Understanding ADC variation by fat content effect using a dual-function MRI phantom.

BACKGROUND: A dual-function phantom designed to quantify the apparent diffusion coefficient (ADC) in different fat contents (FCs) and glass bead densities (GBDs) to simulate the human tissues has not been documented yet. We propose a dual-function phantom to quantify the FC and to measure the ADC at different FCs and different GBDs. METHODS: A fat-containing diffusion phantom comprised by 30 glass-bead-containing fat-water emulsions consisting of six different FCs (0, 10, 20, 30, 40, and 50%) multiplied by five different GBDs (0, 0.1, 0.25, 0.5, and 1.0 g/50 mL). The FC and ADC were measured by the "iterative decomposition of water and fat with echo asymmetry and least squares estimation-IQ," IDEAL-IQ, and single-shot echo-planar diffusion-weighted imaging, SS-EP-DWI, sequences, respectively. Linear regression analysis was used to evaluate the relationship among the fat fraction (FF) measured by IDEAL-IQ, GBD, and ADC. RESULTS: The ADC was significantly, negatively, and linearly associated with the FF (the linear slope ranged from -0.005 to -0.017, R2 = 0.925 to 0.986, all p < 0.001). The slope of the linear relationship between the ADC and the FF, however, varied among different GBDs (the higher the GBD, the lower the slope). ADCs among emulsions across different GBDs and FFs were overlapped. Emulsions with low GBDs plus high FFs shared a same lower ADC range with those with median or high GBDs plus median or lower FFs. CONCLUSIONS: A novel dual-function phantom simulating the human tissues allowed to quantify the influence of FC and GBD on ADC. RELEVANCE STATEMENT: The study developed an innovative dual-function MRI phantom to explore the impact of FC on ADC variation that can affect clinical results. The results revealed the superimposed effect on FF and GBD density on ADC measurements. KEY POINTS: • A dual-function phantom made of glass bead density (GBD) and fat fraction (FF) emulsion has been developed. • Apparent diffusion coefficient (ADC) values are determined by GBD and FF. • The dual-function phantom showed the mutual ADC addition between FF and GBD.

Highly accelerated multi-shot intravoxel incoherent motion diffusion-weighted imaging in brain enabled by parametric POCS-based multiplexed sensitivity encoding.

Recently, intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) has also been demonstrated as an imaging tool for applications in neurological and neurovascular diseases. However, the use of single-shot diffusion-weighted echo-planar imaging for IVIM DWI acquisition leads to suboptimal data quality: for instance, geometric distortion and deteriorated image quality at high spatial resolution. Although the recently commercialized multi-shot acquisition methods, such as multiplexed sensitivity encoding (MUSE), can attain high-resolution and high-quality DWI with signal-to-noise ratio (SNR) performance superior to that of the conventional parallel imaging method, the prolonged scan time associated with multi-shot acquisition is impractical for routine IVIM DWI. This study proposes an acquisition and reconstruction framework based on parametric-POCSMUSE to accelerate the four-shot IVIM DWI with 70% reduction of total scan time (13 min 8 s versus 4 min 8 s). First, the four-shot IVIM DWI scan with 17 b values was accelerated by acquiring only one segment per b value except for b values of 0 and 600 s/mm2 . Second, an IVIM-estimation scheme was integrated into the parametric-POCSMUSE to enable joint reconstruction of multi-b images from under-sampled four-shot IVIM DWI data. In vivo experiments on both healthy subjects and patients show that the proposed framework successfully produced multi-b DW images with significantly higher SNRs and lower reconstruction errors than did the conventional acceleration method based on parallel imaging. In addition, the IVIM quantitative maps estimated from the data produced by the proposed framework showed quality comparable to that of fully sampled MUSE-reconstructed images, suggesting that the proposed framework can enable highly accelerated multi-shot IVIM DWI without sacrificing data quality. In summary, the proposed framework can make multi-shot IVIM DWI feasible in a routine MRI examination, with reasonable scan time and improved geometric fidelity.

The role of input imaging combination and ADC threshold on segmentation of acute ischemic stroke lesion using U-Net.

BACKGROUND: To evaluate the effect of the weighting of input imaging combo and ADC threshold on the performance of the U-Net and to find an optimized input imaging combo and ADC threshold in segmenting acute ischemic stroke (AIS) lesion. METHODS: This study retrospectively enrolled a total of 212 patients having AIS. Four combos, including ADC-ADC-ADC (AAA), DWI-ADC-ADC (DAA), DWI-DWI-ADC (DDA), and DWI-DWI-DWI (DDD), were used as input images, respectively. Three ADC thresholds including 0.6, 0.8 and 1.8 × 10-3 mm2/s were applied. Dice similarity coefficient (DSC) was used to evaluate the segmentation performance of U-Nets. Nonparametric Kruskal-Wallis test with Tukey-Kramer post-hoc tests were used for comparison. A p < .05 was considered statistically significant. RESULTS: The DSC significantly varied among different combos of images and different ADC thresholds. Hybrid U-Nets outperformed uniform U-Nets at ADC thresholds of 0.6 × 10-3 mm2/s and 0.8 × 10-3 mm2/s (p < .001). The U-Net with imaging combo of DDD had segmentation performance similar to hybrid U-Nets at an ADC threshold of 1.8 × 10-3 mm2/s (p = .062 to 1). The U-Net using the imaging combo of DAA at the ADC threshold of 0.6 × 10-3 mm2/s achieved the highest DSC in the segmentation of AIS lesion. CONCLUSIONS: The segmentation performance of U-Net for AIS varies among the input imaging combos and ADC thresholds. The U-Net is optimized by choosing the imaging combo of DAA at an ADC threshold of 0.6 × 10-3 mm2/s in segmentating AIS lesion with highest DSC. KEY POINTS: • Segmentation performance of U-Net for AIS differs among input imaging combos. • Segmentation performance of U-Net for AIS differs among ADC thresholds. • U-Net is optimized using DAA with ADC = 0.6 × 10-3 mm2/s.

Utilization of CT and MRI scanning in Taiwan, 2000-2017.

OBJECTIVES: This population-based study aimed to collect, analyze, and summarize the long-term trends in medical imaging use in Taiwan. METHODS: A retrospective cohort population-based study of medical imaging usage for the individuals who received care under the National Health Insurance system from 2000 to 2017. CT and MRI utilization rates were determined overall as well as across certain variables including patient age, hospital type, health care type, hospital characteristics, and geographic area. RESULTS: Individuals registered in our health insurance system have received 21,766,745 CT scans and 7,520,088 MRI scans from 2000 to 2017. Annual growth rates for both imaging types were positive over that period, though growth rates have slowed in recent years. The growth rate for CT use was greatest (9-12%) between 2001 and 2004, dropped to 2% in 2005, then generally rose thereafter, reaching 3% in 2017. Similarly, MRI use growth peaked at 24% between 2001 and 2003, dropped to 4% in 2005, then increased in a fluctuating manner, reaching 2% in 2017. CONCLUSION: Over the past 2 decades, CT and MRI use in Taiwan has increased sharply, especially in the oldest age group (≥ 60 years old), but growth rates have slowed in recent years. Increases in imaging use have corresponded with improved clinical outcomes, including greater life expectancy and reduced mortality rates, though further assessment is required to demonstrate a direct link with imaging. Nevertheless, the better clinical outcomes are also predisposed by the comprehensive care covered by the NHI system.

Improving performance of deep learning models using 3.5D U-Net via majority voting for tooth segmentation on cone beam computed tomography.

Deep learning allows automatic segmentation of teeth on cone beam computed tomography (CBCT). However, the segmentation performance of deep learning varies among different training strategies. Our aim was to propose a 3.5D U-Net to improve the performance of the U-Net in segmenting teeth on CBCT. This study retrospectively enrolled 24 patients who received CBCT. Five U-Nets, including 2Da U-Net, 2Dc U-Net, 2Ds U-Net, 2.5Da U-Net, 3D U-Net, were trained to segment the teeth. Four additional U-Nets, including 2.5Dv U-Net, 3.5Dv5 U-Net, 3.5Dv4 U-Net, and 3.5Dv3 U-Net, were obtained using majority voting. Mathematical morphology operations including erosion and dilation (E&D) were applied to remove diminutive noise speckles. Segmentation performance was evaluated by fourfold cross validation using Dice similarity coefficient (DSC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV). Kruskal-Wallis test with post hoc analysis using Bonferroni correction was used for group comparison. P < 0.05 was considered statistically significant. Performance of U-Nets significantly varies among different training strategies for teeth segmentation on CBCT (P < 0.05). The 3.5Dv5 U-Net and 2.5Dv U-Net showed DSC and PPV significantly higher than any of five originally trained U-Nets (all P < 0.05). E&D significantly improved the DSC, accuracy, specificity, and PPV (all P < 0.005). The 3.5Dv5 U-Net achieved highest DSC and accuracy among all U-Nets. The segmentation performance of the U-Net can be improved by majority voting and E&D. Overall speaking, the 3.5Dv5 U-Net achieved the best segmentation performance among all U-Nets.

Improving interobserver agreement and performance of deep learning models for segmenting acute ischemic stroke by combining DWI with optimized ADC thresholds.

OBJECTIVES: To examine the role of ADC threshold on agreement across observers and deep learning models (DLMs) plus segmentation performance of DLMs for acute ischemic stroke (AIS). METHODS: Twelve DLMs, which were trained on DWI-ADC-ADC combination from 76 patients with AIS using 6 different ADC thresholds with ground truth manually contoured by 2 observers, were tested by additional 67 patients in the same hospital and another 78 patients in another hospital. Agreement between observers and DLMs were evaluated by Bland-Altman plot and intraclass correlation coefficient (ICC). The similarity between ground truth (GT) defined by observers and between automatic segmentation performed by DLMs was evaluated by Dice similarity coefficient (DSC). Group comparison was performed using the Mann-Whitney U test. The relationship between the DSC and ADC threshold as well as AIS lesion size was evaluated by linear regression analysis. A p < .05 was considered statistically significant. RESULTS: Excellent interobserver agreement and intraobserver repeatability in the manual segmentation (all ICC > 0.98, p < .001) were achieved. The 95% limit of agreement was reduced from 11.23 cm2 for GT on DWI to 0.59 cm2 for prediction at an ADC threshold of 0.6 × 10-3 mm2/s combined with DWI. The segmentation performance of DLMs was improved with an overall DSC from 0.738 ± 0.214 on DWI to 0.971 ± 0.021 on an ADC threshold of 0.6 × 10-3 mm2/s combined with DWI. CONCLUSIONS: Combining an ADC threshold of 0.6 × 10-3 mm2/s with DWI reduces interobserver and inter-DLM difference and achieves best segmentation performance of AIS lesions using DLMs. KEY POINTS: • Higher Dice similarity coefficient (DSC) in predicting acute ischemic stroke lesions was achieved by ADC thresholds combined with DWI than by DWI alone (all p < .05). • DSC had a negative association with the ADC threshold in most sizes, both hospitals, and both observers (most p < .05) and a positive association with the stroke size in all ADC thresholds, both hospitals, and both observers (all p < .001). • An ADC threshold of 0.6 × 10-3 mm2/s eliminated the difference of DSC at any stroke size between observers or between hospitals (p = .07 to > .99).

Improving diagnosing performance for malignant parotid gland tumors using machine learning with multifeatures based on diffusion-weighted magnetic resonance imaging.

In this study, the performance of machine learning in classifying parotid gland tumors based on diffusion-related features obtained from the parotid gland tumor, the peritumor parotid gland, and the contralateral parotid gland was evaluated. Seventy-eight patients participated in this study and underwent magnetic resonance diffusion-weighted imaging. Three regions of interest, including the parotid gland tumor, the peritumor parotid gland, and the contralateral parotid gland, were manually contoured for 92 tumors, including 20 malignant tumors (MTs), 42 Warthin tumors (WTs), and 30 pleomorphic adenomas (PMAs). We recorded multiple apparent diffusion coefficient (ADC) features and applied a machine-learning method with the features to classify the three types of tumors. With only mean ADC of tumors, the area under the curve of the classification model was 0.63, 0.85, and 0.87 for MTs, WTs, and PMAs, respectively. The performance metrics were improved to 0.81, 0.89, and 0.92, respectively, with multiple features. Apart from the ADC features of parotid gland tumor, the features of the peritumor and contralateral parotid glands proved advantageous for tumor classification. Combining machine learning and multiple features provides excellent discrimination of tumor types and can be a practical tool in the clinical diagnosis of parotid gland tumors.

Multi-shot Diffusion-Weighted MRI With Multiplexed Sensitivity Encoding (MUSE) in the Assessment of Active Inflammation in Crohn's Disease.

BACKGROUND: Single-shot diffusion-weighted imaging (ssDWI) has been shown useful for detecting active bowel inflammation in Crohn's disease (CD) without MRI contrast. However, ssDWI suffers from geometric distortion and low spatial resolution. PURPOSE: To compare conventional ssDWI with higher-resolution ssDWI (HR-ssDWI) and multi-shot DWI based on multiplexed sensitivity encoding (MUSE-DWI) for evaluating bowel inflammation in CD, using contrast-enhanced MR imaging (CE-MRI) as the reference standard. STUDY TYPE: Prospective. SUBJECTS: Eighty nine patients with histological diagnosis of CD from previous endoscopy (55 male/34 female, age: 17-69 years). FIELD STRENGTH/SEQUENCES: ssDWI (2.7 mm × 2.7 mm), HR-ssDWI (1.8 mm × 1.8 mm), MUSE-DWI (1.8 mm × 1.8 mm) based on echo-planar imaging, T2-weighted imaging, and CE-MRI sequences, all at 1.5 T. ASSESSMENT: Five raters independently evaluated the tissue texture conspicuity, geometry accuracy, minimization of artifacts, diagnostic confidence, and overall image quality using 5-point Likert scales. The diagnostic performance (sensitivity, specificity and accuracy) of each DWI sequences was assessed on per-bowel-segment basis. STATISTICAL TESTS: Inter-rater agreement for qualitative evaluation of each parameter was measured by the intra-class correlation coefficient (ICC). Paired Wilcoxon signed-rank tests were performed to evaluate the statistical significance of differences in qualitative scoring between DWI sequences. A P value <0.05 was considered to be statistically significant. RESULTS: Tissue texture conspicuity, geometric distortions, and overall image quality were significantly better for MUSE-DWI than for ssDWI and HR-ssDWI with good agreement among five raters (ICC: 0.70-0.89). HR-ssDWI showed significantly poorer performance to ssDWI and MUSE-DWI for all qualitative scores and had the worst diagnostic performance (sensitivity of 57.0% and accuracy of 87.3%, with 36 undiagnosable cases due to severe artifacts). MUSE-DWI showed significantly higher sensitivity (97.5% vs. 86.1%) and accuracy (98.9% vs. 95.1%) than ssDWI for detecting bowel inflammation. DATA CONCLUSION: MUSE-DWI was advantageous in assessing bowel inflammation in CD, resulting in improved spatial resolution and image quality. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 2.

Image-based diagnosis of residual or recurrent nasopharyngeal carcinoma may be a phantom tumor phenomenon.

ABSTRACT: Some nasopharyngeal carcinoma (NPC) patients may present convincing radiological evidence mimicking residual or recurrent tumor after radiotherapy. However, by means of biopsies and long term follow-up, the radiologically diagnosed residuals/recurrences are not always what they appear to be. We report our experience on this "phantom tumor" phenomenon. This may help to avoid the unnecessary and devastating re-irradiation subsequent to the incorrect diagnosis.In this longitudinal cohort study, we collected 19 patients of image-based diagnosis of residual/recurrent NPC during the period from Feb, 2010 to Nov. 2016, and then observed them until June, 2019. They were subsequently confirmed to have no residual/recurrent lesions by histological or clinical measures. Image findings and pathological features were analyzed.Six patients showed residual tumors after completion of radiotherapy and 13 were radiologically diagnosed to have recurrences based on magnetic resonance imaging (MRI) criteria 6 to 206 months after radiotherapy. There were 3 types of image patterns: extensive recurrent skull base lesions (10/19); a persistent or residual primary lesion (3/19); lesions both in the nasopharynx and skull base (6/19). Fourteen patients had biopsy of the lesions. The histological diagnoses included necrosis/ inflammation in 10 (52.7%), granulation tissue with inflammation in 2, and reactive epithelial cell in 1. Five patients had no pathological proof and were judged to have no real recurrence/residual tumor based on the absence of detectable plasma EB virus DNA and subjective judgment. These 5 patients have remained well after an interval of 38-121 months without anti-cancer treatments.Image-based diagnosis of residual or recurrent nasopharyngeal carcinoma may be unreliable. False positivity, the "phantom tumor phenomenon", is not uncommon in post-radiotherapy MRI. This is particularly true if the images show extensive skull base involvement at 5 years or more after completion of radiotherapy. MRI findings compatible with NPC features must be treated as a real threat until proved otherwise. However, the balance between under- and over-diagnosis must be carefully sought. Without a pathological confirmation, the diagnosis of residual or recurrent NPC must be made taking into account physical examination results, endoscopic findings and Epstein-Barr virus viral load. A subjective medical judgment is needed based on clinical and laboratory data and the unique anatomic complexities of the nasopharynx.

Quantifying lumbar vertebral perfusion by a Tofts model on DCE-MRI using segmental versus aortic arterial input function.

The purpose of this study was to investigate the influence of arterial input function (AIF) selection on the quantification of vertebral perfusion using axial dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). In this study, axial DCE-MRI was performed on 2 vertebrae in each of eight healthy volunteers (mean age, 36.9 years; 5 men) using a 1.5-T scanner. The pharmacokinetic parameters Ktrans, ve, and vp, derived using a Tofts model on axial DCE-MRI of the lumbar vertebrae, were evaluated using various AIFs: the population-based aortic AIF (AIF_PA), a patient-specific aortic AIF (AIF_A) and a patient-specific segmental arterial AIF (AIF_SA). Additionally, peaks and delay times were changed to simulate the effects of various AIFs on the calculation of perfusion parameters. Nonparametric analyses including the Wilcoxon signed rank test and the Kruskal-Wallis test with a Dunn-Bonferroni post hoc analysis were performed. In simulation, Ktrans and ve increased as the peak in the AIF decreased, but vp increased when delay time in the AIF increased. In humans, the estimated Ktrans and ve were significantly smaller using AIF_A compared to AIF_SA no matter the computation style (pixel-wise or region-of-interest based). Both these perfusion parameters were significantly greater using AIF_SA compared to AIF_A.

Classification of parotid gland tumors by using multimodal MRI and deep learning.

Various MRI sequences have shown their potential to discriminate parotid gland tumors, including but not limited to T2 -weighted, postcontrast T1 -weighted, and diffusion-weighted images. In this study, we present a fully automatic system for the diagnosis of parotid gland tumors by using deep learning methods trained on multimodal MRI images. We used a two-dimensional convolution neural network, U-Net, to segment and classify parotid gland tumors. The U-Net model was trained with transfer learning, and a specific design of the batch distribution optimized the model accuracy. We also selected five combinations of MRI contrasts as the input data of the neural network and compared the classification accuracy of parotid gland tumors. The results indicated that the deep learning model with diffusion-related parameters performed better than those with structural MR images. The performance results (n = 85) of the diffusion-based model were as follows: accuracy of 0.81, 0.76, and 0.71, sensitivity of 0.83, 0.63, and 0.33, and specificity of 0.80, 0.84, and 0.87 for Warthin tumors, pleomorphic adenomas, and malignant tumors, respectively. Combining diffusion-weighted and contrast-enhanced T1 -weighted images did not improve the prediction accuracy. In summary, the proposed deep learning model could classify Warthin tumor and pleomorphic adenoma tumor but not malignant tumor.

Imaging quality of PROPELLER diffusion-weighted MR imaging and its diagnostic performance in distinguishing pleomorphic adenomas from Warthin tumors of the parotid gland.

The aim of this study was to evaluate the imaging quality and diagnostic performance of fast spin echo diffusion-weighted imaging with periodically rotated overlapping parallel lines with enhanced reconstruction (FSE-PROP-DWI) in distinguishing parotid pleomorphic adenoma (PMA) from Warthin tumor (WT). This retrospective study enrolled 44 parotid gland tumors from 34 patients, including 15 PMAs and 29 WTs with waived written informed consent. All participants underwent 1.5 T diffusion-weighted imaging including FSE-PROP-DWI and single-shot echo-planar diffusion-weighted imaging (SS-EP-DWI). After imaging resizing and registration among T2WI, FSE-PROP-DWI and SS-EP-DWI, imaging distortion was quantitatively analyzed by using the Dice coefficient. Signal-to-noise ratio and contrast-to-noise ratio were qualitatively evaluated. The mean apparent diffusion coefficient (ADC) of parotid gland tumors was calculated. Wilcoxon signed-rank test was used for paired comparison between FSE-PROP-DWI versus SS-EP-DWI. Mann-Whitney U test was used for independent group comparison between PMAs versus WTs. Diagnostic performance was evaluated by receiver operating characteristics curve analysis. P < 0.05 was considered statistically significant. The Dice coefficient was statistically significantly higher on FSE-PROP-DWI than SS-EP-DWI for both tumors (P < 0.005). Mean ADC was statistically significantly higher in PMAs than WTs on both FSE-PROP-DWI and SS-EP-DWI (P < 0.005). FSE-PROP-DWI and SS-EP-DWI successfully distinguished PMAs from WTs with an AUC of 0.880 and 0.945, respectively (P < 0.05). Sensitivity, specificity, positive predictive value, negative predictive value and accuracy in diagnosing PMAs were 100%, 69.0%, 62.5%, 100% and 79.5% for FSE-PROP-DWI, and 100%, 82.8%, 75%, 100% and 88.6% for SS-EP-DWI, respectively. FSE-PROP-DWI is useful to distinguish parotid PMAs from WTs with less distortion of tumors but lower AUC than SS-EP-DWI.

Low-grade albuminuria is associated with hearing loss in non-diabetic US males: A cross-sectional analysis of 1999-2004 national health and nutrition examination survey.

High levels of albuminuria have been demonstrated to associate with hearing loss in non-diabetic people, while the clinical impact of low-grade albuminuria has attracted less attention. This cross-sectional population-based study aimed to examine whether hearing loss in non-diabetic United States (US) adults is independently associated with low-grade albuminuria or reduced estimated glomeruli filtration rate (eGFR).A total of 2518 participants aged 20 to 69 years were selected from the US National Health and Nutritional Examination Survey database. Participants with diabetes or high-grade albuminuria were excluded. Hearing loss was assessed using low-frequency pure-tone average (LFPTA) thresholds (0.5, 1.0, 2.0 kHz) and high-frequency pure-tone average (HFPTA) thresholds (3.0, 4.0, 6.0, 8.0 kHz). Logistic and linear regression analyses were used to evaluate associations between renal function indicators and hearing loss.The median age of included participants was 37.4 years, and 55% of them were female. Multivariate analysis revealed that participants with urinary albumin-to-creatinine ratio (UACR) in the highest tertile had a significantly higher risk of hearing loss (OR, 1.79; 95% CI, 1.01-3.19) and higher HFPTA thresholds (ß: 2.23; SE: 0.77). Participants with eGFR <60 mL/min/1.73 m had higher LFPTA thresholds (ß: 4.31; SE: 1.79). After stratification by sex, a significant risk remained only for males in the highest UACR tertile, with 2.18 times the risk of hearing loss (95% CI, 1.06-4.48).Non-diabetic US males with low-grade albuminuria are at increased risk of hearing loss, independent of eGFR.

Comparative effectiveness of simultaneous integrated boost vs sequential intensity-modulated radiotherapy for oropharyngeal or hypopharyngeal cancer patients: A population-based propensity score-matched analysis.

There were 2 common radiotherapy dose fractionation strategies in head-and-neck cancer patients (such as oropharyngeal cancer [OPC] or hypopharyngeal cancer [HPC]) treated with radiotherapy: intensity-modulated radiotherapy using simultaneous integrated boost (IMRT-SIB) and sequential IMRT (IMRT-SEQ). There is a lack of high-level clinical evidence to compare IMRT-SIB vs IMRT-SEQ specifically for OPC or HPC patients. The present study investigated the survival outcomes of OPC or HPC patients receiving definite concurrent chemoradiotherapy (CCRT) with either IMRT-SIB or IMRT-SEQ via a population-based propensity score (PS)-based analysis.The localized stage OPC or HPC patients diagnosed between 2011 and 2015 were identified based on the Health and Welfare Data Science Center database in Taiwan. These patients received definitive CCRT with either IMRT-SIB or IMRT-SEQ. We constructed a PS-matched cohort (1:1 for IMRT-SIB vs IMRT-SEQ) to balance observable potential confounders. We compared the hazard ratio (HR) of death between IMRT-SIB and IMRT-SEQ during the entire follow-up period. We also evaluated other disease outcome or subgroups.Our study population constituted 200 patients with well balance in observed covariables. The HR of death when IMRT-SIB was compared to IMRT-SEQ was 1.23 (95% confidence interval 0.84-1.80, P = .29). The results were similar for other disease outcome or subgroups.We found the survival outcome might be comparable for those treated with IMRT-SIB vs those treated with IMRT-SEQ.

JOURNAL CLUB: The Warthin Tumor Score: A Simple and Reliable Method to Distinguish Warthin Tumors From Pleomorphic Adenomas and Carcinomas.

OBJECTIVE: The objective of this article is to propose a Warthin tumor (WT) score to distinguish WTs from other parotid tumors. MATERIALS AND METHODS: The study included 78 patients with 92 histologically proven parotid tumors, including 42 WTs, 30 pleomorphic adenomas (PMAs), and 20 carcinomas. Echo-planar DW images were acquired. The WT score, which comprised the mean apparent diffusion coefficient (ADCM) and the SD of the ADC (ADCSD) of tumors, patient age, and patient sex, was used to predict WTs. The diagnostic performance of the WT score was evaluated using ROC analyses. Statistical significance was denoted by p < 0.05. RESULTS: With the use of optimized criteria, including an ADCM less than or equal to 1.016 × 10-3 mm2/s (WT score, 1), an ADCSD less than or equal to 0.1171 × 10-3 mm2/s (WT score, 1), patient age older than 49 years (WT score, 1), and male sex (WT score, 1), a WT score greater than 2 had a sensitivity, specificity, positive negative value, negative predictive value, and accuracy of 85.7%, 100.0%, 100.0%, 89.3%, and 93.4%, respectively. CONCLUSION: The WT score allows parotid WTs to be distinguished from PMAs and carcinomas with high accuracy.

Vestibular schwannomas: Accuracy of tumor volume estimated by ice cream cone formula using thin-sliced MR images.

PURPOSE: We estimated the volume of vestibular schwannomas by an ice cream cone formula using thin-sliced magnetic resonance images (MRI) and compared the estimation accuracy among different estimating formulas and between different models. METHODS: The study was approved by a local institutional review board. A total of 100 patients with vestibular schwannomas examined by MRI between January 2011 and November 2015 were enrolled retrospectively. Informed consent was waived. Volumes of vestibular schwannomas were estimated by cuboidal, ellipsoidal, and spherical formulas based on a one-component model, and cuboidal, ellipsoidal, Linskey's, and ice cream cone formulas based on a two-component model. The estimated volumes were compared to the volumes measured by planimetry. Intraobserver reproducibility and interobserver agreement was tested. Estimation error, including absolute percentage error (APE) and percentage error (PE), was calculated. Statistical analysis included intraclass correlation coefficient (ICC), linear regression analysis, one-way analysis of variance, and paired t-tests with P < 0.05 considered statistically significant. RESULTS: Overall tumor size was 4.80 ± 6.8 mL (mean ±standard deviation). All ICCs were no less than 0.992, suggestive of high intraobserver reproducibility and high interobserver agreement. Cuboidal formulas significantly overestimated the tumor volume by a factor of 1.9 to 2.4 (P ≤ 0.001). The one-component ellipsoidal and spherical formulas overestimated the tumor volume with an APE of 20.3% and 29.2%, respectively. The two-component ice cream cone method, and ellipsoidal and Linskey's formulas significantly reduced the APE to 11.0%, 10.1%, and 12.5%, respectively (all P < 0.001). CONCLUSION: The ice cream cone method and other two-component formulas including the ellipsoidal and Linskey's formulas allow for estimation of vestibular schwannoma volume more accurately than all one-component formulas.

Proton change of parotid glands after gustatory stimulation examined by magnetic resonance imaging.

The aim of this study was to investigate proton changes of the parotid gland after gustatory stimulation by semi-quantitative parameters and an empirical mathematical model (EMM) using high-temporal-resolution, double-echo, echo-planar imaging (EPI). Approved by a local institutional review board, this study examined 20 parotid glands from 10 healthy volunteers (male:female = 6: 4; age ± standard deviation =35.1 ± 14.1 years) with written informed consent obtained. All participants underwent 1.5-T, double-echo EPI with gustatory stimulation. Semi-quantitative parameters, including maximal drop ratio (MDR), time to peak (TTP), drop slope (DS), recovery slope (RS) and recovery ratio (RR), were calculated. The effect of temporal resolution on parotid functional parameters was evaluated. An EMM comprising an output function ( Sot=Aoe-kot+B) and an input function ( Sint=Ain1-e-kint) was also applied to fit all dynamic curves. Kruskal-Wallis test, Wilcoxon test, linear regression analysis and goodness of fit were used for statistical analysis. p < 0.05 was considered to be statistically significant. The signal intensity dropped significantly after gustatory stimulation on the proton density (PD) image (p < 0.01). MDR was 8.26% in the PD image. MDR and RR were negatively associated with time interval, whereas DS and TTP were significantly positively associated with time interval (all p < 0.05). EMM parametric values derived from PD-time curves of parotid glands were 12.04 ± 6.81%, 6.43 ± 4.23 min-1 , 88.73 ± 6.18%, 8.41 ± 4.86 min-1 and 1.09 ± 1.35 for Ao , ko , B, Ain and kin , respectively. Semi-quantitative functional parameters and EMM parameters using high-temporal-resolution, double-echo EPI allow the quantification of parotid proton changes after gustatory stimulation.