Publications by authors named "Seyedmehdi Payabvash"

98 Publications

Age-related topographic map of magnetic resonance diffusion metrics in neonatal brains.

Hum Brain Mapp 2022 May 23. Epub 2022 May 23.

Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut, USA.

Accelerated maturation of brain parenchyma close to term-equivalent age leads to rapid changes in diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) metrics of neonatal brains, which can complicate the evaluation and interpretation of these scans. In this study, we characterized the topography of age-related evolution of diffusion metrics in neonatal brains. We included 565 neonates who had MRI between 0 and 3 months of age, with no structural or signal abnormality-including 162 who had DTI scans. We analyzed the age-related changes of apparent diffusion coefficient (ADC) values throughout brain and DTI metrics (fractional anisotropy [FA] and mean diffusivity [MD]) along white matter (WM) tracts. Rate of change in ADC, FA, and MD values across 5 mm cubic voxels was calculated. There was significant reduction of ADC and MD values and increase of FA with increasing gestational age (GA) throughout neonates' brain, with the highest temporal rates in subcortical WM, corticospinal tract, cerebellar WM, and vermis. GA at birth had significant effect on ADC values in convexity cortex and corpus callosum as well as FA/MD values in corpus callosum, after correcting for GA at scan. We developed online interactive atlases depicting age-specific normative values of ADC (ages 34-46 weeks), and FA/MD (35-41 weeks). Our results show a rapid decrease in diffusivity metrics of cerebral/cerebellar WM and vermis in the first few weeks of neonatal age, likely attributable to myelination. In addition, prematurity and low GA at birth may result in lasting delay in corpus callosum myelination and cerebral cortex cellularity.
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http://dx.doi.org/10.1002/hbm.25956DOI Listing
May 2022

Bedside monitoring of hypoxic ischemic brain injury using low-field, portable brain magnetic resonance imaging after cardiac arrest.

Resuscitation 2022 May 11. Epub 2022 May 11.

Department of Neurology, Yale School of Medicine, New Haven, CT, USA.

Background: Assessment of brain injury severity is critically important after survival from cardiac arrest (CA). Recent advances in low-field MRI technology have permitted the acquisition of clinically useful bedside brain imaging. Our objective was to deploy a novel approach for evaluating brain injury after CA in critically ill patients at high risk for adverse neurological outcome.

Methods: This retrospective, single center study involved review of all consecutive portable MRIs performed as part of clinical care for CA patients between September 2020 and January 2022. Portable MR images were retrospectively reviewed by a blinded board-certified neuroradiologist (S.P.). Fluid-inversion recovery (FLAIR) signal intensities were measured in select regions of interest.

Results: We performed 22 low-field MRI examinations in 19 patients resuscitated from CA (68.4% male, mean [standard deviation] age, 51.8 [13.1] years). Twelve patients (63.2%) had findings consistent with HIBI on conventional neuroimaging radiology report. Low-field MRI detected findings consistent with HIBI in all of these patients. Low-field MRI was acquired at a median (interquartile range) of 78 (40-136) hours post-arrest. Quantitatively, we measured FLAIR signal intensity in three regions of interest, which were higher amongst patients with confirmed HIBI. Low-field MRI was completed in all patients without disruption of intensive care unit equipment monitoring and no safety events occurred.

Conclusion: In a critically ill CA population in whom MR imaging is often not feasible, low-field MRI can be deployed at the bedside to identify HIBI. Low-field MRI provides an opportunity to evaluate the time-dependent nature of MRI findings in CA survivors.
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http://dx.doi.org/10.1016/j.resuscitation.2022.05.002DOI Listing
May 2022

CT angiographic radiomics signature for risk stratification in anterior large vessel occlusion stroke.

Neuroimage Clin 2022 7;34:103034. Epub 2022 May 7.

Section of Neuroradiology, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States. Electronic address:

Background And Purpose: As "time is brain" in acute stroke triage, the need for automated prognostication tools continues to increase, particularly in rapidly expanding tele-stroke settings. We aimed to create an automated prognostication tool for anterior circulation large vessel occlusion (LVO) stroke based on admission CTA radiomics.

Methods: We automatically extracted 1116 radiomics features from the anterior circulation territory on admission CTAs of 829 acute LVO stroke patients who underwent mechanical thrombectomy in two academic centers. We trained, optimized, validated, and compared different machine-learning models to predict favorable outcome (modified Rankin Scale ≤ 2) at discharge and 3-month follow-up using four different input sets: "Radiomics", "Radiomics + Treatment" (radiomics, post-thrombectomy reperfusion grade, and intravenous thrombolysis), "Clinical + Treatment" (baseline clinical variables and treatment), and "Combined" (radiomics, treatment, and baseline clinical variables).

Results: For discharge outcome prediction, models were optimized/trained on n = 494 and tested on an independent cohort of n = 100 patients from Yale. Receiver operating characteristic analysis of the independent cohort showed no significant difference between best-performing Combined input models (area under the curve, AUC = 0.77) versus Radiomics + Treatment (AUC = 0.78, p = 0.78), Radiomics (AUC = 0.78, p = 0.55), or Clinical + Treatment (AUC = 0.77, p = 0.87) models. For 3-month outcome prediction, models were optimized/trained on n = 373 and tested on an independent cohort from Yale (n = 72), and an external cohort from Geisinger Medical Center (n = 232). In the independent cohort, there was no significant difference between Combined input models (AUC = 0.76) versus Radiomics + Treatment (AUC = 0.72, p = 0.39), Radiomics (AUC = 0.72, p = 0.39), or Clinical + Treatment (AUC = 76, p = 0.90) models; however, in the external cohort, the Combined model (AUC = 0.74) outperformed Radiomics + Treatment (AUC = 0.66, p < 0.001) and Radiomics (AUC = 0.68, p = 0.005) models for 3-month prediction.

Conclusion: Machine-learning signatures of admission CTA radiomics can provide prognostic information in acute LVO stroke candidates for mechanical thrombectomy. Such objective and time-sensitive risk stratification can guide treatment decisions and facilitate tele-stroke assessment of patients. Particularly in the absence of reliable clinical information at the time of admission, models solely using radiomics features can provide a useful prognostication tool.
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http://dx.doi.org/10.1016/j.nicl.2022.103034DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9108990PMC
May 2022

Impact of collateral flow on cost-effectiveness of endovascular thrombectomy.

J Neurosurg 2022 Apr 29:1-10. Epub 2022 Apr 29.

1Department of Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut.

Objective: Acute ischemic stroke patients with large-vessel occlusion and good collateral blood flow have significantly better outcomes than patients with poor collateral circulation. The purpose of this study was to evaluate the cost-effectiveness of endovascular thrombectomy (EVT) based on collateral status and, in particular, to analyze its effectiveness in ischemic stroke patients with poor collaterals.

Methods: A decision analysis study was performed with Markov modeling to estimate the lifetime quality-adjusted life-years (QALYs) and associated costs of EVT based on collateral status. The study was performed over a lifetime horizon with a societal perspective in the US setting. Base-case analysis was done for good, intermediate, and poor collateral status. One-way, two-way, and probabilistic sensitivity analyses were performed.

Results: EVT resulted in greater effectiveness of treatment compared to no EVT/medical therapy (2.56 QALYs in patients with good collaterals, 1.88 QALYs in those with intermediate collaterals, and 1.79 QALYs in patients with poor collaterals), which was equivalent to 1050, 771, and 734 days, respectively, in a health state characterized by a modified Rankin Scale (mRS) score of 0-2. EVT also resulted in lower costs in patients with good and intermediate collaterals. For patients with poor collateral status, the EVT strategy had higher effectiveness and higher costs, with an incremental cost-effectiveness ratio (ICER) of $44,326/QALY. EVT was more cost-effective as long as it had better outcomes in absolute numbers in at least 4%-8% more patients than medical management.

Conclusions: EVT treatment in the early time window for good outcome after ischemic stroke is cost-effective irrespective of the quality of collateral circulation, and patients should not be excluded from thrombectomy solely on the basis of collateral status. Despite relatively lower benefits of EVT in patients with poor collaterals, even smaller differences in better outcomes have significant long-term financial implications that make EVT cost-effective.
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http://dx.doi.org/10.3171/2022.2.JNS212887DOI Listing
April 2022

Machine Learning Models for Classifying High- and Low-Grade Gliomas: A Systematic Review and Quality of Reporting Analysis.

Front Oncol 2022 22;12:856231. Epub 2022 Apr 22.

Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States.

Objectives: To systematically review, assess the reporting quality of, and discuss improvement opportunities for studies describing machine learning (ML) models for glioma grade prediction.

Methods: This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses of Diagnostic Test Accuracy (PRISMA-DTA) statement. A systematic search was performed in September 2020, and repeated in January 2021, on four databases: Embase, Medline, CENTRAL, and Web of Science Core Collection. Publications were screened in Covidence, and reporting quality was measured against the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Statement. Descriptive statistics were calculated using GraphPad Prism 9.

Results: The search identified 11,727 candidate articles with 1,135 articles undergoing full text review and 85 included in analysis. 67 (79%) articles were published between 2018-2021. The mean prediction accuracy of the best performing model in each study was 0.89 ± 0.09. The most common algorithm for conventional machine learning studies was Support Vector Machine (mean accuracy: 0.90 ± 0.07) and for deep learning studies was Convolutional Neural Network (mean accuracy: 0.91 ± 0.10). Only one study used both a large training dataset (n>200) and external validation (accuracy: 0.72) for their model. The mean adherence rate to TRIPOD was 44.5% ± 11.1%, with poor reporting adherence for model performance (0%), abstracts (0%), and titles (0%).

Conclusions: The application of ML to glioma grade prediction has grown substantially, with ML model studies reporting high predictive accuracies but lacking essential metrics and characteristics for assessing model performance. Several domains, including generalizability and reproducibility, warrant further attention to enable translation into clinical practice.

Systematic Review Registration: PROSPERO, identifier CRD42020209938.
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http://dx.doi.org/10.3389/fonc.2022.856231DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9076130PMC
April 2022

Brain Tumor Imaging: Applications of Artificial Intelligence.

Semin Ultrasound CT MR 2022 Apr 11;43(2):153-169. Epub 2022 Feb 11.

Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT. Electronic address:

Artificial intelligence has become a popular field of research with goals of integrating it into the clinical decision-making process. A growing number of predictive models are being employed utilizing machine learning that includes quantitative, computer-extracted imaging features known as radiomic features, and deep learning systems. This is especially true in brain-tumor imaging where artificial intelligence has been proposed to characterize, differentiate, and prognostication. We reviewed current literature regarding the potential uses of machine learning-based, and deep learning-based artificial intelligence in neuro-oncology as it pertains to brain tumor molecular classification, differentiation, and treatment response. While there is promising evidence supporting the use of artificial intelligence in neuro-oncology, there are still more investigations needed on a larger, multicenter scale along with a streamlined and standardized image processing workflow prior to its introduction in routine clinical decision-making protocol.
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http://dx.doi.org/10.1053/j.sult.2022.02.005DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8961005PMC
April 2022

Radiomics: A Primer on Processing Workflow and Analysis.

Semin Ultrasound CT MR 2022 Apr 12;43(2):142-146. Epub 2022 Feb 12.

Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT. Electronic address:

Quantitative analysis of medical images can provide objective tools for diagnosis, prognostication, and disease monitoring. Radiomics refers to automated extraction of a large number of quantitative features from medical images for characterization of underlying pathologies. In this review, we will discuss the principles of radiomics, image preprocessing, feature extraction workflow, and statistical analysis. We will also address the limitations and future directions of radiomics.
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http://dx.doi.org/10.1053/j.sult.2022.02.003DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8961004PMC
April 2022

Machine Learning Applications for Differentiation of Glioma from Brain Metastasis-A Systematic Review.

Cancers (Basel) 2022 Mar 8;14(6). Epub 2022 Mar 8.

Department of Radiology and Biomedical Imaging, Yale School of Medicine, 333 Cedar Street, P.O. Box 208042, New Haven, CT 06510, USA.

Glioma and brain metastasis can be difficult to distinguish on conventional magnetic resonance imaging (MRI) due to the similarity of imaging features in specific clinical circumstances. Multiple studies have investigated the use of machine learning (ML) models for non-invasive differentiation of glioma from brain metastasis. Many of the studies report promising classification results, however, to date, none have been implemented into clinical practice. After a screening of 12,470 studies, we included 29 eligible studies in our systematic review. From each study, we aggregated data on model design, development, and best classifiers, as well as quality of reporting according to the TRIPOD statement. In a subset of eligible studies, we conducted a meta-analysis of the reported AUC. It was found that data predominantly originated from single-center institutions (n = 25/29) and only two studies performed external validation. The median TRIPOD adherence was 0.48, indicating insufficient quality of reporting among surveyed studies. Our findings illustrate that despite promising classification results, reliable model assessment is limited by poor reporting of study design and lack of algorithm validation and generalizability. Therefore, adherence to quality guidelines and validation on outside datasets is critical for the clinical translation of ML for the differentiation of glioma and brain metastasis.
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http://dx.doi.org/10.3390/cancers14061369DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8946855PMC
March 2022

Editorial: Radiomics Advances Precision Medicine.

Front Oncol 2022 2;12:853948. Epub 2022 Mar 2.

Department of Radiology, City of Hope Comprehensive Cancer Center, Duarte, CA, United States.

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http://dx.doi.org/10.3389/fonc.2022.853948DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8924066PMC
March 2022

Cost-effectiveness of thrombectomy in patients with minor stroke and large vessel occlusion: effect of thrombus location on cost-effectiveness and outcomes.

J Neurointerv Surg 2022 Jan 12. Epub 2022 Jan 12.

Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut, USA

Background: To evaluate the cost-effectiveness of endovascular thrombectomy (EVT) to treat large vessel occlusion (LVO) in patients with acute, minor stroke (National Institute of Health Stroke Scale (NIHSS) <6) and impact of occlusion site.

Methods: A Markov decision-analytic model was constructed accounting for both costs and outcomes from a societal perspective. Two different management strategies were evaluated: EVT and medical management. Base case analysis was done for three different sites of occlusion: proximal M1, distal M1 and M2 occlusions. One-way, two-way and probabilistic sensitivity analyses were performed.

Results: Base-case calculation showed EVT to be the dominant strategy in 65-year-old patients with proximal M1 occlusion and NIHSS <6, with lower cost (US$37 229 per patient) and higher effectiveness (1.47 quality-adjusted life years (QALYs)), equivalent to 537 days in perfect health or 603 days in modified Rankin score (mRS) 0-2 health state. EVT is the cost-effective strategy in 92.7% of iterations for patients with proximal M1 occlusion using a willingness-to-pay threshold of US$100 000/QALY. EVT was cost-effective if it had better outcomes in 2%-3% more patients than intravenous thrombolysis (IVT) in absolute numbers (base case difference -16%). EVT was cost-effective when the proportion of M2 occlusions was less than 37.1%.

Conclusions: EVT is cost-effective in patients with minor stroke and LVO in the long term (lifetime horizon), considering the poor outcomes and significant disability associated with non-reperfusion. Our study emphasizes the need for caution in interpreting previous observational studies which concluded similar results in EVT versus medical management in patients with minor stroke due to a high proportion of patients with M2 occlusions in the two strategies.
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http://dx.doi.org/10.1136/neurintsurg-2021-018375DOI Listing
January 2022

Bedside detection of intracranial midline shift using portable magnetic resonance imaging.

Sci Rep 2022 01 7;12(1):67. Epub 2022 Jan 7.

Hyperfine, Inc, Guilford, CT, USA.

Neuroimaging is crucial for assessing mass effect in brain-injured patients. Transport to an imaging suite, however, is challenging for critically ill patients. We evaluated the use of a low magnetic field, portable MRI (pMRI) for assessing midline shift (MLS). In this observational study, 0.064 T pMRI exams were performed on stroke patients admitted to the neuroscience intensive care unit at Yale New Haven Hospital. Dichotomous (present or absent) and continuous MLS measurements were obtained on pMRI exams and locally available and accessible standard-of-care imaging exams (CT or MRI). We evaluated the agreement between pMRI and standard-of-care measurements. Additionally, we assessed the relationship between pMRI-based MLS and functional outcome (modified Rankin Scale). A total of 102 patients were included in the final study (48 ischemic stroke; 54 intracranial hemorrhage). There was significant concordance between pMRI and standard-of-care measurements (dichotomous, κ = 0.87; continuous, ICC = 0.94). Low-field pMRI identified MLS with a sensitivity of 0.93 and specificity of 0.96. Moreover, pMRI MLS assessments predicted poor clinical outcome at discharge (dichotomous: adjusted OR 7.98, 95% CI 2.07-40.04, p = 0.005; continuous: adjusted OR 1.59, 95% CI 1.11-2.49, p = 0.021). Low-field pMRI may serve as a valuable bedside tool for detecting mass effect.
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http://dx.doi.org/10.1038/s41598-021-03892-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8742125PMC
January 2022

Similar admission NIHSS may represent larger tissue-at-risk in patients with right-sided versus left-sided large vessel occlusion.

J Neurointerv Surg 2021 Oct 13. Epub 2021 Oct 13.

Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut, USA

Background: We investigated the effects of the side of large vessel occlusion (LVO) on post-thrombectomy infarct volume and clinical outcome with regard to admission National Institutes of Health Stroke Scale (NIHSS) score.

Methods: We retrospectively identified patients with anterior LVO who received endovascular thrombectomy and follow-up MRI. Applying voxel-wise general linear models and multivariate analysis, we assessed the effects of occlusion side, admission NIHSS, and post-thrombectomy reperfusion (modified Thrombolysis in Cerebral Infarction, mTICI) on final infarct distribution and volume as well as discharge modified Rankin Scale (mRS) score.

Results: We included 469 patients, 254 with left-sided and 215 with right-sided LVO. Admission NIHSS was higher in those with left-sided LVO (median (IQR) 16 (10-22)) than in those with right-sided LVO (14 (8-16), p>0.001). In voxel-wise analysis, worse post-thrombectomy reperfusion, lower admission NIHSS score, and poor discharge outcome were associated with right-hemispheric infarct lesions. In multivariate analysis, right-sided LVO was an independent predictor of larger final infarct volume (p=0.003). There was a significant three-way interaction between admission stroke severity (based on NIHSS), LVO side, and mTICI with regard to final infarct volume (p=0.041). Specifically, in patients with moderate stroke (NIHSS 6-15), incomplete reperfusion (mTICI 0-2b) was associated with larger final infarct volume (p<0.001) and worse discharge outcome (p=0.02) in right-sided compared with left-sided LVO.

Conclusions: When adjusted for admission NIHSS, worse post-thrombectomy reperfusion is associated with larger infarct volume and worse discharge outcome in right-sided versus left-sided LVO. This may represent larger tissue-at-risk in patients with right-sided LVO when applying admission NIHSS as a clinical biomarker for penumbra.
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http://dx.doi.org/10.1136/neurintsurg-2021-017785DOI Listing
October 2021

The coronal plane maximum diameter of deep intracerebral hemorrhage predicts functional outcome more accurately than hematoma volume.

Int J Stroke 2021 Oct 13:17474930211050749. Epub 2021 Oct 13.

Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA.

Background: Among prognostic imaging variables, the hematoma volume on admission computed tomography (CT) has long been considered the strongest predictor of outcome and mortality in intracerebral hemorrhage.

Aims: To examine whether different features of hematoma shape are associated with functional outcome in deep intracerebral hemorrhage.

Methods: We analyzed 790 patients from the ATACH-2 trial, and 14 shape features were quantified. We calculated Spearman's Rho to assess the correlation between shape features and three-month modified Rankin scale (mRS) score, and the area under the receiver operating characteristic curve (AUC) to quantify the association between shape features and poor outcome defined as mRS>2 as well as mRS > 3.

Results: Among 14 shape features, the maximum intracerebral hemorrhage diameter in the coronal plane was the strongest predictor of functional outcome, with a maximum coronal diameter >∼3.5 cm indicating higher three-month mRS scores. The maximum coronal diameter versus hematoma volume yielded a Rho of 0.40 versus 0.35 ( = 0.006), an AUC of 0.71 versus 0.68 ( = 0.004), and an AUC of 0.71 versus 0.69 ( = 0.029). In multiple regression analysis adjusted for known outcome predictors, the maximum coronal diameter was independently associated with three-month mRS (p < 0.001).

Conclusions: A coronal-plane maximum diameter measurement offers greater prognostic value in deep intracerebral hemorrhage than hematoma volume. This simple shape metric may expedite assessment of admission head CTs, offer a potential biomarker for hematoma size eligibility criteria in clinical trials, and may substitute volume in prognostic intracerebral hemorrhage scoring systems.
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http://dx.doi.org/10.1177/17474930211050749DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9005571PMC
October 2021

The Initial Step Towards Establishing a Quantitative, Magnetic Resonance Imaging-Based Framework for Response Assessment of Spinal Metastases After Stereotactic Body Radiation Therapy.

Neurosurgery 2021 10;89(5):884-891

Department of Radiation Oncology, University of Toronto, Toronto, Canada.

Background: There are no established threshold values regarding the degree of growth on imaging when assessing response of spinal metastases treated with stereotactic body radiation therapy (SBRT).

Objective: To determine a magnetic resonance imaging-based minimum detectable difference (MDD) in gross tumor volume (GTV) and its association with 1-yr radiation site-specific (RSS) progression-free survival (PFS).

Methods: GTVs at baseline and first 2 post-SBRT scans (Post1 and Post2, respectively) for 142 spinal segments were contoured, and percentage volume change between scans calculated. One-year RSS PFS was acquired from medical records. The MDD was determined. The MDD was compared against optimal thresholds of GTV changes associated with 1-yr RSS PFS using Youden's J index, and receiver operating characteristic curves between timepoints compared to determine which timeframe had the best association.

Results: A total of 17 of the 142 segments demonstrated progression. The MDD was 10.9%. Baseline-Post2 demonstrated the best performance (area under the curve [AUC] 0.90). Only Baseline-Post2 had an optimal threshold > MDD at 14.7%. Due to large distribution of GTVs, volumes were split into tertiles. Small tumors (GTV < 2 cc) had optimal thresholds of 42.0%, 71.3%, and 37.2% at Baseline-Post1 (AUC 0.81), Baseline-Post2 (AUC 0.89), and Post1-Post2 (AUC 0.77), respectively. Medium tumors (2 ≤ GTV ≤ 8.3 cc) all demonstrated optimal thresholds < MDD, with AUCs ranging from 0.65 to 0.84. Large tumors (GTV > 8.3 cc) had 2 timepoints where optimal thresholds > MDD: Baseline-Post2 (13.3%; AUC 0.97) and Post1-Post2 (11.8%; AUC 0.66). Baseline-Post2 had the best association with RSS PFS for all tertiles.

Conclusion: Given a MDD of 10.9%, for small GTVs, larger (>37%) changes were required before local failure could be determined, compared to 11% to 13% for medium/large tumors.
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http://dx.doi.org/10.1093/neuros/nyab310DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8645191PMC
October 2021

Site-Specific Variation in Radiomic Features of Head and Neck Squamous Cell Carcinoma and Its Impact on Machine Learning Models.

Cancers (Basel) 2021 Jul 24;13(15). Epub 2021 Jul 24.

Augmented Intelligence & Precision Health Laboratory (AIPHL), Department of Radiology and the Research Institute of the McGill University Health Centre, McGill University, Montreal, QC H4A 3J1, Canada.

Current radiomic studies of head and neck squamous cell carcinomas (HNSCC) are typically based on datasets combining tumors from different locations, assuming that the radiomic features are similar based on histopathologic characteristics. However, molecular pathogenesis and treatment in HNSCC substantially vary across different tumor sites. It is not known if a statistical difference exists between radiomic features from different tumor sites and how they affect machine learning model performance in endpoint prediction. To answer these questions, we extracted radiomic features from contrast-enhanced neck computed tomography scans (CTs) of 605 patients with HNSCC originating from the oral cavity, oropharynx, and hypopharynx/larynx. The difference in radiomic features of tumors from these sites was assessed using statistical analyses and Random Forest classifiers on the radiomic features with 10-fold cross-validation to predict tumor sites, nodal metastasis, and HPV status. We found statistically significant differences (-value ≤ 0.05) between the radiomic features of HNSCC depending on tumor location. We also observed that differences in quantitative features among HNSCC from different locations impact the performance of machine learning models. This suggests that radiomic features may reveal biologic heterogeneity complementary to current gold standard histopathologic evaluation. We recommend considering tumor site in radiomic studies of HNSCC.
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http://dx.doi.org/10.3390/cancers13153723DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8345201PMC
July 2021

Rapid Assessment of Blood Pressure Variability and Outcome After Successful Thrombectomy.

Stroke 2021 08 27;52(9):e531-e535. Epub 2021 Jul 27.

Neurocritical Care and Emergency Neurology (C.K.N., N.P.), Yale School of Medicine and Yale-New Haven Hospital, CT.

Background And Purpose: High blood pressure (BP) variability after endovascular stroke therapy is associated with poor outcome. Conventional BP variability measures require long recordings, limiting their utility as a risk assessment tool to guide clinical decision-making. Here, we performed rapid assessment of BP variability by spectral analysis and evaluated its association with early clinical improvement and long-term functional outcomes.

Methods: We conducted a prospective study of 146 patients with anterior circulation ischemic stroke who underwent successful endovascular stroke therapy. Spectral analysis of 5-minute recordings of beat-to-beat BP was used to quantify BP variability. Outcomes included initial clinical response and modified Rankin Scale at 90 days.

Results: Increased BP variability at high frequencies was independently associated with poor functional outcome at 90 days (adjusted odds ratio [aOR], 1.85 [95% CI, 1.07-3.25], =0.03; low-/high-frequency ratio aOR, 0.67 [95% CI, 0.46-0.92], =0.02) and reduced likelihood of an early neurological recovery (aOR, 0.62 [95% CI, 0.44-0.91], =0.01 and aOR, 1.37 [95% CI, 1.03-1.87], =0.04, respectively).

Conclusions: High-frequency BP oscillations after successful reperfusion may be harmful and associate with a decreased likelihood of neurological recovery and favorable functional outcomes. Rapid assessment of BP variability throughout the postreperfusion period is feasible and may allow for a more personalized BP management.
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http://dx.doi.org/10.1161/STROKEAHA.121.034291DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8384685PMC
August 2021

Admission computed tomography radiomic signatures outperform hematoma volume in predicting baseline clinical severity and functional outcome in the ATACH-2 trial intracerebral hemorrhage population.

Eur J Neurol 2021 09 18;28(9):2989-3000. Epub 2021 Jul 18.

Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA.

Background And Purpose: Radiomics provides a framework for automated extraction of high-dimensional feature sets from medical images. We aimed to determine radiomics signature correlates of admission clinical severity and medium-term outcome from intracerebral hemorrhage (ICH) lesions on baseline head computed tomography (CT).

Methods: We used the ATACH-2 (Antihypertensive Treatment of Acute Cerebral Hemorrhage II) trial dataset. Patients included in this analysis (n = 895) were randomly allocated to discovery (n = 448) and independent validation (n = 447) cohorts. We extracted 1130 radiomics features from hematoma lesions on baseline noncontrast head CT scans and generated radiomics signatures associated with admission Glasgow Coma Scale (GCS), admission National Institutes of Health Stroke Scale (NIHSS), and 3-month modified Rankin Scale (mRS) scores. Spearman's correlation between radiomics signatures and corresponding target variables was compared with hematoma volume.

Results: In the discovery cohort, radiomics signatures, compared to ICH volume, had a significantly stronger association with admission GCS (0.47 vs. 0.44, p = 0.008), admission NIHSS (0.69 vs. 0.57, p < 0.001), and 3-month mRS scores (0.44 vs. 0.32, p < 0.001). Similarly, in independent validation, radiomics signatures, compared to ICH volume, had a significantly stronger association with admission GCS (0.43 vs. 0.41, p = 0.02), NIHSS (0.64 vs. 0.56, p < 0.001), and 3-month mRS scores (0.43 vs. 0.33, p < 0.001). In multiple regression analysis adjusted for known predictors of ICH outcome, the radiomics signature was an independent predictor of 3-month mRS in both cohorts.

Conclusions: Limited by the enrollment criteria of the ATACH-2 trial, we showed that radiomics features quantifying hematoma texture, density, and shape on baseline CT can provide imaging correlates for clinical presentation and 3-month outcome. These findings couldtrigger a paradigm shift where imaging biomarkers may improve current modelsfor prognostication, risk-stratification, and treatment triage of ICH patients.
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http://dx.doi.org/10.1111/ene.15000DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8818333PMC
September 2021

Early head CT in post-cardiac arrest patients: A helpful tool or contributor to self-fulfilling prophecy?

Resuscitation 2021 08 17;165:68-76. Epub 2021 Jun 17.

Department of Neurology, Yale School of Medicine, New Haven, CT, 06510, United States.

Objective: Neuroprognostication guidelines suggest that early head computed tomography (HCT) might be useful in the evaluation of cardiac arrest (CA) patients following return of spontaneous circulation. We aimed to determine the impact of early HCT, performed within the first 6 h following CA, on decision-making following resuscitation.

Methods: We identified a cohort of initially unconscious post-CA patients at a tertiary care academic medical center from 2012 to 2017. Variables pertaining to demographics, CA details, post-CA care, including neuroimaging and neurophysiologic testing, were abstracted retrospectively from the electronic medical records. Changes in management resulting from HCT findings were recorded. Blinded board-certified neurointensivists adjudicated HCT findings related to hypoxic-ischemic brain injury (HIBI) burden. The gray-white matter ratio (GWR) was also calculated.

Results: Of 302 patients, 182 (60.2%) underwent HCT within six hours of CA (early HCT group). Approximately 1 in 4 early HCTs were abnormal (most commonly HIBI changes; 78.7%, n = 37), which resulted in a change in management in nearly half of cases (46.8%, n = 22). The most common changes in management were de-escalation in care [including transition to do not resuscitate status), withholding targeted temperature management, and withdrawal of life sustaining therapy (WLST)]. In cases with radiographic HIBI, mean [standard deviation] GWR was lower (1.20 [0.10] vs 1.30 [0.09], P < 0.001) and progression to brain death was higher (44.4% vs 2.9%; P < 0.001). The inter-rater reliability (IRR) of early HCT to determine presence of HIBI between radiology and three neurointensivists had a wide range (κ 0.13-0.66).

Conclusion: Early HCT identified abnormalities in 25% of cases and frequently influenced therapeutic decisions. Neuroimaging interpretation discrepancies between radiology and neurointensivists are common and agreement on severity of HIBI on early HCT is poor (k 0.11).
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http://dx.doi.org/10.1016/j.resuscitation.2021.06.004DOI Listing
August 2021

Imaging of Spontaneous Intracerebral Hemorrhage.

Neuroimaging Clin N Am 2021 May;31(2):193-203

Division of Neuroradiology, Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 330 Cedar Street, Tompkins East TE-2, New Haven, CT 06520, USA. Electronic address:

Primary or nontraumatic spontaneous intracerebral hemorrhage (ICH) comprises approximately 15% to 20% of all stroke. ICH has a mortality of approximately 40% within the first month, and 75% mortality and morbidity rate within the first year. Despite reduction in overall stroke incidence, hemorrhagic stroke incidence has remained steady since 1980. Neuroimaging is critical in detection of ICH, determining the underlying cause, identification of patients at risk of hematoma expansion, and directing the treatment strategy. This article discusses the neuroimaging methods of ICH, imaging markers for clinical outcome prediction, and future research directions with attention to the latest evidence-based guidelines.
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http://dx.doi.org/10.1016/j.nic.2021.02.003DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8820948PMC
May 2021

Fluid dynamics simulations show that facial masks can suppress the spread of COVID-19 in indoor environments.

ArXiv 2020 Nov 6. Epub 2020 Nov 6.

The Coronavirus disease outbreak of 2019 has been causing significant loss of life and unprecedented economical loss throughout the world. Social distancing and face masks are widely recommended around the globe in order to protect others and prevent the spread of the virus through breathing, coughing, and sneezing. To expand the scientific underpinnings of such recommendations, we carry out high-fidelity computational fluid dynamics simulations of unprecedented resolution and realism to elucidate the underlying physics of saliva particulate transport during human cough with and without facial masks. Our simulations: (a) are carried out under both a stagnant ambient flow (indoor) and a mild unidirectional breeze (outdoor); (b) incorporate the effect of human anatomy on the flow; (c) account for both medical and non-medical grade masks; and (d) consider a wide spectrum of particulate sizes, ranging from 10 micro m to 300 micro m. We show that during indoor coughing some saliva particulates could travel up to 0.48 m, 0.73 m, and 2.62 m for the cases with medical-grade, non-medical grade, and without facial masks, respectively. Thus, in indoor environments either medical or non-medical grade facial masks can successfully limit the spreading of saliva particulates to others. Under outdoor conditions with a unidirectional mild breeze, however, leakage flow through the mask can cause saliva particulates to be entrained into the energetic shear layers around the body and transported very fast at large distances by the turbulent flow, thus, limiting the effectiveness of facial masks.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7654873PMC
November 2020

Prediction of post-radiotherapy locoregional progression in HPV-associated oropharyngeal squamous cell carcinoma using machine-learning analysis of baseline PET/CT radiomics.

Transl Oncol 2021 Jan 16;14(1):100906. Epub 2020 Oct 16.

Section of Neuroradiology, Department of Radiology and Biomedical Imaging, Yale School of Medicine, 789 Howard Ave, PO Box 208042, New Haven, CT 06519, United States. Electronic address:

Locoregional failure remains a therapeutic challenge in oropharyngeal squamous cell carcinoma (OPSCC). We aimed to devise novel objective imaging biomarkers for prediction of locoregional progression in HPV-associated OPSCC. Following manual lesion delineation, 1037 PET and 1037 CT radiomic features were extracted from each primary tumor and metastatic cervical lymph node on baseline PET/CT scans. Applying random forest machine-learning algorithms, we generated radiomic models for censoring-aware locoregional progression prognostication (evaluated by Harrell's C-index) and risk stratification (evaluated in Kaplan-Meier analysis). A total of 190 patients were included; an optimized model yielded a median (interquartile range) C-index of 0.76 (0.66-0.81; p = 0.01) in prognostication of locoregional progression, using combined PET/CT radiomic features from primary tumors. Radiomics-based risk stratification reliably identified patients at risk for locoregional progression within 2-, 3-, 4-, and 5-year follow-up intervals, with log-rank p-values of p = 0.003, p = 0.001, p = 0.02, p = 0.006 in Kaplan-Meier analysis, respectively. Our results suggest PET/CT radiomic biomarkers can predict post-radiotherapy locoregional progression in HPV-associated OPSCC. Pending validation in large, independent cohorts, such objective biomarkers may improve patient selection for treatment de-intensification trials in this prognostically favorable OPSCC entity, and eventually facilitate personalized therapy.
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http://dx.doi.org/10.1016/j.tranon.2020.100906DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7568193PMC
January 2021

Choice of imaging modality for pre-treatment staging of head and neck cancer impacts TNM staging.

Am J Otolaryngol 2020 Nov - Dec;41(6):102662. Epub 2020 Aug 10.

Department of Otolaryngology, Boston Medical Center, Boston, MA, United States of America; Boston University School of Medicine, Boston, MA, United States of America. Electronic address:

Purpose: The purpose of this retrospective cohort study was to determine whether there is a difference in the sensitivity of chest computed tomography (CT) versus F-fluorodeoxyglucose positron emission tomography with low-dose nonenhanced CT (F-FDG PET/CT or PET/CT) in the detection of distant metastases in head and neck cancer, within a tertiary care setting.

Materials And Methods: Patients with head and neck cancer, and known distant metastases, who underwent both F-FDG PET/CT with integrated low-dose nonenhanced CT and diagnostic chest CT prior to initiation of therapy from 2008 to 2017 were included. Two head and neck radiologists, blinded to all patient information and to each other's readings, reviewed the PET/CT or CT chest images for each patient and identified whether distant metastases were present. No radiologist read both modalities for a single patient. Concordance between imaging modalities was quantitatively analyzed using McNemar's test.

Results: 27 patients were included. McNemar's mid p-value analysis showed no significant difference in the detection of distant metastases (p = .6875). However, PET/CT detected distant metastases in three patients that chest CT did not, while chest CT identified distant metastatic disease in two patients that were negative on PET/CT.

Conclusions: While this study did not identify a statistically significant difference in sensitivity, five patients had distant metastases identified on only one of the two modalities. Use of a single modality would have resulted in inaccurate staging in 7-11% of patients in our study. The use of both modalities offers the greatest accuracy when providing stage-adapted oncologic treatment.
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http://dx.doi.org/10.1016/j.amjoto.2020.102662DOI Listing
December 2020

Effects of Collateral Status on Infarct Distribution Following Endovascular Therapy in Large Vessel Occlusion Stroke.

Stroke 2020 09 12;51(9):e193-e202. Epub 2020 Aug 12.

Department of Radiology and Biomedical Imaging, Division of Neurocritical Care and Emergency Neurology, Department of Neurology (S.M.S., C.K.N., K.U.P., S.K., A.S., G.J.F., K.N.S., N.H.P.), Yale University School of Medicine, New Haven, CT.

Background And Purpose: We aim to examine effects of collateral status and post-thrombectomy reperfusion on final infarct distribution and early functional outcome in patients with anterior circulation large vessel occlusion ischemic stroke.

Methods: Patients with large vessel occlusion who underwent endovascular intervention were included in this study. All patients had baseline computed tomography angiography and follow-up magnetic resonance imaging. Collateral status was graded according to the criteria proposed by Miteff et al and reperfusion was assessed using the modified Thrombolysis in Cerebral Infarction (mTICI) system. We applied a multivariate voxel-wise general linear model to correlate the distribution of final infarction with collateral status and degree of reperfusion. Early favorable outcome was defined as a discharge modified Rankin Scale score ≤2.

Results: Of the 283 patients included, 129 (46%) had good, 97 (34%) had moderate, and 57 (20%) had poor collateral status. Successful reperfusion (mTICI 2b/3) was achieved in 206 (73%) patients. Poor collateral status was associated with infarction of middle cerebral artery border zones, whereas worse reperfusion (mTICI scores 0-2a) was associated with infarction of middle cerebral artery territory deep white matter tracts and the posterior limb of the internal capsule. In multivariate regression models, both mTICI (<0.001) and collateral status (<0.001) were among independent predictors of final infarct volumes. However, mTICI (<0.001), but not collateral status (=0.058), predicted favorable outcome at discharge.

Conclusions: In this cohort of patients with large vessel occlusion stroke, both the collateral status and endovascular reperfusion were strongly associated with middle cerebral artery territory final infarct volumes. Our findings suggesting that baseline collateral status predominantly affected middle cerebral artery border zones infarction, whereas higher mTICI preserved deep white matter and internal capsule from infarction; may explain why reperfusion success-but not collateral status-was among the independent predictors of favorable outcome at discharge. Infarction of the lentiform nuclei was observed regardless of collateral status or reperfusion success.
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http://dx.doi.org/10.1161/STROKEAHA.120.029892DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7484023PMC
September 2020

Potential Added Value of PET/CT Radiomics for Survival Prognostication beyond AJCC 8th Edition Staging in Oropharyngeal Squamous Cell Carcinoma.

Cancers (Basel) 2020 Jul 3;12(7). Epub 2020 Jul 3.

Section of Neuroradiology, Department of Radiology and Biomedical Imaging, Yale School of Medicine, 789 Howard Ave, New Haven, CT 06519, USA.

Accurate risk-stratification can facilitate precision therapy in oropharyngeal squamous cell carcinoma (OPSCC). We explored the potential added value of baseline positron emission tomography (PET)/computed tomography (CT) radiomic features for prognostication and risk stratification of OPSCC beyond the American Joint Committee on Cancer (AJCC) 8th edition staging scheme. Using institutional and publicly available datasets, we included OPSCC patients with known human papillomavirus (HPV) status, without baseline distant metastasis and treated with curative intent. We extracted 1037 PET and 1037 CT radiomic features quantifying lesion shape, imaging intensity, and texture patterns from primary tumors and metastatic cervical lymph nodes. Utilizing random forest algorithms, we devised novel machine-learning models for OPSCC progression-free survival (PFS) and overall survival (OS) using "radiomics" features, "AJCC" variables, and the "combined" set as input. We designed both single- (PET or CT) and combined-modality (PET/CT) models. Harrell's C-index quantified survival model performance; risk stratification was evaluated in Kaplan-Meier analysis. A total of 311 patients were included. In HPV-associated OPSCC, the best "radiomics" model achieved an average C-index ± standard deviation of 0.62 ± 0.05 ( = 0.02) for PFS prediction, compared to 0.54 ± 0.06 ( = 0.32) utilizing "AJCC" variables. Radiomics-based risk-stratification of HPV-associated OPSCC was significant for PFS and OS. Similar trends were observed in HPV-negative OPSCC. In conclusion, radiomics imaging features extracted from pre-treatment PET/CT may provide complimentary information to the current AJCC staging scheme for survival prognostication and risk-stratification of HPV-associated OPSCC.
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http://dx.doi.org/10.3390/cancers12071778DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7407414PMC
July 2020

A Fully Automated Method for Segmenting Arteries and Quantifying Vessel Radii on Magnetic Resonance Angiography Images of Varying Projection Thickness.

Front Neurosci 2020 16;14:537. Epub 2020 Jun 16.

Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States.

Purpose: Precise quantification of cerebral arteries can help with differentiation and prognostication of cerebrovascular disease. Existing image processing and segmentation algorithms for magnetic resonance angiography (MRA) are limited to the analysis of either 2D maximum intensity projection images or the entire 3D volume. The goal of this study was to develop a fully automated, hybrid 2D-3D method for robust segmentation of arteries and accurate quantification of vessel radii using MRA at varying projection thicknesses.

Methods: A novel algorithm that employs an adaptive Frangi filter for segmentation of vessels followed by estimation of vessel radii is presented. The method was evaluated on MRA datasets and corresponding manual segmentations from three healthy subjects for various projection thicknesses. In addition, the vessel metrics were computed in four additional subjects. Three synthetically generated angiographic datasets resembling brain vasculature were also evaluated under different noise levels. Dice similarity coefficient, Jaccard Index, F-score, and concordance correlation coefficient were used to measure the segmentation accuracy of manual versus automatic segmentation.

Results: Our new adaptive filter rendered accurate representations of vessels, maintained accurate vessel radii, and corresponded better to manual segmentation at different projection thicknesses than prior methods. Validation with synthetic datasets under low contrast and noisy conditions revealed accurate quantification of vessels without distortions.

Conclusion: We have demonstrated a method for automatic segmentation of vascular trees and the subsequent generation of a vessel radii map. This novel technique can be applied to analyze arterial structures in healthy and diseased populations and improve the characterization of vascular integrity.
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http://dx.doi.org/10.3389/fnins.2020.00537DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7308498PMC
June 2020

PET/CT radiomics signature of human papilloma virus association in oropharyngeal squamous cell carcinoma.

Eur J Nucl Med Mol Imaging 2020 12 12;47(13):2978-2991. Epub 2020 May 12.

Section of Neuroradiology, Department of Radiology and Biomedical Imaging, Yale School of Medicine, 789 Howard Ave, PO Box 208042, New Haven, CT, 06519, USA.

Purpose: To devise, validate, and externally test PET/CT radiomics signatures for human papillomavirus (HPV) association in primary tumors and metastatic cervical lymph nodes of oropharyngeal squamous cell carcinoma (OPSCC).

Methods: We analyzed 435 primary tumors (326 for training, 109 for validation) and 741 metastatic cervical lymph nodes (518 for training, 223 for validation) using FDG-PET and non-contrast CT from a multi-institutional and multi-national cohort. Utilizing 1037 radiomics features per imaging modality and per lesion, we trained, optimized, and independently validated machine-learning classifiers for prediction of HPV association in primary tumors, lymph nodes, and combined "virtual" volumes of interest (VOI). PET-based models were additionally validated in an external cohort.

Results: Single-modality PET and CT final models yielded similar classification performance without significant difference in independent validation; however, models combining PET and CT features outperformed single-modality PET- or CT-based models, with receiver operating characteristic area under the curve (AUC) of 0.78, and 0.77 for prediction of HPV association using primary tumor lesion features, in cross-validation and independent validation, respectively. In the external PET-only validation dataset, final models achieved an AUC of 0.83 for a virtual VOI combining primary tumor and lymph nodes, and an AUC of 0.73 for a virtual VOI combining all lymph nodes.

Conclusion: We found that PET-based radiomics signatures yielded similar classification performance to CT-based models, with potential added value from combining PET- and CT-based radiomics for prediction of HPV status. While our results are promising, radiomics signatures may not yet substitute tissue sampling for clinical decision-making.
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http://dx.doi.org/10.1007/s00259-020-04839-2DOI Listing
December 2020

Applications of radiomics in precision diagnosis, prognostication and treatment planning of head and neck squamous cell carcinomas.

Cancers Head Neck 2020 4;5. Epub 2020 May 4.

1Department of Radiology and Biomedical Imaging, Division of Neuroradiology, Yale School of Medicine, New Haven, CT USA.

Recent advancements in computational power, machine learning, and artificial intelligence technology have enabled automated evaluation of medical images to generate quantitative diagnostic and prognostic biomarkers. Such objective biomarkers are readily available and have the potential to improve personalized treatment, precision medicine, and patient selection for clinical trials. In this article, we explore the merits of the most recent addition to the "-omics" concept for the broader field of head and neck cancer - "Radiomics". This review discusses radiomics studies focused on (molecular) characterization, classification, prognostication and treatment guidance for head and neck squamous cell carcinomas (HNSCC). We review the underlying hypothesis, general concept and typical workflow of radiomic analysis, and elaborate on current and future challenges to be addressed before routine clinical application.
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http://dx.doi.org/10.1186/s41199-020-00053-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7197186PMC
May 2020

Machine Learning Decision Tree Models for Differentiation of Posterior Fossa Tumors Using Diffusion Histogram Analysis and Structural MRI Findings.

Front Oncol 2020 7;10:71. Epub 2020 Feb 7.

Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States.

We applied machine learning algorithms for differentiation of posterior fossa tumors using apparent diffusion coefficient (ADC) histogram analysis and structural MRI findings. A total of 256 patients with intra-axial posterior fossa tumors were identified, of whom 248 were included in machine learning analysis, with at least 6 representative subjects per each tumor pathology. The ADC histograms of solid components of tumors, structural MRI findings, and patients' age were applied to construct decision models using Classification and Regression Tree analysis. We also compared different machine learning classification algorithms (i.e., naïve Bayes, random forest, neural networks, support vector machine with linear and polynomial kernel) for dichotomized differentiation of the 5 most common tumors in our cohort: metastasis ( = 65), hemangioblastoma ( = 44), pilocytic astrocytoma ( = 43), ependymoma ( = 27), and medulloblastoma ( = 26). The decision tree model could differentiate seven tumor histopathologies with terminal nodes yielding up to 90% accurate classification rates. In receiver operating characteristics (ROC) analysis, the decision tree model achieved greater area under the curve (AUC) for differentiation of pilocytic astrocytoma ( = 0.020); and atypical teratoid/rhabdoid tumor ATRT ( = 0.001) from other types of neoplasms compared to the official clinical report. However, neuroradiologists' interpretations had greater accuracy in differentiating metastases ( = 0.001). Among different machine learning algorithms, random forest models yielded the highest accuracy in dichotomized classification of the 5 most common tumor types; and in multiclass differentiation of all tumor types random forest yielded an averaged AUC of 0.961 in training datasets, and 0.873 in validation samples. Our study demonstrates the potential application of machine learning algorithms and decision trees for accurate differentiation of brain tumors based on pretreatment MRI. Using easy to apply and understandable imaging metrics, the proposed decision tree model can help radiologists with differentiation of posterior fossa tumors, especially in tumors with similar qualitative imaging characteristics. In particular, our decision tree model provided more accurate differentiation of pilocytic astrocytomas from ATRT than by neuroradiologists in clinical reads.
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http://dx.doi.org/10.3389/fonc.2020.00071DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7018938PMC
February 2020

Poor Outcomes Related to Anterior Extension of Large Hemispheric Infarction: Topographic Analysis of GAMES-RP Trial MRI Scans.

J Stroke Cerebrovasc Dis 2020 Feb 29;29(2):104488. Epub 2019 Nov 29.

Division of Neurocritical Care and Emergency Neurology, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts.

Background: We aimed to assess the correlation of lesion location and clinical outcome in patients with large hemispheric infarction (LHI).

Methods: We analyzed admission MRI data from the GAMES-RP trial, which enrolled patients with anterior circulation infarct volumes of 82-300 cm within 10 hours of onset. Infarct lesions were segmented and co-registered onto MNI-152 brain space. Voxel-wise general linear models were applied to assess location-outcome correlations after correction for infarct volume as a co-variate.

Results: We included 83 patients with known 3-month modified Rankin scale (mRS). In voxel-wise analysis, there was significant correlation between admission infarct lesions involving the anterior cerebral artery (ACA) territory and its middle cerebral artery (MCA) border zone with both higher 3-month mRS and post-stroke day 3 and 7 National Institutes of Health Stroke Scale (NIHSS) total score and arm/leg subscores. Higher NIHSS total scores from admission through poststroke day 2 correlated with left MCA infarcts. In multivariate analysis, ACA territory infarct volume (P = .001) and admission NIHSS (P = .005) were independent predictors of 3-month mRS. Moreover, in a subgroup of 36 patients with infarct lesions involving right MCA-ACA border zone, intravenous (IV) glibenclamide (BIIB093; glyburide) treatment was the only independent predictor of 3-month mRS in multivariate regression analysis (P = .016).

Conclusions: Anterior extension of LHI with involvement of ACA territory and ACA-MCA border zone is an independent predictor of poor functional outcome, likely due to impairment of arm/leg motor function. If confirmed in larger cohorts, infarct topology may potentially help triage LHI patients who may benefit from IV glibenclamide.

Clinical Trial Registration: URL: https://www.clinicaltrials.gov. Unique identifier: NCT01794182.
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http://dx.doi.org/10.1016/j.jstrokecerebrovasdis.2019.104488DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8820410PMC
February 2020

Stroke atlas of the brain: Voxel-wise density-based clustering of infarct lesions topographic distribution.

Neuroimage Clin 2019 13;24:101981. Epub 2019 Aug 13.

Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA. Electronic address:

Objective: The supply territories of main cerebral arteries are predominantly identified based on distribution of infarct lesions in patients with large arterial occlusion; whereas, there is no consensus atlas regarding the supply territories of smaller end-arteries. In this study, we applied a data-driven approach to construct a stroke atlas of the brain using hierarchical density clustering in large number of infarct lesions, assuming that voxels/regions supplied by a common end-artery tend to infarct together.

Methods: A total of 793 infarct lesions on MRI scans of 458 patients were segmented and coregistered to MNI-152 standard brain space. Applying a voxel-wise data-driven hierarchical density clustering algorithm, we identified those voxels that were most likely to be part of same infarct lesions in our dataset. A step-wise clustering scheme was applied, where the clustering threshold was gradually decreased to form the first 20 mother (>50 cm) or main (1-50 cm) clusters in addition to any possible number of tiny clusters (<1 cm); and then, any resultant mother clusters were iteratively subdivided using the same scheme. Also, in a randomly selected 2/3 subset of our cohort, a bootstrapping cluster analysis with 100 permutations was performed to assess the statistical robustness of proposed clusters.

Results: Approximately 91% of the MNI-152 brain mask was covered by 793 infarct lesions across patients. The covered area of brain was parcellated into 4 mother, 16 main, and 123 tiny clusters at the first hierarchy level. Upon iterative clustering subdivision of mother clusters, the brain tissue was eventually parcellated into 1 mother cluster (62.6 cm), 181 main clusters (total volume 1107.3 cm), and 917 tiny clusters (total volume of 264.8 cm). In bootstrap analysis, only 0.12% of voxels, were labelled as "unstable" - with a greater reachability distance in cluster scheme compared to their corresponding mean bootstrapped reachability distance. On visual assessment, the mother/main clusters were formed along supply territories of main cerebral arteries at initial hierarchical levels, and then tiny clusters emerged in deep white matter and gray matter nuclei prone to small vessel ischemic infarcts.

Conclusions: Applying voxel-wise data-driven hierarchical density clustering on a large number of infarct lesions, we have parcellated the brain tissue into clusters of voxels that tend to be part of same infarct lesion, and presumably representing end-arterial supply territories. This hierarchical stroke atlas of the brain is shared publicly, and can potentially be applied for future infarct location-outcome analysis.
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http://dx.doi.org/10.1016/j.nicl.2019.101981DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6728875PMC
September 2020
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