Try a new search

Format these results:

Searched for:

person:radmaa01

in-biosketch:true

Total Results:

39


COVID-19 -associated Diffuse Leukoencephalopathy and Microhemorrhages

Radmanesh, Alireza; Derman, Anna; Lui, Yvonne W; Raz, Eytan; Loh, John P; Hagiwara, Mari; Borja, Maria J; Zan, Elcin; Fatterpekar, Girish M
Coronavirus disease 2019 (COVID-19) has been reported in association with a variety of brain imaging findings such as ischemic infarct, hemorrhage, and acute hemorrhagic necrotizing encephalopathy. Here, we report brain imaging features in 11 critically ill COVID-19 patients with persistently depressed mental status who underwent MRI between April 5-25, 2020 at our institution. These features include, 1) Confluent T2 hyperintensity and mild restricted diffusion in bilateral supratentorial deep and subcortical white matter (in 10 of 11 patients), and 2) multiple punctate microhemorrhages in juxtacortical and callosal white matter (in 7 of 11 patients). We also discuss potential pathogeneses.
PMID: 32437314
ISSN: 1527-1315
CID: 4444582

Genotype-phenotype correlation at codon 1740 of SETD2

Rabin, Rachel; Radmanesh, Alireza; Glass, Ian A; Dobyns, William B; Aldinger, Kimberly A; Shieh, Joseph T; Romoser, Shelby; Bombei, Hannah; Dowsett, Leah; Trapane, Pamela; Bernat, John A; Baker, Janice; Mendelsohn, Nancy J; Popp, Bernt; Siekmeyer, Manuela; Sorge, Ina; Sansbury, Francis Hugh; Watts, Patrick; Foulds, Nicola C; Burton, Jennifer; Hoganson, George; Hurst, Jane A; Menzies, Lara; Osio, Deborah; Kerecuk, Larissa; Cobben, Jan M; Jizi, Khadijé; Jacquemont, Sebastien; Bélanger, Stacey A; Löhner, Katharina; Veenstra-Knol, Hermine E; Lemmink, Henny H; Keller-Ramey, Jennifer; Wentzensen, Ingrid M; Punj, Sumit; McWalter, Kirsty; Lenberg, Jerica; Ellsworth, Katarzyna A; Radtke, Kelly; Akbarian, Schahram; Pappas, John
The SET domain containing 2, histone lysine methyltransferase encoded by SETD2 is a dual-function methyltransferase for histones and microtubules and plays an important role for transcriptional regulation, genomic stability, and cytoskeletal functions. Specifically, SETD2 is associated with trimethylation of histone H3 at lysine 36 (H3K36me3) and methylation of α-tubulin at lysine 40. Heterozygous loss of function and missense variants have previously been described with Luscan-Lumish syndrome (LLS), which is characterized by overgrowth, neurodevelopmental features, and absence of overt congenital anomalies. We have identified 15 individuals with de novo variants in codon 1740 of SETD2 whose features differ from those with LLS. Group 1 consists of 12 individuals with heterozygous variant c.5218C>T p.(Arg1740Trp) and Group 2 consists of 3 individuals with heterozygous variant c.5219G>A p.(Arg1740Gln). The phenotype of Group 1 includes microcephaly, profound intellectual disability, congenital anomalies affecting several organ systems, and similar facial features. Individuals in Group 2 had moderate to severe intellectual disability, low normal head circumference, and absence of additional major congenital anomalies. While LLS is likely due to loss of function of SETD2, the clinical features seen in individuals with variants affecting codon 1740 are more severe suggesting an alternative mechanism, such as gain of function, effects on epigenetic regulation, or posttranslational modification of the cytoskeleton. Our report is a prime example of different mutations in the same gene causing diverging phenotypes and the features observed in Group 1 suggest a new clinically recognizable syndrome uniquely associated with the heterozygous variant c.5218C>T p.(Arg1740Trp) in SETD2.
PMID: 32710489
ISSN: 1552-4833
CID: 4539912

Anatomical variations in posterior part of the circle of Willis and their associations with brain infarct in different vascular territories

Haghighimorad, Maryam; Bahrami-Motlagh, Hooman; Salehi, Elahe; Radmanesh, Alireza
Background: The relationship between anatomical variations in circle of Willis and brain infarction is controversial. The purpose of this study was to evaluate the relationship between anatomical variations in posterior portion of the circle of Willis assessed by MR angiography (MRA) and ischemic infarction in different brain territories. Methods: This cross-sectional study was conducted on consecutive patients who underwent brain MRI and MRA for suspected cerebrovascular accident. The frequency of anatomical variations including persistent fetal origin of posterior cerebral artery (fPCA) and hypoplastic/aplastic posterior communicating artery (PCoA) and their association with infarction in different intracranial vascular territories was assessed. Results: In total, 298 patients (155 male/143 female with mean age ± SD of 57 ± 15) were enrolled in the study and categorized into two groups with infarction (n = 142) and without infarction (n = 156). Sixty-three patients (21/1%) had fPCA and 231 (77.5%) had PCoA hypoplasia/aplasia. No significant correlation was identified between fPCA or PCoA hypoplasia/aplasia and presence of infarction. However, regarding the territories involved by infarction, the frequency of thalamus infarction was higher in subgroup with PCoA hypoplasia/aplasia, 17/101 (16.8%) compared to 1/41(2.4%) in the subgroup without ipsilateral PCoA hypoplasia/aplasia (p = 0.024). In two subgroups with and without ipsilateral fPCA variation, frequency of infarction in brain territories was not different significantly. Conclusions: In patients with brain infarction, aplastic/hypoplastic ipsilateral PCoA is associated with higher incidence of thalamic territory infarction.
SCOPUS:85125465148
ISSN: 0378-603x
CID: 5188232

MRI Radiogenomics of Pediatric Medulloblastoma: A Multicenter Study

Zhang, Michael; Wong, Samuel W; Wright, Jason N; Wagner, Matthias W; Toescu, Sebastian; Han, Michelle; Tam, Lydia T; Zhou, Quan; Ahmadian, Saman S; Shpanskaya, Katie; Lummus, Seth; Lai, Hollie; Eghbal, Azam; Radmanesh, Alireza; Nemelka, Jordan; Harward, Stephen; Malinzak, Michael; Laughlin, Suzanne; Perreault, Sébastien; Braun, Kristina R M; Lober, Robert M; Cho, Yoon Jae; Ertl-Wagner, Birgit; Ho, Chang Y; Mankad, Kshitij; Vogel, Hannes; Cheshier, Samuel H; Jacques, Thomas S; Aquilina, Kristian; Fisher, Paul G; Taylor, Michael; Poussaint, Tina; Vitanza, Nicholas A; Grant, Gerald A; Pfister, Stefan; Thompson, Eric; Jaju, Alok; Ramaswamy, Vijay; Yeom, Kristen W
Background Radiogenomics of pediatric medulloblastoma (MB) offers an opportunity for MB risk stratification, which may aid therapeutic decision making, family counseling, and selection of patient groups suitable for targeted genetic analysis. Purpose To develop machine learning strategies that identify the four clinically significant MB molecular subgroups. Materials and Methods In this retrospective study, consecutive pediatric patients with newly diagnosed MB at MRI at 12 international pediatric sites between July 1997 and May 2020 were identified. There were 1800 features extracted from T2- and contrast-enhanced T1-weighted preoperative MRI scans. A two-stage sequential classifier was designed-one that first identifies non-wingless (WNT) and non-sonic hedgehog (SHH) MB and then differentiates therapeutically relevant WNT from SHH. Further, a classifier that distinguishes high-risk group 3 from group 4 MB was developed. An independent, binary subgroup analysis was conducted to uncover radiomics features unique to infantile versus childhood SHH subgroups. The best-performing models from six candidate classifiers were selected, and performance was measured on holdout test sets. CIs were obtained by bootstrapping the test sets for 2000 random samples. Model accuracy score was compared with the no-information rate using the Wald test. Results The study cohort comprised 263 patients (mean age ± SD at diagnosis, 87 months ± 60; 166 boys). A two-stage classifier outperformed a single-stage multiclass classifier. The combined, sequential classifier achieved a microaveraged F1 score of 88% and a binary F1 score of 95% specifically for WNT. A group 3 versus group 4 classifier achieved an area under the receiver operating characteristic curve of 98%. Of the Image Biomarker Standardization Initiative features, texture and first-order intensity features were most contributory across the molecular subgroups. Conclusion An MRI-based machine learning decision path allowed identification of the four clinically relevant molecular pediatric medulloblastoma subgroups. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Verschakelen in this issue.
PMID: 35438562
ISSN: 1527-1315
CID: 5218262

Case 299: Sellar Spine [Case Report]

Radmanesh, Alireza; Young, Matthew G
History Part one of this case appeared 4 months previously and may contain larger images. A 32-year-old woman presented to an ophthalmologist for bilateral blurry vision. She underwent MRI of the brain and orbits, which showed a focal abnormality within the pituitary gland. The patient was referred to an endocrinologist for further evaluation. Review of systems and physical examination by the endocrinologist revealed no symptoms or signs of endocrine dysfunction. Anterior pituitary hormone levels, including growth hormone, prolactin, thyroid stimulating hormone, follicular-stimulating hormone, luteinizing hormone, and adrenocorticotropic hormone, were normal. Dynamic contrast-enhanced MRI of the sella and pituitary gland and subsequent CT of the anterior skull base were performed.
PMID: 35196175
ISSN: 1527-1315
CID: 5173982

Radiomic Signatures of Posterior Fossa Ependymoma: Molecular Subgroups and Risk Profiles

Zhang, Michael; Wang, Edward; Yecies, Derek; Tam, Lydia T; Han, Michelle; Toescu, Sebastian; Wright, Jason N; Altinmakas, Emre; Chen, Eric; Radmanesh, Alireza; Nemelka, Jordan; Oztekin, Ozgur; Wagner, Matthias W; Lober, Robert M; Ertl-Wagner, Birgit; Ho, Chang Y; Mankad, Kshitij; Vitanza, Nicholas A; Cheshier, Samuel H; Jacques, Tom S; Fisher, Paul G; Aquilina, Kristian; Said, Mourad; Jaju, Alok; Pfister, Stefan; Taylor, Michael D; Grant, Gerald A; Mattonen, Sarah; Ramaswamy, Vijay; Yeom, Kristen W
BACKGROUND:The risk profile for posterior fossa ependymoma (EP) depends on surgical and molecular status [Group A (PFA) versus Group B (PFB)]. While subtotal tumor resection is known to confer worse prognosis, MRI-based EP risk-profiling is unexplored. We aimed to apply machine learning strategies to link MRI-based biomarkers of high-risk EP and also to distinguish PFA from PFB. METHODS:We extracted 1800 quantitative features from presurgical T2-weighted (T2-MRI) and gadolinium-enhanced T1-weighted (T1-MRI) imaging of 157 EP patients. We implemented nested cross-validation to identify features for risk score calculations and apply a Cox model for survival analysis. We conducted additional feature selection for PFA versus PFB and examined performance across three candidate classifiers. RESULTS:For all EP patients with GTR, we identified four T2-MRI-based features and stratified patients into high- and low-risk groups, with 5-year overall survival rates of 62% and 100%, respectively (p < 0.0001). Among presumed PFA patients with GTR, four T1-MRI and five T2-MRI features predicted divergence of high- and low-risk groups, with 5-year overall survival rates of 62.7% and 96.7%, respectively (p = 0.002). T1-MRI-based features showed the best performance distinguishing PFA from PFB with an AUC of 0.86. CONCLUSIONS:We present machine learning strategies to identify MRI phenotypes that distinguish PFA from PFB, as well as high- and low-risk PFA. We also describe quantitative image predictors of aggressive EP tumors that might assist risk-profiling after surgery. Future studies could examine translating radiomics as an adjunct to EP risk assessment when considering therapy strategies or trial candidacy.
PMID: 34850171
ISSN: 1523-5866
CID: 5065682

Case 299

Radmanesh, Alireza; Young, Matthew G
History A 32-year-old woman presented to an ophthalmologist for bilateral blurry vision. She underwent MRI of the brain and orbits, which showed a focal abnormality within the pituitary gland. The patient was referred to an endocrinologist for further evaluation. Review of systems and physical examination by the endocrinologist revealed no symptoms or signs of endocrine dysfunction. Anterior pituitary hormone levels, including growth hormone, prolactin, thyroid stimulating hormone, follicular stimulating hormone, luteinizing hormone, and adrenocorticotropic hormone, were normal. Dynamic contrast-enhanced MRI of the sella and pituitary gland (Figs 1-3) and subsequent CT of the anterior skull base (Figs 4, 5) were performed.
PMID: 34694934
ISSN: 1527-1315
CID: 5042242

Machine Assist for Pediatric Posterior Fossa Tumor Diagnosis: A Multinational Study

Zhang, Michael; Wong, Samuel W; Wright, Jason N; Toescu, Sebastian; Mohammadzadeh, Maryam; Han, Michelle; Lummus, Seth; Wagner, Matthias W; Yecies, Derek; Lai, Hollie; Eghbal, Azam; Radmanesh, Alireza; Nemelka, Jordan; Harward, Stephen; Malinzak, Michael; Laughlin, Suzanne; Perreault, Sebastien; Braun, Kristina R M; Vossough, Arastoo; Poussaint, Tina; Goetti, Robert; Ertl-Wagner, Birgit; Ho, Chang Y; Oztekin, Ozgur; Ramaswamy, Vijay; Mankad, Kshitij; Vitanza, Nicholas A; Cheshier, Samuel H; Said, Mourad; Aquilina, Kristian; Thompson, Eric; Jaju, Alok; Grant, Gerald A; Lober, Robert M; Yeom, Kristen W
BACKGROUND:Clinicians and machine classifiers reliably diagnose pilocytic astrocytoma (PA) on magnetic resonance imaging (MRI) but less accurately distinguish medulloblastoma (MB) from ependymoma (EP). One strategy is to first rule out the most identifiable diagnosis. OBJECTIVE:To hypothesize a sequential machine-learning classifier could improve diagnostic performance by mimicking a clinician's strategy of excluding PA before distinguishing MB from EP. METHODS:We extracted 1800 total Image Biomarker Standardization Initiative (IBSI)-based features from T2- and gadolinium-enhanced T1-weighted images in a multinational cohort of 274 MB, 156 PA, and 97 EP. We designed a 2-step sequential classifier - first ruling out PA, and next distinguishing MB from EP. For each step, we selected the best performing model from 6-candidate classifier using a reduced feature set, and measured performance on a holdout test set with the microaveraged F1 score. RESULTS:Optimal diagnostic performance was achieved using 2 decision steps, each with its own distinct imaging features and classifier method. A 3-way logistic regression classifier first distinguished PA from non-PA, with T2 uniformity and T1 contrast as the most relevant IBSI features (F1 score 0.8809). A 2-way neural net classifier next distinguished MB from EP, with T2 sphericity and T1 flatness as most relevant (F1 score 0.9189). The combined, sequential classifier was with F1 score 0.9179. CONCLUSION/CONCLUSIONS:An MRI-based sequential machine-learning classifiers offer high-performance prediction of pediatric posterior fossa tumors across a large, multinational cohort. Optimization of this model with demographic, clinical, imaging, and molecular predictors could provide significant advantages for family counseling and surgical planning.
PMID: 34392363
ISSN: 1524-4040
CID: 5061032

Neuroimaging Features of Intracranial Hypertension in Pediatric Patients With New-Onset Idiopathic Seizures, a Comparison With Patients with Confirmed Diagnosis of Idiopathic Intracranial Hypertension: A Preliminary Study

Kamali, Arash; Aein, Azin; Naderi, Niyousha; Choi, Sally J; Doyle, Nathan; Butler, Ian J; Huisman, Thierry A G M; Bonfante, Eliana E; Sheikh-Bahaei, Nasim; Khanpara, Shekhar; Patel, Rajan P; Riascos, Roy F; Zhang, Xu; Tang, Rosa A; Radmanesh, Alireza
CONCLUSION:A cutoff value of 6.0 mm for optic nerve sheath dilation may be used as a screening imaging marker to suspect elevated opening pressure with specificity of 88% in pediatric patients with new-onset idiopathic seizures.
PMID: 34747259
ISSN: 1708-8283
CID: 5068352

Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction

Muckley, Matthew J; Riemenschneider, Bruno; Radmanesh, Alireza; Kim, Sunwoo; Jeong, Geunu; Ko, Jingyu; Jun, Yohan; Shin, Hyungseob; Hwang, Dosik; Mostapha, Mahmoud; Arberet, Simon; Nickel, Dominik; Ramzi, Zaccharie; Ciuciu, Philippe; Starck, Jean-Luc; Teuwen, Jonas; Karkalousos, Dimitrios; Zhang, Chaoping; Sriram, Anuroop; Huang, Zhengnan; Yakubova, Nafissa; Lui, Yvonne W; Knoll, Florian
Accelerating MRI scans is one of the principal outstanding problems in the MRI research community. Towards this goal, we hosted the second fastMRI competition targeted towards reconstructing MR images with subsampled k-space data. We provided participants with data from 7,299 clinical brain scans (de-identified via a HIPAA-compliant procedure by NYU Langone Health), holding back the fully-sampled data from 894 of these scans for challenge evaluation purposes. In contrast to the 2019 challenge, we focused our radiologist evaluations on pathological assessment in brain images. We also debuted a new Transfer track that required participants to submit models evaluated on MRI scanners from outside the training set. We received 19 submissions from eight different groups. Results showed one team scoring best in both SSIM scores and qualitative radiologist evaluations. We also performed analysis on alternative metrics to mitigate the effects of background noise and collected feedback from the participants to inform future challenges. Lastly, we identify common failure modes across the submissions, highlighting areas of need for future research in the MRI reconstruction community.
PMID: 33929957
ISSN: 1558-254x
CID: 4853732