bluuebunny/crossref_metadata_2025_split huggingface dataset:
Coffee, Black Tea, and Green Tea Consumption in Relation to Plasma Metabolites in an Asian Population
Authors: Seow Wei Jie , Low Dorrain Yanwen , Pan Wen‐Chi , Gunther Samuel H. , Sim Xueling , Torta Federico , Herr Deron R. , Kovalik Jean‐Paul , Ching Jianhong , Khoo Chin Meng , Wenk Markus R. , Tai E Shyong , van Dam Rob M. 
Published: October 2020

ScopeCoffee and tea are among the most popular beverages in the world. However, the association between habitual coffee, green tea, and black tea consumption with metabolomics profiles in Asian populations remain largely unknown.Methods and Results158 metabolites (14 amino acids, 45 acylcarnitines, and 99 sphingolipids) in the blood plasma of participants are measured from the population‐based Singapore Prospective Study Program cohort using mass spectrometry (MS). Linear regression models are used to obtain the estimates for the association between coffee and tea consumption with metabolite levels, adjusted for potential confounders and false discovery rate (FDR). Coffee consumption is significantly associated with higher levels of 63 sphingolipids (29 sphingomyelins, 32 ceramides, a sphingosine‐1‐phosphate, and a sphingosine) and lower levels of 13 acylcarnitines and alanine. Black tea consumption is significantly associated with higher levels of eight sphingolipids, and lower levels of an amino acid, whereas green tea is significantly inversely associated with four metabolites (C8:1‐OH acylcarnitine, ganglioside GM3 d18:1/16:0, sphingomyelins d18:2/18:0 and d18:1/14:0).ConclusionsCoffee, black tea, and green tea consumption are associated with plasma levels of certain classes of sphingolipids and acylcarnitines in an Asian population, particularly sphingomyelins, which may mediate the health benefits of these beverages.
Reproducibility analysis of multi‐institutional paired expert annotations and radiomic features of the Ivy Glioblastoma Atlas Project (Ivy GAP) dataset
Authors: Pati Sarthak , Verma Ruchika , Akbari Hamed , Bilello Michel , Hill Virginia B. , Sako Chiharu , Correa Ramon , Beig Niha , Venet Ludovic , Thakur Siddhesh , Serai Prashant , Ha Sung Min , Blake Geri D. , Shinohara Russell Taki , Tiwari Pallavi , Bakas Spyridon 
Published: October 2020

PurposeThe availability of radiographic magnetic resonance imaging (MRI) scans for the Ivy Glioblastoma Atlas Project (Ivy GAP) has opened up opportunities for development of radiomic markers for prognostic/predictive applications in glioblastoma (GBM). In this work, we address two critical challenges with regard to developing robust radiomic approaches: (a) the lack of availability of reliable segmentation labels for glioblastoma tumor sub‐compartments (i.e., enhancing tumor, non‐enhancing tumor core, peritumoral edematous/infiltrated tissue) and (b) identifying “reproducible” radiomic features that are robust to segmentation variability across readers/sites.Acquisition and validation methodsFrom TCIA’s Ivy GAP cohort, we obtained a paired set (n = 31) of expert annotations approved by two board‐certified neuroradiologists at the Hospital of the University of Pennsylvania (UPenn) and at Case Western Reserve University (CWRU). For these studies, we performed a reproducibility study that assessed the variability in (a) segmentation labels and (b) radiomic features, between these paired annotations. The radiomic variability was assessed on a comprehensive panel of 11 700 radiomic features including intensity, volumetric, morphologic, histogram‐based, and textural parameters, extracted for each of the paired sets of annotations. Our results demonstrated (a) a high level of inter‐rater agreement (median value of DICE ≥0.8 for all sub‐compartments), and (b) ≈24% of the extracted radiomic features being highly correlated (based on Spearman’s rank correlation coefficient) to annotation variations. These robust features largely belonged to morphology (describing shape characteristics), intensity (capturing intensity profile statistics), and COLLAGE (capturing heterogeneity in gradient orientations) feature families.Data format and usage notesWe make publicly available on TCIA’s Analysis Results Directory (https://doi.org/10.7937/9j41‐7d44), the complete set of (a) multi‐institutional expert annotations for the tumor sub‐compartments, (b) 11 700 radiomic features, and (c) the associated reproducibility meta‐analysis.Potential applicationsThe annotations and the associated meta‐data for Ivy GAP are released with the purpose of enabling researchers toward developing image‐based biomarkers for prognostic/predictive applications in GBM.
Deep learning algorithms for brain disease detection with magnetic induction tomography
Authors: Chen Ruijuan , Huang Juan , Song Yixiang , Li Bingnan , Wang Jinhai , Wang Huiquan 
Published: October 2020

PurposeIn order to improve the reconstruction accuracy of magnetic induction tomography (MIT) and achieve fast imaging especially in the detection of cerebral hemorrhage, artificial intelligence algorithms are proposed to improve the accuracy of MIT inverse problem.MethodsAccording to the standard geometric data of human head, a three‐dimensional (3D) head model containing four layer tissues is established for brain image reconstruction of MIT. Four deep learning (DL) networks, including restricted Boltzmann machine (RBM), deep belief network (DBN), stacked autoencoder (SAE), and denoising autoencoder (DAE), are used to solve the nonlinear reconstruction problem of MIT, and the reconstruction results of DL networks and back‐projection algorithm are compared. Finally, in order to verify the practical value of DL algorithms, the phantom experiment is carried out with MIT detection system.ResultsUsing the nonlinear data learning ability of DL algorithms, the rapid and high‐precision imaging of cerebral hemorrhage can be realized. Compared with the back‐projection algorithm, the DL improves the artifact and the accuracy of the reconstruction image. The location and volume of bleeding can be reconstructed and the prediction time reaches 20 ms. Moreover, the anti‐noise performance of the networks can reach 20 dB.ConclusionsThe DL can effectively improve the reconstruction accuracy and prediction speed of the image when it is applied to the reconstruction of cerebral hemorrhage in MIT. This feasibility study MIT to be a potential technology for brain diseases to fully meet the needs of accurate, rapid, and low‐cost clinical diagnosis and continuous monitoring.
Impact of range uncertainty on clinical distributions of linear energy transfer and biological effectiveness in proton therapy
Authors: Hahn Christian , Eulitz Jan , Peters Nils , Wohlfahrt Patrick , Enghardt Wolfgang , Richter Christian , Lühr Armin 
Published: October 2020

PurposeIncreased radiation response after proton irradiation, such as late radiation‐induced toxicity, is determined by high dose and elevated linear energy transfer (LET). Steep dose‐averaged LET (LETd) gradients and elevated LETd occur at the end of proton range and might be particularly sensitive to uncertainties in range prediction. Therefore, this study quantified LETd distributions and the impact of range uncertainty in robust dose‐optimized proton treatment plans and assessed the biological effect in normal tissues and tumors of patients.MethodsFor each of six cancer patients (two brain, head‐and‐neck, and prostate), two nominal treatment plans were robustly dose optimized using single‐ and multi‐field optimization, respectively. For each plan, two additional scenarios with ±3.5% range deviation relative to the nominal plan were derived by global rescaling of stopping‐power ratios. Dose and LETd distributions were calculated for each scenario using the beam parameters of the corresponding nominal plan. The variability in relative biological effectiveness (RBE) and probability of late radiation‐induced brain toxicity (PIC) was assessed.ResultsThe optimization technique (single‐ vs multi‐field) had a negligible impact on the LETd distributions in the clinical target volume (CTV) and in most organs at risk (OARs). LETd distributions in the CTV were rather homogeneous with arithmetic mean of LETd below 3.2 keV/µm and robust against range deviations. The RBE variability within the CTV induced by range uncertainty was small (≤0.05, 95% confidence interval). In OARs, LETd hotspots (>7 keV/µm) occurred and LETd distributions were inhomogeneous and sensitive to range deviations. LETd hotspots and the impact of range deviations were most prominent in OARs of brain tumor patients which translated in RBE values exceeding 1.1 in all brain OARs. The near‐maximum predicted PIC in healthy brain tissue of brain tumor patients was smaller than 5% and occurred adjacent to the CTV. Range deviations induced absolute differences in PIC up to 1.2%.ConclusionsRobust dose optimization generates LETd distributions in the target volume robust against range deviations. The current findings support using a constant RBE within the CTV. The impact of range deviations on the considered probability of late radiation‐induced toxicity in brain tissue was limited for robust dose‐optimized treatment plans. Incorporation of LETd in robust optimization frameworks may further reduce uncertainty related to the RBE‐weighted dose estimation in normal tissues.
Experimental investigation of TRS‐483 reference dosimetry correction factors for Leksell Gamma Knife® Icon™ beams
Authors: Häger Wille , Kaveckyte Vaiva , Benmakhlouf Hamza 
Published: October 2020

PurposeRadiosurgery using the Leksell Gamma Knife® (LGK) Icon™ is an established technique used for treating intracranial lesions. The largest beam field size the LGK Icon can produce is a 16 mm diameter sphere. Despite this, reference dosimetry on the LGK Icon is typically performed using ionization chambers calibrated in 10 × 10 cm2 fields. Furthermore, plastic phantoms are widely used instead of liquid water phantoms. In an effort to resolve these issues, the International Atomic Energy Agency (IAEA) in collaboration with American Association of Physicists in medicine (AAPM) recently published Technical Report Series No. 483 (TRS‐483) as a Code of Practice for small‐field dosimetry. TRS‐483 includes small‐field correction factors, , intended to account for the differences between setups when using small‐field modalities such as the LGK Icon, and conventional setups. Since the publication of TRS‐483, at least three new sets of values of for the LGK Icon have been published. The purpose of this study was to experimentally investigate the published values of for commonly used phantom and ionization chamber (IC) models for the LGK Icon.MethodsDose‐rates from two LGK units were determined using acrylonitrile butadiene styrene (ABS) and Certified Medical Grade Solid Water® (SW) phantoms, and PTW 31010 and PTW 31016 ICs. Correction factors were applied, and the resulting dose‐rates compared. Relative validity of the correction factors was investigated by taking the ratios of dose‐rate correction factor products. Additionally, dose‐rates from the individual sectors were determined in order to calculate the beam attenuation caused by the ABS phantom adapter.Results and ConclusionsIt was seen that the dose‐rate is underestimated by at least 1% when using the ABS phantom, which was attributed to fluence perturbation caused by the IC and phantom adapter. Published correction factors account for these effects to varying degree and should be used. The SW phantom is unlikely to underestimate the dose‐rate by more than 1%, and applying could not be shown to be necessary. Out of the two phantom models, the ABS phantom is not recommended for use in LGK reference dosimetry. The use of newly published values of should be considered.
Differences in the dielectric properties of various benign and malignant thyroid nodules
Authors: Huang Shengyi , Cai Weizhen , Han Shuai , lin Yu , Wang Yu , Chen Fei , Shao Guoli , Liu Yonghong , Yu Xuefei , Cai Zhai , Zou Zenan , Yao Shun , Wang Qiaohui , Li Zhou 
Published: October 2020

PurposeThis experiment was conducted to investigate the dielectric properties of different types of thyroid nodules. Our goal was to find a simple and fast method to detect thyroid diseases at different stages from the dielectric properties of thyroid nodules.MethodsWe used the open‐ended coaxial line method to measure the dielectric permittivities of thyroid tissues from 155 patients at frequencies ranging from 1 to 4000 MHz. Tissues that were investigated included normal thyroid tissue and benign and malignant thyroid nodules (nodular goiter, follicular adenoma, papillary carcinoma, and follicular carcinoma), as determined from pathological reports. Differences in dielectric properties were measured between each nodule and the surrounding 1 cm of tissue.ResultsThe analysis results revealed that the dielectric permittivity and conductivity values were positively correlated with the degree of malignancy of the nodule (normal < benign < malignant; all differences P < 0.05). This was more obvious at frequencies within 20~70 MHz, following the order normal tissue < nodular goiter < follicular adenoma < papillary carcinoma < follicular carcinoma. A significant difference (P < 0.05) in dielectric permittivity and conductivity was found when comparing these nodules with the surrounding 1 cm of tissue.ConclusionsNormal, benign, and malignant nodules were successfully distinguished from one another, and dielectric permittivity was found to be a more sensitive parameter than conductivity. In particular, different disease types can be distinguished at a stimulation frequency of 20~70 MHz, which shows that dielectric properties have application prospects for the detection and diagnosis of cancer. At the same time, the dielectric parameter differences between the surrounding 1 cm of tissue and the diseased nodule can distinguish the tumor and its surrounding tissues in real time during surgery to determine the tumor boundary.
Deep learning‐based radiomics predicts response to chemotherapy in colorectal liver metastases
Authors: Wei Jingwei , Cheng Jin , Gu Dongsheng , Chai Fan , Hong Nan , Wang Yi , Tian Jie 
Published: October 2020

PurposeThe purpose of this study was to develop and validate a deep learning (DL)‐based radiomics model to predict the response to chemotherapy in colorectal liver metastases (CRLM).MethodsIn this retrospective study, we enrolled 192 patients diagnosed with CRLM who received first‐line chemotherapy and were followed by response assessment. Tumor response was identified according to the Response Evaluation Criteria in Solid Tumors (RECIST). Contrast‐enhanced multidetector computed tomography (MDCT) images were fed as inputs of the ResNet10‐based DL radiomics model, and the possibility of response was predicted as the output. The final combined DL radiomics model was constructed by integrating the response‐related clinical factors and the developed DL radiomics signature. A time‐independent validation cohort (n = 48) was extracted from the 192 patients to evaluate the DL model with area under the receiver operating characteristic curve (AUC), specificity, and sensitivity. Meanwhile, a traditional radiomics model was constructed using least absolute shrinkage and selection operator (lasso) as comparisons with the DL‐based model.ResultsAccording to RECIST criteria, 131 patients were identified as responders with complete response, partial response, and stable disease, while 61 patients were nonresponders with progression disease. The selected predictive clinical factor turned out to be the carcinoembryonic antigen (CEA) level with AUC of 0.489 (95% confidence interval [CI], 0.380–0.599) and 0.558 (95% CI, 0.374–0.741) in the training and validation cohorts, respectively. The DL‐based model provided better performance than the traditional classifier‐based radiomics model with significantly higher AUC (training: 0.903 [95% CI, 0.851–0.955] vs 0.745 [95% CI, 0.659–0.831]; validation: 0.820 [95% CI, 0.681–0.959] vs 0.598 [95% CI, 0.422–0.774]). The combination of DL‐based model with the CEA level provided slightly increased performance with AUC of 0.935 [95% CI, 0.897–0.973] in the training cohort and 0.830 [95% CI, 0.688‐0.973] in the validation cohort.ConclusionsThe developed DL‐based radiomics model could improve the efficiency to predict the response to chemotherapy in CRLM, which may assist in subsequent personalized treatment decision‐making in CRLM management.
Patient‐reported outcomes in the setting of a randomized control trial on the efficacy of transcutaneous stimulation of the posterior tibial nerve compared to percutaneous stimulation in idiopathic overactive bladder syndrome
Authors: Ramírez‐García Inés , Kauffmann Stéphanie , Blanco‐Ratto Laia , Carralero‐Martínez Andrea , Sánchez Emília 
Published: October 2020

AbstractAimTo compare the efficacy of transcutaneous tibial nerve stimulation (TTNS) with percutaneous tibial nerve stimulation (PTNS) regarding patient‐reported outcomes measures, specifically quality‐of‐life (QoL) improvement and patient's treatment benefit, on symptoms associated with overactive bladder (OAB).MethodsPatients with refractory OAB symptoms and detrusor overactivity were randomized to receive TTNS or PTNS for 12 weeks. Data from self‐report OAB questionnaire short form (q‐SF) and incontinence (I)‐QoL questionnaires were collected pretreatment and at Weeks 6 and 12. Patients’ self‐perceived benefits on symptoms (treatment benefit scale, TBS) were also gathered. Mean change in scores within and between groups were estimated.ResultsSixty‐eight adult patients (67.6% women) were included in the intention to treat analysis. Of those, 61 completed all questionnaires and were analyzed as per protocol. Statistically significant improvements in OAB‐q‐SF and I‐QoL between baseline and end‐of‐treatment scores were observed in both TTNS and PTNS groups (p < .001), being the differences much higher than the minimal important difference in both questionnaires. The scores of the OAB‐q‐SF scales (symptoms bother and health‐related QoL) showed no statistically significant differences between the two groups. Similarly, the differences regarding the I‐QoL questionnaire were statistically nonsignificant either (p = .607). When assessing the treatment benefit on symptoms, TBS responses between groups showed no statistical differences.ConclusionA significant improvement of QoL was observed in both TTNS and PTNS groups. However, no patient‐reported outcomes measures scores support a difference between the two groups. Therefore, these findings along with TTNS ease of application and less invasiveness may lead to an increased indication of this technique for OAB.
Symptoms and noninvasive test parameters that clinically differentiate detrusor underactivity from bladder outlet obstruction without a pressure‐flow‐based diagnosis in men with lower urinary tract symptoms
Authors: Takahashi Ryosuke , Takei Mineo , Namitome Ryo , Yamaguchi Osamu , Eto Masatoshi 
Published: October 2020

AbstractAimTo examine the symptoms and noninvasive test parameters that clinically differentiate detrusor underactivity (DU) from bladder outlet obstruction (BOO) without using a pressure‐flow study (PFS) in male patients with lower urinary tract symptoms.MethodsWe examined 909 men who underwent PFS and also had the following data: International Prostate Symptom Score (IPSS), free uroflowmetry, postvoid residual (PVR), and prostate volume (PV). Using these data, significant symptoms and noninvasive test parameters that clinically differentiate DU from BOO were examined.ResultsSignificant parameters between DU patients and BOO patients were older age, smaller PV, and lower urgency symptom score (IPSS Q4). Optimal cutoff values were determined using receiver operating characteristic curves (≥74 years old, PV ≤ 34.8, and IPSS Q4 ≤ 1) and patients were categorized on the basis of the number of significant factors. The probability of DU or BOO in patients with three significant factors were 77% and 34%, respectively.ConclusionThree factors (older age, smaller PV, and fewer urgency symptom) were the predictive factors to differentiate DU from BOO, which might be useful for estimating the probability of DU in clinical practice without a PFS.
Testing a new, intensified infusion‐withdrawal protocol for urinary urgency provocation in brain‐bladder studies
Authors: Clarkson Becky D , Karim Helmet T , Griffiths Derek J , Resnick Neil M 
Published: October 2020

AbstractIntroductionThe brain's role in bladder control has become an important area of study in the last 15 years. Typically, the brain's role in urinary urgency has been studied by repeated infusion and withdrawal of fluid, per catheter, to provoke urgency sensation during a whole brain magnetic resonance imaging (MRI) scan. Since this technique generally requires a large group size, we tested a more intense infusion‐withdrawal protocol in an attempt to improve signal to noise ratio and repeatability of the signal which would, in turn, allow us to further probe subtypes of urgency urinary incontinence.MethodsA total of 12 women over the age of 60 were recruited to test a new “intense” infusion withdrawal protocol. They underwent this new protocol during a functional brain MRI scan. The primary outcome was comparison of activity within the insula, medial pre‐frontal cortex and dorsal anterior cingulate cortex/supplementary motor area (dACC/SMA). Immediate test‐retest repeatability was measured using intraclass correlation. Secondary exploratory evaluation of differences in the whole brain between protocols was conducted.ResultsThere was no significant difference in signal in any of the a priori regions of interest between protocols. Test‐retest repeatability in the new protocol was poor compared to the original protocol, and variability was higher. Three participants were not able to tolerate the “intense” protocol.ConclusionThe small improvement in signal to noise ratio of the new protocol was not sufficient to overcome the poorly tolerated intense filling protocol.
In vivo investigation of the multi‐exponentialT2decay in human white matter at 7 T: Implications for myelin water imaging at UHF
Authors: Wiggermann Vanessa , MacKay Alexander L. , Rauscher Alexander , Helms Gunther 
Published: October 2020

IntroductionMulti‐componentT2mapping using a gradient‐ and spin‐echo (GraSE) acquisition has become standard for myelin water imaging at 3 T. Higher magnetic field strengths promise signal‐to‐noise ratio benefits but face specific absorption rate limits and shortenedT2times. This study investigates compartmentalT2times in vivo and addresses advantages and challenges of multi‐componentT2mapping at 7 T.MethodsWe acquired 3D multi‐echo GraSE data in seven healthy adults at 7 T, with three subjects also scanned at 3 T. Stimulated echoes arising fromB1+inhomogeneities were accounted for by the extended phase graph (EPG) algorithm. We used the computedT2distributions to determineT2times that identify different water pools and assessed signal‐to‐noise and fit‐to‐noise characteristics of the signal estimation. We compared shortT2fractions andT2properties of the intermediate water pool at 3 T and 7 T.ResultsFlip angle mapping confirmed that EPG accurately determined the largerB1+inhomogeneity at 7 T. Multi‐componentT2analysis demonstrated shortenedT2times at 7 T compared with 3 T. Fit‐to‐noise and signal‐to‐noise ratios were improved at 7 T but depended onB1+homogeneity. Adjusting the shortestT2to 8 ms and theT2threshold that separates different water compartments to 20 ms yielded shortT2fractions at 7 T that conformed to 3 T data. ShortT2fractions in myelin‐rich white matter regions were lower at 7 T than at 3 T, and higher in iron‐rich structures.DiscussionAdjusting theT2compartment boundaries was required due to the shorterT2relaxation times at 7 T. Shorter echo spacing would better sample the fast decaying signal but would increase peripheral nerve stimulation. Multi‐channel transmission will improveT2measurements at 7 T.ConclusionWe used a multi‐echo 3D GraSE sequence to characterize the multi‐exponentialT2decay at 7 T. We adaptedT2parameters for evaluation of the shortT2fraction. Obtained 7 T multi‐componentT2maps were in good agreement with 3 T data.