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Background : Endometrial cancer is strongly associated with obesity, and tumors often harbor mutations in major cancer signaling pathways. To inform the integration of body composition into targeted therapy paradigms, this hypothesis-generating study explores the association between muscle mass, body fat, and tumor proteomics. Methods : We analyzed data from 113 patients in The Cancer Genome Atlas (TCGA) and Cancer Proteomic Tumor Analysis Consortium (CPTAC) cohorts and their corresponding abdominal CT scans. Among these patients, tumor proteomics data were available for 45 patients, and 133 proteins were analyzed. Adiposity and muscle components were assessed at the L3 vertebral level on the CT scans. Patients were stratified into tertiles of muscle and fat mass and categorized into three groups: high muscle/low adiposity, high muscle/high adiposity, and low muscle/all adiposities. Linear and Cox regression models were adjusted for study cohort, stage, histology type, age, race, and ethnicity. Results : Compared with the high-muscle/low-adiposity group, both the high-muscle/high-adiposity (HR = 4.3, 95% CI = 1.0-29.0) and low-muscle (HR = 4.4, 95% CI = 1.3-14.9) groups experienced higher mortality. Low muscle was associated with higher expression of phospho-4EBP1(T37 and S65), phospho-GYS(S641) and phospho-MAPK(T202/Y204) but lower expression of ARID1A, CHK2, SYK, LCK, EEF2, CYCLIN B1, and FOXO3A. High muscle/high adiposity was associated with higher expression of phospho-4EBP1 (T37), phospho-GYS (S641), CHK1, PEA15, SMAD3, BAX, DJ1, GYS, PKM2, COMPLEX II Subunit 30, and phospho-P70S6K (T389) but with lower expression of CHK2, CRAF, MSH6, TUBERIN, PR, ERK2, beta-CATENIN, AKT, and S6. Conclusions : These findings demonstrate an association between body composition and proteins involved in key cancer signaling pathways, notably the PI3K/AKT/MTOR, MAPK/ERK, cell cycle regulation, DNA damage response, and mismatch repair pathways. These findings warrant further validation and assessment in relation to prognosis and outcomes in these patients.

To improve awareness and understanding of cybersecurity threats to radiology practice and better equip healthcare practices to manage cybersecurity risks associated with medical imaging, this article reviews topics related to cybersecurity in healthcare, with emphasis on common vulnerabilities in radiology operations. This review is intended to assist radiologists and radiology administrators who are not information technology specialists to attain an updated overview of relevant cybersecurity concepts and concerns relevant to safe and effective practice of radiology and provides a succinct reference for individuals interested in learning about imaging-related vulnerabilities in healthcare settings. As cybersecurity incidents have become increasingly common in healthcare, we first review common cybersecurity threats in healthcare and provide updates on incidence of healthcare data breaches, with emphasis on the impact to radiology. Next, we discuss practical considerations on how to respond to a healthcare data breach, including notification and disclosure requirements, and elaborate on a variety of technical, organizational, and individual actions that can be adopted to minimize cybersecurity risks applicable to radiology professionals and administrators. While emphasis is placed on specific vulnerabilities within radiology workflow, many of the preventive or mitigating strategies are also relevant to cybersecurity within the larger digital healthcare arena. We anticipate that readers, upon completing this review article, will gain a better appreciation of cybersecurity issues relevant to radiology practice and be better equipped to mitigate cybersecurity risks associated with medical imaging., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

Objective: Obesity is a high-morbidity chronic condition and risk factor for multiple diseases that necessitate imaging. This study assesses the relationship between BMI and same-year utilization of CT and MR imaging in a large healthcare population., Methods: In this retrospective population-based study, all patients aged ≥18 years with a documented BMI in the multi-institutional Cosmos database were included. Cohorts were identified based on ≥1 documented BMI in 2021 within pre-defined ranges. For each cohort, we assessed the percentage of patients undergoing head, neck, chest, spine, or abdomen/pelvis CT and MR during the same year. Disease severity was quantified based on emergency department (ED) visits and mortality., Results: In our population of 49.6 million patients, same-year CT and MR utilization was 14.5 ±0.01% and 6.0±0.01%, respectively. The underweight cohort had the highest CT (25.8±0.1%) and MR (8.01 ± 0.05) imaging utilization. At high extremes of BMI (>50 kg/m2), CT utilization mildly increased (18.4±0.1%), but MR utilization decreased (5.3±0.04%). While morbidity differences may explain some BMI-utilization relationships, lower MR utilization in the BMI>50 cohort contrasts with higher age-adjusted mortality (1.8±0.03%) and ED utilization (32.4±0.1%) in this cohort relative to normal weight (1.5±0.01% and 25.7±0.02%, respectively)., Conclusion: Underweight patients had disproportionately high CT/MR utilization, and high extremes of BMI are associated with mildly higher CT and lower MR utilization than the normal weight cohort. The elevated mortality and ED utilization in severely obese patients contrasts with their lower MR imaging utilization. Our findings may assist public health efforts to accommodate obesity trends., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Dawod et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Objectives: Essential hypertension is a common chronic condition that can exacerbate or complicate various neurological diseases that may necessitate neuroimaging. Given growing medical imaging costs and the need to understand relationships between population blood pressure control and neuroimaging utilization, we seek to quantify the relationship between maximum blood pressure recorded in a given year and same-year utilization of neuroimaging CT or MR in a large healthcare population., Methods: A retrospective population-based cohort study was performed by extracting aggregate data from a multi-institutional dataset of patient encounters from 2016, 2018, and 2020 using an informatics platform (Cosmos) consisting of de-duplicated data from over 140 academic and non-academic health systems, comprising over 137 million unique patients. A population-based sample of all patients with recorded blood pressures of at least 50 mmHg DBP or 90 mmHg SBP were included. Cohorts were identified based on maximum annual SBP and DBP meeting or exceeding pre-defined thresholds. For each cohort, we assessed neuroimaging CT and MR utilization, defined as the percentage of patients undergoing ≥1 neuroimaging exam of interest in the same calendar year., Results: The multi-institutional population consisted of >38 million patients for the most recent calendar year analyzed, with overall utilization of 3.8-5.1% for CT and 1.5-2.0% for MR across the study period. Neuroimaging utilization increased substantially with increasing annual maximum BP. Even a modest BP increase to 140 mmHg systolic or 90 mmHg diastolic is associated with 3-4-fold increases in MR and 5-7-fold increases in CT same-year imaging compared to BP values below 120 mmHg / 80 mmHg., Conclusion: Higher annual maximum recorded blood pressure is associated with higher same-year neuroimaging CT and MR utilization rates. These observations are relevant to public health efforts on hypertension management to mitigate costs associated with growing imaging utilization., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Welch et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

There is an expanding body of literature that describes the application of deep learning and other machine learning and artificial intelligence methods with potential relevance to neuroradiology practice. In this article, we performed a literature review to identify recent developments on the topics of artificial intelligence in neuroradiology, with particular emphasis on large datasets and large-scale algorithm assessments, such as those used in imaging AI competition challenges. Numerous applications relevant to ischemic stroke, intracranial hemorrhage, brain tumors, demyelinating disease, and neurodegenerative/neurocognitive disorders were discussed. The potential applications of these methods to spinal fractures, scoliosis grading, head and neck oncology, and vascular imaging were also reviewed. The AI applications examined perform a variety of tasks, including localization, segmentation, longitudinal monitoring, diagnostic classification, and prognostication. While research on this topic is ongoing, several applications have been cleared for clinical use and have the potential to augment the accuracy or efficiency of neuroradiologists.

Viet Nam has a coastline of 3200 km with thousands of islands providing diverse habitats for benthic harmful algal species including species of Gambierdiscus. Some of these species produce ciguatera toxins, which may accumulate in large carnivore fish potentially posing major threats to public health. This study reports five species of Gambierdiscus from Vietnamese waters, notably G. australes, G. caribaeus, G. carpenteri, G. pacificus, and G. vietnamensis sp. nov. All species are identified morphologically by LM and SEM, and identifications are supported by molecular analyses of nuclear rDNA (D1-D3 and D8-D10 domains of LSU, SSU, and ITS1-5.8S-ITS2 region) based on cultured material collected during 2010-2021. Statistical analyses of morphometric measurements may be used to differentiate some species if a sufficiently large number of cells are examined. Gambierdiscus vietnamensis sp. nov. is morphologically similar to other strongly reticulated species, such as G. belizeanus and possibly G. pacificus; the latter species is morphologically indistinguishable from G. vietnamensis sp. nov., but they are genetically distinct, and molecular analysis is deemed necessary for proper identification of the new species. This study also revealed that strains denoted G. pacificus from Hainan Island (China) should be included in G. vietnamensis sp. nov., (© 2023 Phycological Society of America.)

Background and Purpose: A medical AI system's generalizability describes the continuity of its performance acquired from varying geographic, historical, and methodologic settings. Previous literature on this topic has mostly focused on "how" to achieve high generalizability (e.g., via larger datasets, transfer learning, data augmentation, model regularization schemes), with limited success. Instead, we aim to understand "when" the generalizability is achieved: Our study presents a medical AI system that could estimate its generalizability status for unseen data on-the-fly., Materials and Methods: We introduce a latent space mapping (LSM) approach utilizing Fréchet distance loss to force the underlying training data distribution into a multivariate normal distribution. During the deployment, a given test data's LSM distribution is processed to detect its deviation from the forced distribution; hence, the AI system could predict its generalizability status for any previously unseen data set. If low model generalizability is detected, then the user is informed by a warning message integrated into a sample deployment workflow. While the approach is applicable for most classification deep neural networks (DNNs), we demonstrate its application to a brain metastases (BM) detector for T1-weighted contrast-enhanced (T1c) 3D MRI. The BM detection model was trained using 175 T1c studies acquired internally (from the authors' institution) and tested using (1) 42 internally acquired exams and (2) 72 externally acquired exams from the publicly distributed Brain Mets dataset provided by the Stanford University School of Medicine. Generalizability scores, false positive (FP) rates, and sensitivities of the BM detector were computed for the test datasets., Results and Conclusion: The model predicted its generalizability to be low for 31% of the testing data (i.e., two of the internally and 33 of the externally acquired exams), where it produced (1) ∼13.5 false positives (FPs) at 76.1% BM detection sensitivity for the low and (2) ∼10.5 FPs at 89.2% BM detection sensitivity for the high generalizability groups respectively. These results suggest that the proposed formulation enables a model to predict its generalizability for unseen data., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Background: Patients' subjective experiences during clinical interactions may affect their engagement in healthcare, and better understanding of the issues patients consider most important may help improve service quality and patient-staff relationships. While diagnostic imaging is a growing component of healthcare utilization, few studies have quantitatively and systematically assessed what patients deem most relevant in radiology settings. To elucidate factors driving patient satisfaction in outpatient radiology, we derived quantitative models to identify items most predictive of patients' overall assessment of radiology encounters., Methods: Press-Ganey survey data (N = 69,319) collected over a 9-year period at a single institution were retrospectively analyzed, with each item response dichotomized as "favorable" or "unfavorable." Multiple logistic regression analyses were performed on 18 binarized Likert items to compute odds ratios (OR) for those question items significantly predicting Overall Rating of Care or Likelihood of Recommending. In a secondary analysis to identify topics more relevant to radiology than other encounter types, items significantly more predictive of concordant ratings in radiology compared to non-radiology visits were also identified., Results: Among radiology survey respondents, top predictors of Overall Rating and Likelihood of Recommending were items addressing patient concerns or complaints (OR 6.8 and 4.9, respectively) and sensitivity to patient needs (OR 4.7 and 4.5, respectively). When comparing radiology and non-radiology visits, the top items more predictive for radiology included unfavorable responses to helpfulness of registration desk personnel (OR 1.4-1.6), comfort of waiting areas (OR 1.4), and ease of obtaining an appointment at the desired time (OR 1.4)., Conclusions: Items related to patient-centered empathic communication were the most predictive of favorable overall ratings among radiology outpatients, while underperformance in logistical issues related to registration, scheduling, and waiting areas may have greater adverse impact on radiology than non-radiology encounters. Findings may offer potential targets for future quality improvement efforts., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Ajam et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Part of the Indo-Chinese peninsula and located on the northwest edge of the Coral Triangle in the South China Sea, the Vietnamese coastal zone is home to a wealthy marine biodiversity associated with the regional geological setting and history, which supports a large number of marine ecosystems along a subtropical to tropical gradient. The diversity of coastal benthic marine primary producers is also a key biological factor supporting marine biological diversity. The present review provides: (1) an updated checklist of the Vietnamese marine flora, (2) a review of molecular-assisted alpha taxonomic efforts, (3) an analysis of marine floral biodiversity spatial distribution nationally and regionally (South China Sea), (4) a review of the impact of anthropogenic and environmental stressors on the Vietnamese marine flora, and (5) the efforts developed in the last decade for its conservation. Based on the studies conducted since 2013 and the nomenclatural changes that occurred during this period, an updated checklist of benthic marine algae and seagrasses consisted in a new total of 878 species, including 439 Rhodophyta, 156 Ochrophyta, 196 Chlorophyta, 87 Cyanobacteria, and 15 phanerogam seagrasses. This update contains 54 new records and 5 new species of macroalgae. The fairly poor number of new records and new species identified in the last 10 years in a "mega-diverse" country can be largely attributed to the limited efforts in exploring algal biodiversity and the limited use of genetic tools, with only 25.4% (15 species) of these new records and species made based on molecular-assisted alpha taxonomy. The South Central Coast supports the highest species diversity of marine algae, which coincides with the largest density of coral reefs along the Vietnamese coast. Vietnam holds in the South China Sea one of the richest marine floras, imputable to the country's geographical, geological, and climatic settings. However, Vietnam marine floral biodiversity is under critical threats examined here, and current efforts are insufficient for its conservation. A methodical molecular-assisted re-examination of Vietnam marine floral biodiversity is urgently needed, complemented with in-depth investigations of the main threats targeting marine flora and vulnerable taxa, and finally, conservation measures should be urgently implemented.

Background and Purpose: Protocolling, the process of determining the most appropriate acquisition parameters for an imaging study, is time-consuming and produces variable results depending on the performing physician. The purpose of this study was to assess the potential of an artificial intelligence-based semiautomated tool in reducing the workload and decreasing unwarranted variation in the protocolling process., Materials and Methods: We collected 19,721 MR imaging brain examinations at a large academic medical center. Criterion standard labels were created using physician consensus. A model based on the Long Short-Term Memory network was trained to predict the most appropriate protocol for any imaging request. The model was modified into a clinical decision support tool in which high-confidence predictions, determined by the values the model assigns to each possible choice, produced the best protocol automatically and low confidence predictions provided a shortened list of protocol choices for review., Results: The model achieved 90.5% accuracy in predicting the criterion standard labels and demonstrated higher agreement than the original protocol assignments, which achieved 85.9% accuracy (κ = 0.84 versus 0.72, P value < .001). As a clinical decision support tool, the model automatically assigned 70% of protocols with 97.3% accuracy and, for the remaining 30% of examinations, achieved 94.7% accuracy when providing the top 2 protocols., Conclusions: Our model achieved high accuracy on a standard based on physician consensus. It showed promise as a clinical decision support tool to reduce the workload by automating the protocolling of a sizeable portion of examinations while maintaining high accuracy for the remaining examinations., (© 2023 by American Journal of Neuroradiology.)

Background: Magnetic resonance elastography (MRE) is an imaging technique that can noninvasively assess the shear properties of the intervertebral disc (IVD). Unlike the standard gradient recalled echo (GRE) MRE technique, a spin-echo echo-planar imaging (SE-EPI) sequence has the potential to improve imaging efficiency and patient compliance., Purpose: To validate the use of an SE-EPI sequence for MRE of the IVD compared against the standard GRE sequence., Study Type: Cross-over., Subjects: Twenty-eight healthy volunteers (15 males and 13 females, age range: 19-55)., Field Strength/sequence: 3 T; GRE, SE-EPI with breath holds (SE-EPI-BH) and SE-EPI with free breathing (SE-EPI-FB) MRE sequences., Assessment: MRE-derived shear stiffnesses were calculated via principal frequency analysis. SE-EPI derived shear stiffness and octahedral shear strain signal-to-noise ratios (OSS-SNR) were compared against those derived using the GRE sequence. The reproducibility and repeatability of SE-EPI stiffness measurements were determined. Shear stiffness was evaluated in the nucleus pulposus (NP) and annulus fibrosus (AF) regions of the disc. Scan times between sequences were compared., Statistical Tests: Linear mixed models, Bland-Altman plots, and Lin's concordance correlation coefficients (CCCs) were used with P < 0.05 considered statistically significant., Results: Good correlation was observed between shear stiffnesses derived from the SE-EPI sequences with those derived from the GRE sequence with CCC values greater than 0.73 and 0.78 for the NP and AF regions, respectively. OSS-SNR was not significantly different between GRE and SE-EPI sequences (P > 0.05). SE-EPI sequences generated highly reproducible and repeatable stiffness measurements with CCC values greater than 0.97 in the NP and AF regions and reduced scan time by at least 51% compared to GRE. SE-EPI-BH and SE-EPI-FB stiffness measurements were similar with CCC values greater than 0.98 for both regions., Data Conclusion: SE-EPI-based MRE-derived stiffnesses were highly reproducible and repeatable and correlated with current standard GRE MRE-derived stiffness estimates while reducing scan times., Level of Evidence: 1 TECHNICAL EFFICACY STAGE: 1., (© 2022 International Society for Magnetic Resonance in Medicine.)

Rationale and Objectives: Evaluate trends and demographic predictors of imaging utilization at a university-affiliated health system., Materials and Methods: In this single-institution retrospective study, per capita estimates of imaging utilization among patients active in the health system were computed by cross-referencing all clinical encounters (2004-2016) for 1,628,980 unique patients with a listing of 6,157,303 diagnostic radiology encounters. Time trends in imaging utilization and effects of gender, race/ethnicity, and age were assessed, with subgroup analyses performed by imaging modality. Utilization was analyzed as both a continuous and binary outcome variable., Results: Over 13 years, total diagnostic exams rose 6.8% a year (285,947-622,196 exams per annum), while the active population size grew 7.0% a year (244,238-543,290 active patients per annum). Per capita utilization peaked in 2007 at 1.33 studies/patient/year before dropping to 1.06 from 2011 to 2015. Latest per capita utilization was 0.22 for computed tomography, 0.10 for MR, 0.20 for US, 0.03 for NM, 0.51 for radiography, and 0.07 for mammography. Over the study period, ultrasound utilization doubled, whereas NM and radiography utilization decreased. computed tomography, MR, and mammography showed no significant net change. Univariate analysis of utilization as a continuous variable showed statistically significant effects of gender, race/ethnicity, and age (P < 0.0001), with utilization higher in males and Blacks and lower in Asian/Pacific Islanders and Hispanics. Utilization increased with age, except for a decline after age 75. Many of the effects of age, gender, and race/ethnicity were also found when analyzing the binarized utilization variable., Conclusions: Although absolute counts of imaging studies more than doubled, the net change in per capita utilization over the study period was minimal. Variations in utilization across age, gender, and race/ethnicity may reflect differential health needs and/or access disparities, warranting future studies., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Seagrass meadows provide valuable ecosystem services but are fragile and threatened ecosystems all over the world. This review highlights the current advances in seagrass research from Viet Nam. One goal is to support decision makers in developing science-based conservation strategies. In recent years, several techniques were applied to estimate the size of seagrass meadows. Independent from the method used, there is an alarming decline in the seagrass area in almost all parts of Viet Nam. Since 1990, a decline of 46.5% or 13,549 ha was found. Only in a few protected and difficult-to-reach areas was an increase observed. Conditions at those sites could be investigated in more detail to make suggestions for conservation and recovery of seagrass meadows. Due to their lifestyle and morphology, seagrasses take up compounds from their environment easily. Phytoremediation processes of Thalassia hemprichii and Enhalus acoroides are described exemplarily. High accumulation of heavy metals dependent on their concentration in the environment in different organs can be observed. On the one hand, seagrasses play a role in phytoremediation processes in polluted areas; on the other hand, they might suffer at high concentrations, and pollution will contribute to their overall decline. Compared with the neighboring countries, the total C org stock from seagrass beds in Viet Nam was much lower than in the Philippines and Indonesia but higher than that of Malaysia and Myanmar. Due to an exceptionally long latitudinal coastline of 3,260 km covering cool to warm water environments, the seagrass species composition in Viet Nam shows a high diversity and a high plasticity within species boundaries. This leads to challenges in taxonomic issues, especially with the Halophila genus, which can be better deduced from genetic diversity/population structures of members of Hydrocharitaceae. Finally, the current seagrass conservation and management efforts in Viet Nam are presented and discussed. Only decisions based on the interdisciplinary cooperation of scientists from all disciplines mentioned will finally lead to conserve this valuable ecosystem for mankind and biodiversity., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Nguyen, Phan, Cao, Nguyen Nhat, Nguyen, Nguyen, Lau, Hoang, Nguyen-Thi, Nguyen, Dao, Teichberg and Papenbrock.)

The detection of brain metastases (BM) in their early stages could have a positive impact on the outcome of cancer patients. The authors previously developed a framework for detecting small BM (with diameters of <15 mm) in T1-weighted contrast-enhanced 3D magnetic resonance images (T1c). This study aimed to advance the framework with a noisy-student-based self-training strategy to use a large corpus of unlabeled T1c data. Accordingly, a sensitivity-based noisy-student learning approach was formulated to provide high BM detection sensitivity with a reduced count of false positives. This paper (1) proposes student/teacher convolutional neural network architectures, (2) presents data and model noising mechanisms, and (3) introduces a novel pseudo-labeling strategy factoring in the sensitivity constraint. The evaluation was performed using 217 labeled and 1247 unlabeled exams via two-fold cross-validation. The framework utilizing only the labeled exams produced 9.23 false positives for 90% BM detection sensitivity, whereas the one using the introduced learning strategy led to ~9% reduction in false detections (i.e., 8.44). Significant reductions in false positives (>10%) were also observed in reduced labeled data scenarios (using 50% and 75% of labeled data). The results suggest that the introduced strategy could be utilized in existing medical detection applications with access to unlabeled datasets to elevate their performances.

The emergence of the COVID-19 pandemic over a relatively brief interval illustrates the need for rapid data-driven approaches to facilitate clinical decision making. We examined a machine learning process to predict inpatient mortality among COVID-19 patients using clinical and chest radiographic data. Modeling was performed with a de-identified dataset of encounters prior to widespread vaccine availability. Non-imaging predictors included demographics, pre-admission clinical history, and past medical history variables. Imaging features were extracted from chest radiographs by applying a deep convolutional neural network with transfer learning. A multi-layer perceptron combining 64 deep learning features from chest radiographs with 98 patient clinical features was trained to predict mortality. The Local Interpretable Model-Agnostic Explanations (LIME) method was used to explain model predictions. Non-imaging data alone predicted mortality with an ROC-AUC of 0.87 ± 0.03 (mean ± SD), while the addition of imaging data improved prediction slightly (ROC-AUC: 0.91 ± 0.02). The application of LIME to the combined imaging and clinical model found HbA1c values to contribute the most to model prediction (17.1 ± 1.7%), while imaging contributed 8.8 ± 2.8%. Age, gender, and BMI contributed 8.7%, 8.2%, and 7.1%, respectively. Our findings demonstrate a viable explainable AI approach to quantify the contributions of imaging and clinical data to COVID mortality predictions.

Objective: To help quantify the potential microeconomic impact of patient satisfaction in radiology, we tested the hypothesis that patient volume trends reflect patient satisfaction trends in outpatient magnetic resonance imaging (MRI)., Methods: Patient visits (N = 39,595) at distinct outpatient MRI sites within a university-affiliated hospital system during a 1-year period were retrospectively analyzed. Individual sites were grouped as having "decreasing," "stable," or "increasing" volume using an average quarterly volume change threshold of 5%. Based on Press Ganey outpatient services surveys, changes in satisfaction scores from the baseline quarter were calculated. Mood's median tests were applied to assess statistical significance of differences in satisfaction score improvements among the three volume trend designations during the 3 post-baseline fiscal quarters., Results: Quarterly volume was stable at 6 sites, increased at 1 site (by 18%), and decreased at 2 sites (by 20%-24%). There was a statistically significant association between volume trend and net change in satisfaction scores for all 5 domains assessed on the Press Ganey survey: Overall assessment (P < 0.0001), Facilities (P = 0.026), Personal issues (P = 0.013), Registration (P = 0.0004), and Test or treatment (P < 0.0001), with median score changes generally higher at facilities with higher volume trends., Discussion: It can be inferred that patient satisfaction drives volume in this scenario, whereas the converse relationship of volume adversely affecting satisfaction is not observed. Patient satisfaction and volume at MRI sites are interrelated, and patient experiences or perceptions of quality may influence decisions regarding what imaging sites are preferentially utilized., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Early detection of brain metastases (BM) is one of the determining factors for the successful treatment of patients with cancer; however, the accurate detection of small BM lesions (< 15 mm) remains a challenging task. We previously described a framework for the detection of small BM in single-sequence gadolinium-enhanced T1-weighted 3D MRI datasets. It combined classical image processing (IP) with a dedicated convolutional neural network, taking approximately 30 s to process each dataset due to computation-intensive IP stages. To overcome the speed limitation, this study aims to reformulate the framework via an augmented pair of CNNs (eliminating the IP) to reduce the processing times while preserving the BM detection performance. Our previous implementation of the BM detection algorithm utilized Laplacian of Gaussians (LoG) for the candidate selection portion of the solution. In this study, we introduce a novel BM candidate detection CNN (cdCNN) to replace this classical IP stage. The network is formulated to have (1) a similar receptive field as the LoG method, and (2) a bias for the detection of BM lesion loci. The proposed CNN is later augmented with a classification CNN to perform the BM detection task. The cdCNN achieved 97.4% BM detection sensitivity when producing 60 K candidates per 3D MRI dataset, while the LoG achieved 96.5% detection sensitivity with 73 K candidates. The augmented BM detection framework generated on average 9.20 false-positive BM detections per patient for 90% sensitivity, which is comparable with our previous results. However, it processes each 3D data in 1.9 s, presenting a 93.5% reduction in the computation time., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

The genus Halophila shows the highest species diversity within the seagrass genera. Southeast Asian countries where several boundary lines exist were considered as the origin of seagrasses. We hypothesize that the boundary lines, such as Wallace's and Lydekker's Lines, may act as marine geographic barriers to the population structure of Halophila major. Seagrass samples were collected at three islands in Vietnamese waters and analyzed by the molecular maker ITS. These sequences were compared with published ITS sequences from seagrasses collected in the whole region of interest. In this study, we reveal the haplotype and nucleotide diversity, linking population genetics, phylogeography, phylogenetics and estimation of relative divergence times of H. major and other members of the Halophila genus. The morphological characters show variation. The results of the ITS marker analysis reveal smaller groups of H. major from Myanmar, Shoalwater Bay (Australia) and Okinawa (Japan) with high supporting values. The remaining groups including Sri Lanka, Viet Nam, the Philippines, Thailand, Malaysia, Indonesia, Two Peoples Bay (Australia) and Tokushima (Japan) showed low supporting values. The Wallacea region shows the highest haplotype and also nucleotide diversity. Non-significant differences were found among regions, but significant differences were presented among populations. The relative divergence times between some members of section Halophila were estimated 2.15-6.64 Mya., Competing Interests: The authors have declared that no competing interests exist.

Objective: To characterize predictors of patient satisfaction in outpatient radiology, we examined whether patient satisfaction differs across radiology modalities and demographic groups. Methods: A random sampling of Press-Ganey outpatient services surveys for radiology and non-radiology visits from September 2008 to September 2017 were retrospectively analyzed. Composite scores averaged across all Likert items were analyzed as both a continuous variable and a dichotomous variable of dissatisfaction (defined as ≤3 on the 5-point scale). Results: Among 9983 radiology surveys, mammography had higher composite scores than MRI, CT, radiography, US, and NM/PET (p < 0.001) and lower dissatisfaction (3.9%) than CT (6.7%), MRI (7.3%), and radiography (8.2%). Low-scoring responses were most common in the Facilities domain (7.8%) and least common in Overall Assessment (3.8%). Satisfaction metrics were lowest for ages 20-29 and highest for ages 70-79. Lower dissatisfaction rates were seen among Hispanics (3%) and whites (6%), compared to blacks (10%) and Asians (18%). Conclusion: Significant differences in patient satisfaction were found across imaging modalities and demographic variables. Further investigations to identify contributing factors may help improve patient experiences., Competing Interests: Declaration of Conflicting Interests: The authors declare no conflict of interest. Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2021.)

Data-driven approaches have great potential to shape future practices in radiology. The most straightforward strategy to obtain clinically accurate models is to use large, well-curated and annotated datasets. However, patient privacy constraints, tedious annotation processes, and the limited availability of radiologists pose challenges to building such datasets. This review details model training strategies in scenarios with limited data, insufficiently labeled data, and/or limited expert resources. This review discusses strategies to enlarge the data sample, decrease the time burden of manual supervised labeling, adjust the neural network architecture to improve model performance, apply semisupervised approaches, and leverage efficiencies from pretrained models. Keywords: Computer-aided Detection/Diagnosis, Transfer Learning, Limited Annotated Data, Augmentation, Synthetic Data, Semisupervised Learning, Federated Learning, Few-Shot Learning, Class Imbalance., Competing Interests: Disclosures of Conflicts of Interest: S.C. No relevant relationships. X.V.N. Equity ownership in and dividends from multiple publicly traded companies that may be considered “broadly relevant to artificial intelligence” (NVIDIA, Amazon, Microsoft, AMD, Apple). L.R.F. Patents issued, no royalties: Radiographic marker that displays upright angle on portable radiographs (Patent no. 9,541,822 B2) and multiscale universal CT window (Patent no. 8,406,493 B2); royalties from Springer (combat radiology, unrelated); research agreement with Philips. L.M.P. associate editor of Radiology: Artificial Intelligence., (2021 by the Radiological Society of North America, Inc.)

We deem a computer to exhibit artificial intelligence (AI) when it performs a task that would normally require intelligent action by a human. Much of the recent excitement about AI in the medical literature has revolved around the ability of AI models to recognize anatomy and detect pathology on medical images, sometimes at the level of expert physicians. However, AI can also be used to solve a wide range of noninterpretive problems that are relevant to radiologists and their patients. This review summarizes some of the newer noninterpretive uses of AI in radiology., (Copyright © 2020 The Association of University Radiologists. All rights reserved.)

Artificial intelligence (AI) systems play an increasingly important role in all parts of the imaging chain, from image creation to image interpretation to report generation. In order to responsibly manage radiology AI systems and make informed purchase decisions about them, radiologists must understand the underlying principles of AI. Our task force was formed by the Radiology Research Alliance (RRA) of the Association of University Radiologists to identify and summarize a curated list of current educational materials available for radiologists., (Copyright © 2021. Published by Elsevier Inc.)

Identification of species of the dinoflagellate genus Ostreopsis is difficult because several species have been poorly described, others misidentified in the literature, and the type species, O. siamensis, has not been described by contemporary taxonomic methods. In the present study, it is argued that Ostreopsis sp. 6 as described by previous authors is similar to the type species, and we offer an emended description of O. siamensis by LM, SEM, and molecular analyses of nuclear LSU and ITS rDNA based on material collected a few hundred kilometers from the type locality in the Gulf of Thailand and along the Vietnamese east coast. Ostreopsis siamensis is genetically different from the species reported as O. cf. siamensis in the literature and the latter should be described as a separate species. It is also concluded that with the poor knowledge of the morphological variability of many species of Ostreopsis, O. siamensis may not be distinguished from other similar-sized species by its morphological features, and hence molecular data are needed for reliable identification. The species Ostreopsis lenticularis and Ostreopsis cf. ovata were also found and described., (© 2021 Phycological Society of America.)

Objective: Incidental detection of thyroid cancers has been proposed as a cause of thyroid cancer increases over past decades, but few studies assess the impact of imaging utilization on thyroid cancer incidence. This study quantifies neck CT prevalence and its relationship with thyroid cancer incidence as a function of age, sex and race., Design and Patients: Medical records of over 1 million patients at our institution were retrospectively analysed to quantify neck CT prevalence from 2004 to 2011 (study period). A national cancer database was used to compute thyroid cancer incidences over the study period and a reference period (1974-81) and to calculate change in thyroid incidence between the two periods. Both populations were partitioned into demographic subgroups of varying age, sex and race. Linear correlation between neck imaging and thyroid cancer incidence changes among subgroups was assessed using Pearson's correlation., Results: Neck CT imaging and change in thyroid cancer incidence varied across all examined demographic variables, particularly age. When stratifying by age, CT use correlated strongly with recent national thyroid cancer incidence (R = .97) and with 30-year change in thyroid cancer incidence (R = .87). Across all demographic subgroups, CT prevalence correlated strongly and positively with change in thyroid cancer incidence (R = .60), greater for whites (R = .60) and blacks (R = .70) than other races (R = .28)., Conclusion: Differences in neck CT usage strongly and positively correlates with the variation in thyroid cancer trends based on age, gender and race., (© 2021 John Wiley & Sons Ltd.)

Purpose: In mild cognitive impairment (MCI), identifying individuals at high risk for progressive cognitive deterioration can be useful for prognostication and intervention. This study quantitatively characterizes cognitive decline rates in MCI and tests whether volumetric data from baseline magnetic resonance imaging (MRI) can predict accelerated cognitive decline., Methods: The authors retrospectively examined Alzheimer Disease Neuroimaging Initiative data to obtain serial Mini-Mental Status Exam (MMSE) scores, diagnoses, and the following baseline MRI volumes: total intracranial volume, whole-brain and ventricular volumes, and volumes of the hippocampus, entorhinal cortex, fusiform gyrus, and medial temporal lobe. Subjects with <24 months or <4 measurements of MMSE data were excluded. Predictive modeling of fast cognitive decline (defined as >0.6/year) from baseline volumetric data was performed on subjects with MCI using a single hidden layer neural network., Results: Among 698 baseline MCI subjects, the median annual decline in the MMSE score was 1.3 for converters to dementia versus 0.11 for stable MCI (P<0.001). A 0.6/year threshold captured dementia conversion with 82% accuracy (sensitivity 79%, specificity 85%, area under the receiver operating characteristic curve 0.88). Regional volumes on baseline MRI predicted fast cognitive decline with a test accuracy of 71%., Discussion: An MMSE score decrease of >0.6/year is associated with MCI-to-dementia conversion and can be predicted from baseline MRI., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.)

Radiologists very frequently encounter incidental findings related to the thyroid gland. Given increases in imaging use over the past several decades, thyroid incidentalomas are increasingly encountered in clinical practice, and it is important for radiologists to be aware of recent developments with respect to workup and diagnosis of incidental thyroid abnormalities. Recent reporting and management guidelines, such as those from the American College of Radiology and American Thyroid Association, are reviewed along with applicable evidence in the literature. Trending topics, such as artificial intelligence approaches to guide thyroid incidentaloma workup, are also discussed., Competing Interests: Disclosure The authors have nothing to disclose., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Patient comfort is an important factor of a successful magnetic resonance (MR) examination, and improvements in the patient's MR scanning experience can contribute to improved image quality, diagnostic accuracy, and efficiency in the radiology department, and therefore reduced cost. Magnet designs that are more open and accessible, reduced auditory noise of MR examinations, light and flexible radiofrequency (RF) coils, and faster motion-insensitive imaging techniques can all significantly improve the patient experience in MR imaging. In this work, we review the design, development, and implementation of these physics and engineering approaches to improve patient comfort.

For many patients, numerous unpleasant features of the magnetic resonance imaging (MRI) experience such as scan duration, auditory noise, spatial confinement, and motion restrictions can lead to premature termination or low diagnostic quality of imaging studies. This article discusses practical, patient-oriented considerations that are helpful for radiologists contemplating ways to improve the MRI experience for patients. Patient friendly scanner properties are discussed, with an emphasis on literature findings of effectiveness in mitigating patient claustrophobia, other anxiety, or motion and on reducing scan incompletion rates or need for sedation. As shorter scanning protocols designed to answer specific diagnostic questions may be more practical and tolerable to the patient than a full-length standard-of-care examination, a few select protocol adjustments potentially useful for specific clinical settings are discussed. In addition, adjunctive devices such as audiovisual or other sensory aides that can be useful distractive approaches to reduce patient discomfort are considered. These modifications to the MRI scanning process not only allow for a more pleasant experience for patients, but they may also increase patient compliance and decrease patient movement to allow more efficient acquisition of diagnostic-quality images.

Periprocedural anxiety is a major cause of morbidity, particularly for interventional radiology procedures that often depend on conscious sedation. Management of anxiety and pain during image-guided procedures has traditionally relied on pharmacologic agents such as benzodiazepines and opioids. Although generally safe, use of these medications risks adverse events, and newer noninvasive, nonpharmacologic techniques have evolved to address patient needs. In this review, we explore the roles of hypnosis, structured empathic attention, anodyne imagery, music, video glasses, and mobile applications in reducing procedural anxiety and pain with the goal of improving patient satisfaction, operational efficiency, and clinical outcomes.

Artificial intelligence, particularly deep learning, offers several possibilities to improve the quality or speed of image acquisition in magnetic resonance imaging (MRI). In this article, we briefly review basic machine learning concepts and discuss commonly used neural network architectures for image-to-image translation. Recent examples in the literature describing application of machine learning techniques to clinical MR image acquisition or postprocessing are discussed. Machine learning can contribute to better image quality by improving spatial resolution, reducing image noise, and removing undesired motion or other artifacts. As patients occasionally are unable to tolerate lengthy acquisition times or gadolinium agents, machine learning can potentially assist MRI workflow and patient comfort by facilitating faster acquisitions or reducing exogenous contrast dosage. Although artificial intelligence approaches often have limitations, such as problems with generalizability or explainability, there is potential for these techniques to improve diagnostic utility, throughput, and patient experience in clinical MRI practice.

Purpose: Our study investigates whether a machine-learning-based system can predict the rate of cognitive decline in mildly cognitively impaired patients by processing only the clinical and imaging data collected at the initial visit. Approach: We built a predictive model based on a supervised hybrid neural network utilizing a three-dimensional convolutional neural network to perform volume analysis of magnetic resonance imaging (MRI) and integration of nonimaging clinical data at the fully connected layer of the architecture. The experiments are conducted on the Alzheimer's Disease Neuroimaging Initiative dataset. Results: Experimental results confirm that there is a correlation between cognitive decline and the data obtained at the first visit. The system achieved an area under the receiver operator curve of 0.70 for cognitive decline class prediction. Conclusion: To our knowledge, this is the first study that predicts "slowly deteriorating/stable" or "rapidly deteriorating" classes by processing routinely collected baseline clinical and demographic data [baseline MRI, baseline mini-mental state examination (MMSE), scalar volumetric data, age, gender, education, ethnicity, and race]. The training data are built based on MMSE-rate values. Unlike the studies in the literature that focus on predicting mild cognitive impairment (MCI)-to-Alzheimer's disease conversion and disease classification, we approach the problem as an early prediction of cognitive decline rate in MCI patients., (© 2020 Society of Photo-Optical Instrumentation Engineers (SPIE).)

Patients undergoing MRI may experience fear, claustrophobia, or other anxiety manifestations due to the typically lengthy, spatially constrictive, and noisy MRI acquisition process and in some cases are not able to tolerate completion of the study. This article discusses several patient-centered aspects of radiology practice that emphasize interpersonal interactions. Patient education and prescan communication represent 1 way to increase patients' awareness of what to expect during MRI and therefore mitigate anticipatory anxiety. Some patient interaction strategies to promote relaxation or calming effects are also discussed. Staff teamwork and staff training in communication and interpersonal skills are also described, along with literature evidence of effectiveness with respect to patient satisfaction and productivity endpoints. Attention to how radiologists, nurses, technologists, and other members of the radiology team interact with patients before or during the MRI scan could improve patients' motivation and ability to cooperate with the MRI scanning process as well as their subjective perceptions of the quality of their care. The topics discussed in this article are relevant not only to MRI operations but also to other clinical settings in which patient anxiety or motion represent impediments to optimal workflow.

ACR RADPEER® is the leading method of radiologic peer review in the United States. The program has evolved since its inception in 2002 and was most recently updated in 2016. In 2018, a survey was sent to RADPEER participants to gauge the current state of the program and explore opportunities for continued improvement. A total of 26 questions were included, and more than 300 practices responded. In this report, the ACR RADPEER Committee authors summarize the survey results and discuss opportunities for future iterations of the RADPEER program., (Copyright © 2019 American College of Radiology. All rights reserved.)

Claustrophobia, other anxiety reactions, excessive motion, and other unanticipated patient events in magnetic resonance imaging (MRI) not only delay or preclude diagnostic-quality imaging but can also negatively affect the patient experience. In addition, by impeding MRI workflow, they may affect the finances of an imaging practice. This review article offers an overview of the various types of patient-related unanticipated events that occur in MRI, along with estimates of their frequency of occurrence as documented in the available literature. In addition, the financial implications of these events are discussed from a microeconomic perspective, primarily from the point of view of a radiology practice or hospital, although associated limitations and other economic viewpoints are also included. Efforts to minimize these unanticipated patient events can potentially improve not only patient satisfaction and comfort but also an imaging practice's operational efficiency and diagnostic capabilities.

Objective: To identify factors associated with increased proportion of early gastric cancer to total detected gastric cancer among patients undergoing diagnostic esophagogastroduodenoscopy., Methods: A nationwide survey was conducted across 6 central-type and 6 municipal-type Vietnamese hospitals. A questionnaire regarding annual esophagogastroduodenoscopy volume, esophagogastroduodenoscopy preparation, the use of image-enhanced endoscopy, and number of gastric cancer diagnosed in 2018 was sent to each hospital., Results: The total proportion of early gastric cancer was 4.0% (115/2857). Routine preparation with simethicone and the use of image-enhanced endoscopy were associated with higher proportion of early gastric cancer (OR 1.9, 95% CI: 1.1-3.2, p = 0.016; OR 2.7, 95% CI: 1.8-4.0, p < 0.001, respectively). Esophagogastroduodenoscopies performed at central-type hospitals were associated with higher proportion of early gastric cancer (OR 1.9, 95% CI: 1.1-3.2, p = 0.017). Esophagogastroduodenoscopies performed at hospitals with an annual volume of 30.000-60.000 were associated with higher proportion of early gastric cancer than those performed at hospitals with an annual volume of 10.000-<30.000 (OR 2.7, 95% CI: 1.6-4.8, p < 0.001) and with a volume of >60.000-100.000 (OR 2.7, 95% CI: 1.7-4.2, p < 0.001). Only four (33.3%) hospitals reported all endoscopic types of early gastric cancer., Conclusions: The detection of early gastric cancer is still challenging even for endoscopists working in regions with relatively high prevalence. The real-world evidence showed that endoscopic detection of early gastric cancer could potentially improve with simple adjustments of esophagogastroduodenoscopy protocols., Competing Interests: The authors declare no conflict of interests for this article., (Copyright © 2020 Duc T. Quach et al.)

Background: Laparoscopic-assisted endorectal pull-through (LAEPT) procedure in the management of high-type anorectal malformations (ARMs) was first introduced in 1998 and is quickly accepted worldwide. However, evidence on long-term outcomes of this technique is constrained. This study aims to evaluate the long-term outcomes of LAEPT for high-type ARMs in Vietnamese pediatrics. Materials and Methods: A longitudinal study was carried out from January 2009 to July 2014 in 56 patients <3 years old. Variables included age of operation, associated anomalies, type of fistula, the duration of hospital stay, complications, and long-term functional outcomes (Krickenbeck modified standards were used for children ≥3 years). Results: There were 56 patients including 48 males and 8 females. The mean age at operation was 3.7 months, the mean hospital stay was 4.6 days. The mean operative time was 76.7 minutes. The mean follow-up time was from 38 to 104 months (mean follow-up: 71.5 months). There were 46 (82.1%) patients having feeling of urge, 42 (75.0%) patients having capacity to verbalize, and 40 (71.4%) patients having hold the bowel movement. Conclusion: LAEPT is feasible, safe, and effective in the management of high-type ARMs.

Given the rapid pace at which modern radiology is evolving and the associated paradigm shifts in health care delivery, it is critical that radiologists adapt and constantly update the skills and knowledge required to practice safe, patient-centered care. The Association of University Radiologists-Radiology Research Alliance Lifelong Learning Task Force convened to explore the current status and future directions of lifelong learning in radiology and summarized its finding in this article. We review the various learning platforms and resources available to radiologists in their self-motivated and self-directed pursuit of lifelong learning. We also discuss the challenges and perceived barriers to lifelong learning and strategies to mitigate those barriers and optimize learning outcomes., (Copyright © 2019 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Purpose: Examine the cost of MRI operations before and after implementation of interpersonal skills training to reduce unanticipated patient-related events in an academic medical center., Methods: Teams at four MRI sites (two hospital-based, two freestanding) were trained in evidence-based communication skills in February to April 2015. Training was designed to enable staff members to help patients mobilize their innate coping skills in response to any distress they experienced during their MRI visit. Data were collected before training and afterward from January to June 2016. Staff reported the incidence of disruptive motion, sedation use, MRI delays, incomplete examinations, and no-shows. Cost and revenue associated with MRI operations and staff and physician costs were estimated using Medicare and private insurance rates and data from the US Bureau of Labor Statistics., Results: The study included 12,930 outpatient MRI visits. From baseline to follow-up, average monthly patient volume increased from 1,105 to 1,463 at hospital MRI sites and from 245 to 313 at freestanding MRI sites. Patient factors necessitating sedation or interfering with image progression or quality decreased from 9.0% to 5.5% at hospital sites and from 3.1% to 1.2% at freestanding sites. These changes translated into a reduction in operational costs of $4,600 per 1,000 scheduled patients and an increase in profit of $8,370 per 1,000 scheduled patients in hospital MRI sites, and a corresponding increase in operational costs of $1,570 per 1,000 scheduled-patients and an increase in profit of $12,800 per 1,000 scheduled patients in freestanding MRI sites., Conclusions: We found significant improvements in MRI operational efficiency after interpersonal skills team training, which were associated with reductions in costs and growth in revenue., (Copyright © 2018 American College of Radiology. Published by Elsevier Inc. All rights reserved.)

The λ ≈ 1 mm ( f = 288 GHz) interferometer for the Lithium Tokamak Experiment- β (LTX- β ) will use a chirped-frequency source and a centerstack-mounted retro-reflector mirror to provide electron line density measurements along a single radial chord at the midplane. The interferometer is unique in the use of a single source (narrow-band chirped-frequency interferometry) and a single beam splitter for separating and recombining the probe and reference beams. The current work provides a documentation of the interferometry hardware and evaluates the capabilities of the system as a far-forward collective scattering diagnostic. As such, the current optical setup is estimated to have a detection range of 0.4 ≲ k ⊥ ≲ 1.7 cm -1 , while an improved layout will extend the upper k ⊥ limit to ∼3 cm -1 . Measurements with the diagnostic on LTX are presented, showing interferometry results and scattered signal data. These diagnostics are expected to provide routine measurements on LTX- β for high frequency coherent density oscillations (e.g., Alfvénic modes during neutral beam injection) as well as for broadband turbulence.

The presence of a single nodal metastasis has significant prognostic and treatment implications for patients with head and neck cancer. This study aims to investigate whether dual-energy computed tomography (DECT)-derived iodine content and spectral attenuation curve analysis can improve detection of nodal metastasis in oropharyngeal carcinoma. Eight patients with newly diagnosed oropharyngeal squamous cell carcinoma and pathologically proven nodal metastatic disease (n = 13 metastatic nodes; n = 16 nonmetastatic nodes) who underwent contrast-enhanced DECT of the neck were retrospectively evaluated. DECT-derived iodine content (mg/mL) and monoenergetic attenuation values at 40 keV and 100 keV were obtained via circular regions of interest within metastatic and nonmetastatic cervical lymph nodes. Iodine content was significantly lower in metastatic nodes (0.96 ± 0.28 mg/mL) than in nonmetastatic nodes (1.65 ± 0.38 mg/mL; P = .002). Iodine spectral attenuation slope was significantly lower in metastatic nodes (1.33 ± 0.49 mg/mL) than in nonmetastatic nodes (1.91 ± 0.64 mg/mL; P = .015). A nodal iodine threshold of ≤1.3 mg/mL showed a sensitivity of 84.6% and a specificity of 75.0%, with an area under the curve of 0.839, P < .0001. At a threshold value of ≤1.95 for nodal spectral attenuation slope, an optimized specificity of 92.3% and specificity of 50.0% was achieved, with an area under the curve of 0.68 ( P = .049). DECT-derived quantitative iodine data and spectral attenuation curves may improve the diagnostic accuracy of computed tomography for nodal metastasis in patients with squamous cell carcinoma of the oropharynx., Competing Interests: Conflict of Interest: The authors have no conflict of interest to declare.