higher acute GI or GU symptoms. One (1.8%) patient developed Grade II or higher late GI toxicity. Six (11.3%) patients experienced Grade II late GU toxicity. No Grade III or higher late GI and GU complications have been observed. CONCLUSIONS: Data from the current study demonstrated the feasibility of dose escalation with image-guided and volumetric-modulated arc therapy techniques for the treatment of localized prostate cancer. Minimal acute and late toxicities were observed from patients in this study. Long-term prostate-specific antigen controls are comparable to previously published results of high-dose intensity-modulated RT for localized prostate cancer. Based on this favorable outcome, dose escalation (81 Gy) has become the standard treatment for localized prostate cancer at our institution.

Aspirin decreases hepatocellular carcinoma risk in hepatitis C virus carriers: a nationwide cohort study.

BACKGROUND: Aspirin has been found to lower the occurrence rates of some cancers through the inhibition of the cyclooxygenase enzyme. For example, there is a well-known association between aspirin use and the occurrence of hepatocellular carcinoma (HCC) in hepatitis B virus (HBV) carriers. However, the association, if any, between aspirin use and HCC in hepatitis C virus (HCV) carriers is unknown. Therefore, this study compared the occurrence rates of HCC in HCV carriers treated with or without aspirin. METHODS: The participants in this retrospective cohort study consisted of people newly diagnosed with HCV in Taiwan from 2000 to 2012. Those who were treated with aspirin were defined as the control group, whereas those not treated with aspirin were defined as the comparison cohort. We used a 1:1 propensity score matching by age, sex, comorbidities, drugs, diagnosis year, and index year with covariate assessment. RESULTS: Our study sample consisted of 2980 aspirin-treated HCV carriers and 7771 non-aspirin-treated HCV carriers. After propensity score matching, each cohort consisted of 1911 HCV carriers. The adjusted hazard ratio (aHR) of HCC incidence in the aspirin users (aHR = 0.56, 95% CI = 0.43-0.72, p < 0.001) was significantly lower than that in the non-aspirin users. A Kaplan-Meier analysis showed that among the HCV carriers, the aspirin users had a lower cumulative incidence rate of HCC over the first 10 years of aspirin treatment (p < 0.0001). CONCLUSIONS: The HCC incidence rate was lower in the aspirin-using HCV carriers than in the non- aspirin-using HCV carriers, indicating that the effects of aspirin might occur through inhibition of the cyclooxygenase enzyme pathway. Moreover, protection from HCC was provided by less than a year of aspirin treatment, while treatment with aspirin for 1 to 2 years exhibited the greatest protective effect. We therefore encourage aspirin treatment to prevent HCC in HCV carriers.

Author Correction: Plasma Aß analysis using magnetically-labeled immunoassays and PET 18F-florbetapir binding in non-demented patients with major depressive disorder.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.

Characteristic patterns of inter- and intra-hemispheric metabolic connectivity in patients with stable and progressive mild cognitive impairment and Alzheimer's disease.

The change in hypometabolism affects the regional links in the brain network. Here, to understand the underlying brain metabolic network deficits during the early stage and disease evolution of AD (Alzheimer disease), we applied correlation analysis to identify the metabolic connectivity patterns using 18F-FDG PET data for NC (normal control), sMCI (stable MCI), pMCI (progressive MCI) and AD, and explore the inter- and intra-hemispheric connectivity between anatomically-defined brain regions. Regions extracted from 90 anatomical structures were used to construct the matrix for measuring the inter- and intra-hemispheric connectivity. The brain connectivity patterns from the metabolic network show a decreasing trend of inter- and intra-hemispheric connections for NC, sMCI, pMCI and AD. Connection of temporal to the frontal or occipital regions is a characteristic pattern for conversion of NC to MCI, and the density of links in the parietal-occipital network is a differential pattern between sMCI and pMCI. The reduction pattern of inter and intra-hemispheric brain connectivity in the metabolic network depends on the disease stages, and is with a decreasing trend with respect to disease severity. Both frontal-occipital and parietal-occipital connectivity patterns in the metabolic network using 18F-FDG PET are the key feature for differentiating disease groups in AD.

Diversity of neurodegenerative pathophysiology in nondemented patients with major depressive disorder: Evidence of cerebral amyloidosis and hippocampal atrophy.

BACKGROUND: Patients with late-life depression may be at the preclinical stage of dementia. However, the neurodegenerative processes in late-life depression are poorly understood. This study aimed to investigate the distribution patterns of amyloid pathology and neurodegeneration in a depressive population without dementia. METHODS: The study recruited 63 middle-aged and elderly patients with major depressive disorder (MDD) and 22 control subjects. The MDD patients were further subdivided into those with mild cognitive impairment (MCI) (n = 24) and non-MCI (n = 39) patients. We used the global standardized uptake value ratio of 18 F-florbetapir (AV-45/Amyvid) positron emission tomography imaging as a biomarker of cerebral amyloidosis and the hippocampal volume as a biomarker for neurodegeneration. Cutoff points of brain amyloid positivity and hippocampal atrophy were determined using independent data obtained from clinically diagnosed Alzheimer's disease (AD) patients in a previous study. RESULTS: Most of the control subjects (81.8%) were biomarker-negative, in contrast to the MCI MDD patients (37.5%). A relatively high proportion of the MCI MDD patients (12.5%) exhibited both amyloid positivity and hippocampal atrophy as compared to the control subjects (4.5%) and non-MCI patients (5.1%). However, a considerable proportion of the MCI MDD patients (29.2%) were categorized into the group with hippocampal atrophy alone, and negative amyloid deposition, as compared to the control subjects (0%) and non-MCI patients (5.1%). CONCLUSIONS: This study highlights the expected heterogeneity of the processes of neurodegeneration in MDD patients. The diverse neurodegenerative processes may have important etiologic and therapeutic implications regarding neurodegenerative pathophysiology in late-life depression.

Plasma Aß analysis using magnetically-labeled immunoassays and PET 18F-florbetapir binding in non-demented patients with major depressive disorder.

An increased level of brain amyloid deposition and a decreased level of cerebral spinal fluid (CSF) Aß42 are currently considered reliable biomarkers of Alzheimer's disease (AD); however, the usefulness of plasma Aß levels are not well-established. This study investigated the relationships between plasma Aß levels and cerebral amyloidosis in 36 non-demented patients with major depressive disorder (MDD). All participants underwent 18F-florbetapir PET imaging and provided a blood sample at the same time for immunomagnetic reduction assay to measure the plasma levels of Aß40 and Aß42. We found inverse associations of the plasma Aß42 level and the Aß42/Aß40 ratio, and a positive association of the plasma Aß40 level, with cerebral amyloid deposition in the precuneus, parietal and posterior cingulate cortex. Subgroup analyses in subjects with higher 18F-florbetapir uptake values or MDD with amnestic mild cognitive impairment revealed more pervasive relationships of plasma Aß measures with 18F-florbetapir binding across the brain regions examined. The study suggested that regional brain amyloid deposition in terms of 18F-florbetapir PET uptake had weak-to-moderate associations with plasma Aß42 and Aß40 levels, and the Aß42/Aß40 ratio. Validation in a larger population of subjects of known cerebral amyloidosis status is needed. Careful interpretation of plasma data is warranted.

Spontaneous arteriovenous fistula of the superficial temporal artery: Diagnosis and treatment.

OBJECTIVES: Despite the increasing reports of traumatic and iatrogenic arteriovenous fistulas (AVFs) of the superficial temporal artery (STA), the spontaneous origination of fistulas is extremely rare, and very little is known about their natural development. Spontaneous AVFs of the STA have the characteristic findings of an expanding, painless pulsatile mass and a palpable thrill with or without a vascular murmur. PATIENTS AND METHODS: This article describes five patients with AVFs of the STA with no history of a head injury. Four of them were treated successfully either by surgical resection or by endovascular embolization. These five illustrative cases with their medium-term follow-up results are reported. RESULTS: In two patients, we successfully used a single-balloon alone to occlude the fistula without any complications; the patients experienced no recurrences during the clinical follow-up. In the other two patients, we carefully identified and ligated all of the involved feeding arteries and draining veins, which was followed by an excision of the lesion. At the 6-month follow-up, the patients were doing very well, with no evidence of AVF recurrences or new neurological complaints. CONCLUSIONS: AVFs of the STA can be detected via a computed tomography angiogram (CTA) or by intra-arterial angiography alone. Intra-arterial angiography, however, remains the definitive type of investigation. AVF may be treated either by surgical ligation and excision under a local or general anesthetic or by endovascular embolization. The former modality has been the most common method of treating the lesion in the vast majority of reports. However, endovascular embolization also appears to be suitable for treating this condition.

Focused ultrasound simultaneous irradiation/MRI imaging, and two-stage general kinetic model.

Many studies have investigated how to use focused ultrasound (FUS) to temporarily disrupt the blood-brain barrier (BBB) in order to facilitate the delivery of medication into lesion sites in the brain. In this study, through the setup of a real-time system, FUS irradiation and injections of ultrasound contrast agent (UCA) and Gadodiamide (Gd, an MRI contrast agent) can be conducted simultaneously during MRI scanning. By using this real-time system, we were able to investigate in detail how the general kinetic model (GKM) is used to estimate Gd penetration in the FUS irradiated area in a rat's brain resulting from UCA concentration changes after single FUS irradiation. Two-stage GKM was proposed to estimate the Gd penetration in the FUS irradiated area in a rat's brain under experimental conditions with repeated FUS irradiation combined with different UCA concentrations. The results showed that the focal increase in the transfer rate constant of Ktrans caused by BBB disruption was dependent on the doses of UCA. Moreover, the amount of in vivo penetration of Evans blue in the FUS irradiated area in a rat's brain under various FUS irradiation experimental conditions was assessed to show the positive correlation with the transfer rate constants. Compared to the GKM method, the Two-stage GKM is more suitable for estimating the transfer rate constants of the brain treated with repeated FUS irradiations. This study demonstrated that the entire process of BBB disrupted by FUS could be quantitatively monitored by real-time dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).

Pharmacokinetic changes induced by focused ultrasound in glioma-bearing rats as measured by dynamic contrast-enhanced MRI.

Focused ultrasound (FUS) combined with microbubbles has been shown to be a noninvasive and targeted drug delivery technique for brain tumor treatment. The purpose of this study was to measure the kinetics of Gadolinium diethylenetriamine pentaacetic acid (Gd-DTPA) in glioma-bearing rats in the presence of FUS-induced blood-brain barrier disruption (BBB-D) by magnetic resonance imaging (MRI). A total of ten glioma-bearing rats (9-12 weeks, 290-340 g) were used in this study. Using dynamic contrast-enhanced (DCE)-MRI, the spatial permeability of FUS-induced BBB-D was evaluated and the kinetic parameters were calculated by a general kinetic model (GKM). The results demonstrate that the mean Ktrans of the sonicated tumor (0.128±0.019 at 20 min and 0.103±0.023 at 24 h after sonication, respectively) was significantly higher than (2.46-fold at 20 min and 1.78-fold at 24 h) that of the contralateral (non-sonicated) tumor (0.052±0.019 at 20 min and 0.058±0.012 at 24 h after sonication, respectively). In addition, the transfer constant Ktrans in the sonicated tumor correlated strongly with tissue EB extravasation (R = 0.95), which suggests that DCE-MRI may reflect drug accumulation in the brain. Histological observations showed no macroscopic damage except for a few small erythrocyte extravasations. The current study demonstrates that DCE-MRI can monitor the dynamics of the FUS-induced BBB-D process and constitutes a useful tool for quantifying BBB permeability in tumors.