Mariam Aboian, MD/PhD

• Ajay Malhotra
• Alexandria Brackett
• Anita Huttner
• Anne Marie Boustani
• Antonio Omuro
• Asher Marks
• Benjamin L. Judson
• Bernadette Marquez-Nostra
• Chi Liu
• Harlan Krumholz
• Harriet Kluger
• Jitendra Bhawnani
• Kanat Yalcin
• Ketu Mishra-Gorur
• Lawrence Saperstein
• Lawrence Staib
• Michal Kulon
• MingDe Lin
• Ming-Kai Chen
• Murat Gunel
• Nicholas Blondin
• Ondrej Blaha
• Richard Bronen
• Robert Fulbright
• Sacit Bulent Omay
• Sam Payabvash
• Sanjay Aneja
• Sarah Goldberg
• Sarah Weiss
• Takuya Toyonaga
• Tanyeri Barak
• Veronica Chiang
• Vidya Puthenpura
• Xi Wang
• Zeynep Erson Omay

Brain Neoplasms; Glioblastoma; Nuclear Medicine; Radiotherapy; Informatics; Molecular Imaging; Cognitive Neuroscience; Machine Learning; Mentoring

• Evaluating the diagnostic performance of 18 F-fluciclovine for detection of recurrent brain metastases after radiation therapy: Results from a prospective phase 2 trial.Kotecha R, Chiang V, Tom M, Nabavizadeh A, Zan E, Peddi S, Sulman E, Siegel B, Huang J, Brem S, Ware M, Kesari S, Parent E, Pope W, Holmes R, Chau A, Teoh E, Chao S, Aboian M. Evaluating the diagnostic performance of 18 F-fluciclovine for detection of recurrent brain metastases after radiation therapy: Results from a prospective phase 2 trial. Journal Of Clinical Oncology 2023, 41: 2001-2001. DOI: 10.1200/jco.2023.41.16_suppl.2001.
• PET/MRI Applications in Pediatric EpilepsyPedersen C, Aboian M, Messina S, Daldrup-Link H, Franceschi A. PET/MRI Applications in Pediatric Epilepsy. World Journal Of Nuclear Medicine 2023, 22: 078-086. PMID: 37223623, PMCID: PMC10202574, DOI: 10.1055/s-0043-1764303.
• Peritumoral edema measurements provide additional information to RANO-BM based assessment of lung cancer brain metastases after Gamma Knife therapy (P10-13.002)Kaur M, Cassinelli G, von Reppert M, Jekel L, Varghese S, Dixe I, Omuro A, Chiang V, Aboian M. Peritumoral edema measurements provide additional information to RANO-BM based assessment of lung cancer brain metastases after Gamma Knife therapy (P10-13.002). 2023, 3193. DOI: 10.1212/wnl.0000000000203091.
• 3D Capsule Networks for Brain Image SegmentationAvesta A, Hui Y, Aboian M, Duncan J, Krumholz H, Aneja S. 3D Capsule Networks for Brain Image Segmentation. American Journal Of Neuroradiology 2023, 44: 562-568. PMID: 37080721, PMCID: PMC10171390, DOI: 10.3174/ajnr.a7845.
• Artificial Intelligence for Neuroimaging and Musculoskeletal Radiology: Overview of Current Commercial AlgorithmsBerson E, Aboian M, Malhotra A, Payabvash S. Artificial Intelligence for Neuroimaging and Musculoskeletal Radiology: Overview of Current Commercial Algorithms. Seminars In Roentgenology 2023, 58: 178-183. PMID: 37087138, PMCID: PMC10122717, DOI: 10.1053/j.ro.2023.03.002.
• Automated Tumor Segmentation and Brain Tissue Extraction from Multiparametric MRI of Pediatric Brain Tumors: A Multi-Institutional StudyFathi Kazerooni A, Arif S, Madhogarhia R, Khalili N, Haldar D, Bagheri S, Familiar A, Anderson H, Haldar S, Tu W, Kim M, Viswanathan K, Muller S, Prados M, Kline C, Vidal L, Aboian M, Storm P, Resnick A, Ware J, Vossough A, Davatzikos C, Nabavizadeh A. Automated Tumor Segmentation and Brain Tissue Extraction from Multiparametric MRI of Pediatric Brain Tumors: A Multi-Institutional Study. Neuro-Oncology Advances 2023 DOI: 10.1093/noajnl/vdad027.
• ImmunoPET Imaging Identifies the Optimal Timepoint for Combination Therapy in Xenograft Models of Triple-Negative Breast CancerLi Z, Belitzky E, Blaha O, Cavaliere A, Katz S, Aboian M, Melegari L, Rajabimoghadam K, Kurpiewski S, Zhu X, Marquez-Nostra B. ImmunoPET Imaging Identifies the Optimal Timepoint for Combination Therapy in Xenograft Models of Triple-Negative Breast Cancer. Cancers 2023, 15: 1589. PMID: 36900378, PMCID: PMC10001369, DOI: 10.3390/cancers15051589.
• Comparing 3D, 2.5D, and 2D Approaches to Brain Image Auto-SegmentationAvesta A, Hossain S, Lin M, Aboian M, Krumholz H, Aneja S. Comparing 3D, 2.5D, and 2D Approaches to Brain Image Auto-Segmentation. Bioengineering 2023, 10: 181. PMID: 36829675, PMCID: PMC9952534, DOI: 10.3390/bioengineering10020181.
• Comparison of whole-body DW-MRI with 2-[18F]FDG PET for staging and treatment monitoring of children with Langerhans cell histiocytosisBaratto L, Nyalakonda R, Theruvath A, Sarrami A, Hawk K, Rashidi A, Shen S, States L, Aboian M, Jeng M, Daldrup-Link H. Comparison of whole-body DW-MRI with 2-[18F]FDG PET for staging and treatment monitoring of children with Langerhans cell histiocytosis. European Journal Of Nuclear Medicine And Molecular Imaging 2023, 50: 1689-1698. PMID: 36717409, PMCID: PMC10121877, DOI: 10.1007/s00259-023-06122-6.
• Brain PET Imaging Approach to Cognitive Impairment and DementiaSpano M, Roytman M, Aboian M, Saboury B, Franceschi A, Chiang G. Brain PET Imaging Approach to Cognitive Impairment and Dementia. PET Clinics 2023, 18: 103-113. PMID: 36442959, PMCID: PMC9713600, DOI: 10.1016/j.cpet.2022.09.006.
• Automated tumor segmentation and brain tissue extraction from multiparametric MRI of pediatric brain tumors: A multi-institutional studyFathi Kazerooni A, Arif S, Madhogarhia R, Khalili N, Haldar D, Bagheri S, Familiar A, Anderson H, Haldar S, Tu W, Chul Kim M, Viswanathan K, Muller S, Prados M, Kline C, Vidal L, Aboian M, Storm P, Resnick A, Ware J, Vossough A, Davatzikos C, Nabavizadeh A. Automated tumor segmentation and brain tissue extraction from multiparametric MRI of pediatric brain tumors: A multi-institutional study. Neuro-Oncology Advances 2023, 5: vdad027. PMID: 37051331, PMCID: PMC10084501, DOI: 10.1093/noajnl/vdad027.
• PropofolAboian M, Johnson J, Ginat D. Propofol. 2022, 281-283. DOI: 10.1007/978-3-031-08774-5_40.
• NIMG-02. PACS-INTEGRATED AUTO-SEGMENTATION WORKFLOW FOR BRAIN METASTASES USING NNU-NETJekel L, Bousabarah K, Lin M, Merkaj S, Kaur M, Avesta A, Aneja S, Omuro A, Chiang V, Scheffler B, Aboian M. NIMG-02. PACS-INTEGRATED AUTO-SEGMENTATION WORKFLOW FOR BRAIN METASTASES USING NNU-NET. Neuro-Oncology 2022, 24: vii162-vii162. PMCID: PMC9661012, DOI: 10.1093/neuonc/noac209.622.
• NIMG-07. APPLYING A GLIOMA-TRAINED DEEP LEARNING AUTO-SEGMENTATION TOOL ON BM PRE- AND POST-RADIOSURGERYKaur M, Varghese S, Jekel L, Tillmanns N, Merkaj S, Bousabarah K, Lin M, Bhawnani J, Chiang V, Aboian M. NIMG-07. APPLYING A GLIOMA-TRAINED DEEP LEARNING AUTO-SEGMENTATION TOOL ON BM PRE- AND POST-RADIOSURGERY. Neuro-Oncology 2022, 24: vii162-vii163. PMCID: PMC9660643, DOI: 10.1093/neuonc/noac209.626.
• NIMG-20. INCORPORATION OF AI-BASED AUTOSEGMENTATION AND CLASSIFICATION INTO NEURORADIOLOGY WORKFLOW: PACS-BASED AI TO BUILD YALE GLIOMA DATASETLost J, Tillmans N, Merkaj S, von Reppert M, Lin M, Bousabarah K, Huttner A, Aneja S, Omuro A, Aboian M, Avesta A. NIMG-20. INCORPORATION OF AI-BASED AUTOSEGMENTATION AND CLASSIFICATION INTO NEURORADIOLOGY WORKFLOW: PACS-BASED AI TO BUILD YALE GLIOMA DATASET. Neuro-Oncology 2022, 24: vii165-vii166. DOI: 10.1093/neuonc/noac209.638.
• NIMG-04. LONGITUDINAL TRACKING OF PERITUMORAL EDEMA VOLUME USING PACS-INTEGRATED TOOLS PROVIDES CRITICAL INFORMATION IN TREATMENT ASSESSMENT OF NSCLC BRAIN METASTASES AFTER RADIOSURGERYKaur M, Petersen G, von Reppert M, Jekel L, de Santo I, Varghese S, Chiang V, Aboian M. NIMG-04. LONGITUDINAL TRACKING OF PERITUMORAL EDEMA VOLUME USING PACS-INTEGRATED TOOLS PROVIDES CRITICAL INFORMATION IN TREATMENT ASSESSMENT OF NSCLC BRAIN METASTASES AFTER RADIOSURGERY. Neuro-Oncology 2022, 24: vii162-vii162. PMCID: PMC9661156, DOI: 10.1093/neuonc/noac209.624.
• NIMG-102. RAPNO-DEFINED SEGMENTATION AND VOLUMETRIC ASSESSMENT OF PEDIATRIC BRAIN TUMORS ON MULTI-PARAMETRIC MRI SCANS USING DEEP LEARNING; A ROBUST TOOL WITH POTENTIAL APPLICATION IN TUMOR RESPONSE ASSESSMENTKazerooni A, Madhogarhia R, Arif S, Ware J, Bagheri S, Haldar D, Anderson H, Familiar A, Vidal L, Aboian M, Storm P, Resnick A, Vossough A, Davatzikos C, Nabavizadeh A. NIMG-102. RAPNO-DEFINED SEGMENTATION AND VOLUMETRIC ASSESSMENT OF PEDIATRIC BRAIN TUMORS ON MULTI-PARAMETRIC MRI SCANS USING DEEP LEARNING; A ROBUST TOOL WITH POTENTIAL APPLICATION IN TUMOR RESPONSE ASSESSMENT. Neuro-Oncology 2022, 24: vii188-vii189. PMCID: PMC9660634, DOI: 10.1093/neuonc/noac209.720.
• Clinical implementation of artificial intelligence in neuroradiology with development of a novel workflow-efficient picture archiving and communication system-based automated brain tumor segmentation and radiomic feature extractionAboian M, Bousabarah K, Kazarian E, Zeevi T, Holler W, Merkaj S, Petersen G, Bahar R, Subramanian H, Sunku P, Schrickel E, Bhawnani J, Zawalich M, Mahajan A, Malhotra A, Payabvash S, Tocino I, Lin M, Westerhoff M. Clinical implementation of artificial intelligence in neuroradiology with development of a novel workflow-efficient picture archiving and communication system-based automated brain tumor segmentation and radiomic feature extraction. Frontiers In Neuroscience 2022, 16: 860208. PMID: 36312024, PMCID: PMC9606757, DOI: 10.3389/fnins.2022.860208.
• Deciphering the Clinical Trials of Immunotherapy in Glioblastoma: What a Neuroradiologist Needs to KnowVarzaneh F, Merkaj S, Petersen G, Bahar R, Jekel L, Pala A, Malhotra A, Ivanidze J, Aboian M. Deciphering the Clinical Trials of Immunotherapy in Glioblastoma: What a Neuroradiologist Needs to Know. Neurographics 2022, 12: 176-187. DOI: 10.3174/ng.2100055.
• Spectrum of qualitative and quantitative imaging of pilomyxoid, intermediate pilomyxoid and pilocytic astrocytomas in relation to their genetic alterationsFadel SA, von Reppert M, Kazarian E, Omay EZE, Marks A, Linder N, Hoffmann KT, Darbinyan A, Huttner A, Aboian MS. Spectrum of qualitative and quantitative imaging of pilomyxoid, intermediate pilomyxoid and pilocytic astrocytomas in relation to their genetic alterations. Neuroradiology 2022, 65: 195-205. PMID: 35984480, DOI: 10.1007/s00234-022-03027-3.
• LGG-52. Volumetry-based response characterization of recurrent pediatric low-grade gliomas in PNOC clinical Neuro-oncology trialsvon Reppert M, Lin M, Bousabarah K, Familiar A, Velasco R, Waanders A, Vossough A, Haddock A, Nicolaides T, Swanson K, Kazerooni A, Kline C, Nabavizadeh A, Haas-Kogan D, Prados M, Rubin J, Mueller S, Aboian M. LGG-52. Volumetry-based response characterization of recurrent pediatric low-grade gliomas in PNOC clinical Neuro-oncology trials. Neuro-Oncology 2022, 24: i100-i100. PMCID: PMC9165161, DOI: 10.1093/neuonc/noac079.364.
• Machine Learning Tools for Image-Based Glioma Grading and the Quality of Their Reporting: Challenges and OpportunitiesMerkaj S, Bahar RC, Zeevi T, Lin M, Ikuta I, Bousabarah K, Petersen G, Staib L, Payabvash S, Mongan JT, Cha S, Aboian MS. Machine Learning Tools for Image-Based Glioma Grading and the Quality of Their Reporting: Challenges and Opportunities. Cancers 2022, 14: 2623. PMID: 35681603, PMCID: PMC9179416, DOI: 10.3390/cancers14112623.
• PET/MRI in Pediatric Neuroimaging: Primer for Clinical PracticePedersen C, Aboian M, McConathy J, Daldrup-Link H, Franceschi A. PET/MRI in Pediatric Neuroimaging: Primer for Clinical Practice. American Journal Of Neuroradiology 2022, 43: 938-943. PMID: 35512826, PMCID: PMC9262074, DOI: 10.3174/ajnr.a7464.
• Clinical Evaluation of Nuclear Imaging Agents in Breast CancerLi Z, Aboian MS, Zhu X, Marquez-Nostra B. Clinical Evaluation of Nuclear Imaging Agents in Breast Cancer. Cancers 2022, 14: 2103. PMID: 35565232, PMCID: PMC9101155, DOI: 10.3390/cancers14092103.
• Machine Learning Models for Classifying High- and Low-Grade Gliomas: A Systematic Review and Quality of Reporting AnalysisBahar RC, Merkaj S, Petersen G, Tillmanns N, Subramanian H, Brim WR, Zeevi T, Staib L, Kazarian E, Lin M, Bousabarah K, Huttner AJ, Pala A, Payabvash S, Ivanidze J, Cui J, Malhotra A, Aboian MS. Machine Learning Models for Classifying High- and Low-Grade Gliomas: A Systematic Review and Quality of Reporting Analysis. Frontiers In Oncology 2022, 12: 856231. PMID: 35530302, PMCID: PMC9076130, DOI: 10.3389/fonc.2022.856231.
• Machine Learning in Differentiating Gliomas from Primary CNS Lymphomas: A Systematic Review, Reporting Quality, and Risk of Bias AssessmentPetersen G, Shatalov J, Verma T, Brim WR, Subramanian H, Brackett A, Bahar RC, Merkaj S, Zeevi T, Staib LH, Cui J, Omuro A, Bronen RA, Malhotra A, Aboian MS. Machine Learning in Differentiating Gliomas from Primary CNS Lymphomas: A Systematic Review, Reporting Quality, and Risk of Bias Assessment. American Journal Of Neuroradiology 2022, 43: 526-533. PMID: 35361577, PMCID: PMC8993193, DOI: 10.3174/ajnr.a7473.
• Machine Learning Applications for Differentiation of Glioma from Brain Metastasis—A Systematic ReviewJekel L, Brim WR, von Reppert M, Staib L, Petersen G, Merkaj S, Subramanian H, Zeevi T, Payabvash S, Bousabarah K, Lin M, Cui J, Brackett A, Mahajan A, Omuro A, Johnson MH, Chiang VL, Malhotra A, Scheffler B, Aboian MS. Machine Learning Applications for Differentiation of Glioma from Brain Metastasis—A Systematic Review. Cancers 2022, 14: 1369. PMID: 35326526, PMCID: PMC8946855, DOI: 10.3390/cancers14061369.
• TRAF7 Mutated Subgroups Differ in Sphenoid Wing Meningiomas with HyperostosisJin L, Vetsa S, Vasandani S, Nadar A, Youngblood M, Gupte T, Barak T, Yalcin K, Aguilera S, Mishra-Gorur K, Blondin N, Gorelick E, Omay S, Pointdujour-Lim R, Judson B, Alperovich M, Aboian M, Marianayagam N, McGuone D, Gunel M, Erson-Omay Z, Fulbright R, Moliterno J. TRAF7 Mutated Subgroups Differ in Sphenoid Wing Meningiomas with Hyperostosis. Journal Of Neurological Surgery Part B Skull Base 2022, 83: s1-s270. DOI: 10.1055/s-0042-1743640.
• Radiomics: A Primer on Processing Workflow and AnalysisAvery E, Sanelli PC, Aboian M, Payabvash S. Radiomics: A Primer on Processing Workflow and Analysis. Seminars In Ultrasound CT And MRI 2022, 43: 142-146. PMID: 35339254, PMCID: PMC8961004, DOI: 10.1053/j.sult.2022.02.003.
• Brain Tumor Imaging: Applications of Artificial IntelligenceAfridi M, Jain A, Aboian M, Payabvash S. Brain Tumor Imaging: Applications of Artificial Intelligence. Seminars In Ultrasound CT And MRI 2022, 43: 153-169. PMID: 35339256, PMCID: PMC8961005, DOI: 10.1053/j.sult.2022.02.005.
• Neuropathology of Chiari Malformation II with Chromosome X Alterations: An Autopsy Study in a 17-Month-Old and Review of LiteratureWang X, Habet V, Aboian M, Leelatian N, McGuone D, Morotti R, Kandil S, Darbinyan A. Neuropathology of Chiari Malformation II with Chromosome X Alterations: An Autopsy Study in a 17-Month-Old and Review of Literature. Journal Of Neuropathology & Experimental Neurology 2022, 81: nlab137-. PMID: 35139536, DOI: 10.1093/jnen/nlab137.
• Real-time PACS-integrated longitudinal brain metastasis tracking tool provides comprehensive assessment of treatment response to radiosurgeryPetersen G, Bousabarah K, Verma T, von Reppert M, Jekel L, Gordem A, Jang B, Merkaj S, Fadel S, Owens R, Omuro A, Chiang V, Ikuta I, Lin M, Aboian MS. Real-time PACS-integrated longitudinal brain metastasis tracking tool provides comprehensive assessment of treatment response to radiosurgery. Neuro-Oncology Advances 2022, 4: vdac116. PMID: 36043121, PMCID: PMC9412827, DOI: 10.1093/noajnl/vdac116.
• Identifying clinically applicable machine learning algorithms for glioma segmentation: recent advances and discoveriesTillmanns N, Lum AE, Cassinelli G, Merkaj S, Verma T, Zeevi T, Staib L, Subramanian H, Bahar RC, Brim W, Lost J, Jekel L, Brackett A, Payabvash S, Ikuta I, Lin M, Bousabarah K, Johnson MH, Cui J, Malhotra A, Omuro A, Turowski B, Aboian MS. Identifying clinically applicable machine learning algorithms for glioma segmentation: recent advances and discoveries. Neuro-Oncology Advances 2022, 4: vdac093. PMID: 36071926, PMCID: PMC9446682, DOI: 10.1093/noajnl/vdac093.
• Trends in Development of Novel Machine Learning Methods for the Identification of Gliomas in Datasets That Include Non-Glioma Images: A Systematic ReviewSubramanian H, Dey R, Brim WR, Tillmanns N, Petersen G, Brackett A, Mahajan A, Johnson M, Malhotra A, Aboian M. Trends in Development of Novel Machine Learning Methods for the Identification of Gliomas in Datasets That Include Non-Glioma Images: A Systematic Review. Frontiers In Oncology 2021, 11: 788819. PMID: 35004312, PMCID: PMC8733688, DOI: 10.3389/fonc.2021.788819.
• PET Image Denoising Using a Deep-Learning Method for Extremely Obese PatientsLiu H, Yousefi H, Mirian N, Lin M, Menard D, Gregory M, Aboian M, Boustani A, Chen M, Saperstein L, Pucar D, Kulon M, Liu C. PET Image Denoising Using a Deep-Learning Method for Extremely Obese Patients. IEEE Transactions On Radiation And Plasma Medical Sciences 2021, 6: 766-770. PMCID: PMC10241407, DOI: 10.1109/trpms.2021.3131999.
• PET Image Denoising Using a Deep-Learning Method for Extremely Obese PatientsLiu H, Yousefi H, Mirian N, Lin M, Menard D, Gregory M, Aboian M, Boustani A, Chen M, Saperstein L, Pucar D, Kulon M, Liu C. PET Image Denoising Using a Deep-Learning Method for Extremely Obese Patients. IEEE Transactions On Radiation And Plasma Medical Sciences 2021, 6: 766-770. PMID: 37284026, PMCID: PMC10241407, DOI: 10.1109/trpms.2021.3131999.
• Pediatric SpinePedersen C, Link H, Aboian M. Pediatric Spine. 2021, 765-777. DOI: 10.1007/978-3-030-82367-2_65.
• Amino Acid PET/MRI in Neuro-oncologyShooli H, Assadi M, Nabavizadeh S, Aboian M. Amino Acid PET/MRI in Neuro-oncology. 2021, 137-165. DOI: 10.1007/978-3-030-82367-2_14.
• Pediatric PET/MRI Neuroimaging: OverviewPedersen C, Messina S, Daldrup-Link H, Aboian M. Pediatric PET/MRI Neuroimaging: Overview. 2021, 737-740. DOI: 10.1007/978-3-030-82367-2_62.
• Pediatric Epilepsy: Non-oncologic Applications of PET/MRIMessina S, Pedersen C, Daldrup-Link H, Aboian M. Pediatric Epilepsy: Non-oncologic Applications of PET/MRI. 2021, 741-751. DOI: 10.1007/978-3-030-82367-2_63.
• Pediatric Brain and Head-Neck OncologyPedersen C, Messina S, Daldrup-Link H, Aboian M. Pediatric Brain and Head-Neck Oncology. 2021, 753-764. DOI: 10.1007/978-3-030-82367-2_64.
• [18F]-FDG PET/MR Neuroimaging: Focus on Neuro-Oncology ApplicationsShooli H, Assadi M, Aboian M. [18F]-FDG PET/MR Neuroimaging: Focus on Neuro-Oncology Applications. 2021, 89-98. DOI: 10.1007/978-3-030-82367-2_10.
• NIMG-64. TYPE OF BONY INVOLVEMENT PREDICTS GENOMIC SUBGROUP IN SPHENOID WING MENINGIOMASJin L, Youngblood M, Gupte T, Vetsa S, Nadar A, Barak T, Yalcin K, Aguilera S, Mishra-Gorur K, Blondin N, Omay S, Pointdujour-Lim R, Judson B, Alperovich M, Aboian M, McGuone D, Gunel M, Erson-Omay Z, Fulbright R, Moliterno J. NIMG-64. TYPE OF BONY INVOLVEMENT PREDICTS GENOMIC SUBGROUP IN SPHENOID WING MENINGIOMAS. Neuro-Oncology 2021, 23: vi144-vi144. PMCID: PMC8598770, DOI: 10.1093/neuonc/noab196.562.
• NIMG-23. MACHINE LEARNING METHODS IN GLIOMA GRADE PREDICTION: A SYSTEMATIC REVIEWBahar R, Merkaj S, Brim W, Subramanian H, Zeevi T, Kazarian E, Lin M, Bousabarah K, Payabvash S, Ivanidze J, Cui J, Tocino I, Malhotra A, Aboian M. NIMG-23. MACHINE LEARNING METHODS IN GLIOMA GRADE PREDICTION: A SYSTEMATIC REVIEW. Neuro-Oncology 2021, 23: vi133-vi133. PMCID: PMC8598529, DOI: 10.1093/neuonc/noab196.523.
• NIMG-35. MACHINE LEARNING GLIOMA GRADE PREDICTION LITERATURE: A TRIPOD ANALYSIS OF REPORTING QUALITYMerkaj S, Bahar R, Brim W, Subramanian H, Zeevi T, Kazarian E, Lin M, Bousabarah K, Payabvash S, Ivanidze J, Cui J, Tocino I, Malhotra A, Aboian M. NIMG-35. MACHINE LEARNING GLIOMA GRADE PREDICTION LITERATURE: A TRIPOD ANALYSIS OF REPORTING QUALITY. Neuro-Oncology 2021, 23: vi136-vi136. PMCID: PMC8598513, DOI: 10.1093/neuonc/noab196.535.
• NIMG-17. SYSTEMATIC REVIEW OF LITERATURE EVALUATING MACHINE LEARNING ALGORITHMS TO DEVELOP OUTCOME PREDICTION MODELS IN GLIOMA USING MOLECULAR IMAGING WITH AMINO ACID PETShatalov J, Brim W, Subramanian H, Bazaar J, Johnson M, Aboian M. NIMG-17. SYSTEMATIC REVIEW OF LITERATURE EVALUATING MACHINE LEARNING ALGORITHMS TO DEVELOP OUTCOME PREDICTION MODELS IN GLIOMA USING MOLECULAR IMAGING WITH AMINO ACID PET. Neuro-Oncology 2021, 23: vi131-vi131. PMCID: PMC8598491, DOI: 10.1093/neuonc/noab196.517.
• NIMG-38. MEASURING ADHERENCE TO TRIPOD OF ARTIFICIAL INTELLIGENCE PAPERS IN THE GLIOMA SEGMENTATIONTillmanns N, Lum A, Brim W, Subramanian H, Lin M, Bousabarah K, Malhotra A, cui J, Brackett A, Payabvash S, Ikuta I, Johnson M, Turowski B, Aboian M. NIMG-38. MEASURING ADHERENCE TO TRIPOD OF ARTIFICIAL INTELLIGENCE PAPERS IN THE GLIOMA SEGMENTATION. Neuro-Oncology 2021, 23: vi137-vi137. PMCID: PMC8598634, DOI: 10.1093/neuonc/noab196.537.
• NIMG-46. SYSTEMATIC LITERATURE REVIEW OF ARTIFICIAL INTELLIGENCE ALGORITHMS USING PRE-THERAPY MR IMAGING FOR GLIOMA MOLECULAR SUBTYPE CLASSIFICATIONLost J, Verma T, Tillmanns N, Brim W, Subramanian H, Ikuta I, Bronen R, Zucconi W, Lin M, Bousabarah K, Johnson M, Cui J, Malhotra A, Sabel M, Aboian M. NIMG-46. SYSTEMATIC LITERATURE REVIEW OF ARTIFICIAL INTELLIGENCE ALGORITHMS USING PRE-THERAPY MR IMAGING FOR GLIOMA MOLECULAR SUBTYPE CLASSIFICATION. Neuro-Oncology 2021, 23: vi139-vi139. DOI: 10.1093/neuonc/noab196.545.
• NIMG-67. A SYSTEMATIC REVIEW ON THE DEVELOPMENT OF MACHINE LEARNING MODELS FOR DIFFERENTIATING PCNSL FROM GLIOMASPetersen G, Shatalov J, Brim W, Subramanian H, cui J, Johnson M, Malhotra A, Aboian M, Brackett A. NIMG-67. A SYSTEMATIC REVIEW ON THE DEVELOPMENT OF MACHINE LEARNING MODELS FOR DIFFERENTIATING PCNSL FROM GLIOMAS. Neuro-Oncology 2021, 23: vi144-vi145. PMCID: PMC8598874, DOI: 10.1093/neuonc/noab196.565.
• NIMG-71. IDENTIFYING CLINICALLY APPLICABLE MACHINE LEARNING ALGORITHMS FOR GLIOMA SEGMENTATION USING A SYSTEMATIC LITERATURE REVIEWTillmanns N, Lum A, Brim W, Subramanian H, Lin M, Bousabarah K, Malhotra A, cui J, Brackett A, Payabvash S, Ikuta I, Johnson M, Turowski B, Aboian M. NIMG-71. IDENTIFYING CLINICALLY APPLICABLE MACHINE LEARNING ALGORITHMS FOR GLIOMA SEGMENTATION USING A SYSTEMATIC LITERATURE REVIEW. Neuro-Oncology 2021, 23: vi145-vi145. PMCID: PMC8598815, DOI: 10.1093/neuonc/noab196.568.
• Super-resolution PET Brain Imaging using Deep LearningRen S, Liu J, Xie H, Toyonaga T, Mirian N, Chen M, Aboian M, Carson R, Liu C. Super-resolution PET Brain Imaging using Deep Learning. 2021, 00: 1-6. DOI: 10.1109/nss/mic44867.2021.9875548.
• OTHR-11. Comprehensive Analysis of Driver Mutation Profile in a Cohort of Lung Cancer Patients Using Targeted Gene Panel Analysis with Focus on Brain Metastatic DiseaseKazarian M, Cui J, Tocino I, Mahajan A, Aboian M. OTHR-11. Comprehensive Analysis of Driver Mutation Profile in a Cohort of Lung Cancer Patients Using Targeted Gene Panel Analysis with Focus on Brain Metastatic Disease. Neuro-Oncology Advances 2021, 3: iii16-iii16. PMCID: PMC8351181, DOI: 10.1093/noajnl/vdab071.066.
• OTHR-12. The development of machine learning algorithms for the differentiation of glioma and brain metastases – a systematic reviewBrim W, Jekel L, Petersen G, Subramanian H, Zeevi T, Payabvash S, Bousabarah K, Lin M, Cui J, Brackett A, Mahajan A, Johnson M, Mahajan A, Aboian M. OTHR-12. The development of machine learning algorithms for the differentiation of glioma and brain metastases – a systematic review. Neuro-Oncology Advances 2021, 3: iii17-iii17. PMCID: PMC8351249, DOI: 10.1093/noajnl/vdab071.067.
• OTHR-06. PACS Lesion Tracking Tool provides real time automatic information on brain tumor metastasis growth curves and RECIST criteriaJang B, Lin M, Owens R, Bousabarah K, Mahajan A, Fadel S, Ikuta I, Tocino I, Aboian M. OTHR-06. PACS Lesion Tracking Tool provides real time automatic information on brain tumor metastasis growth curves and RECIST criteria. Neuro-Oncology Advances 2021, 3: iii15-iii15. DOI: 10.1093/noajnl/vdab071.061.
• OTHR-15. Assessment of TRIPOD adherence in articles developing machine learning models for differentiation of glioma from brain metastasisJekel L, Brim W, Petersen G, Subramanian H, Zeevi T, Payabvash S, Bousabarah K, Lin M, Cui J, Brackett A, Johnson M, Malhotra A, Aboian M. OTHR-15. Assessment of TRIPOD adherence in articles developing machine learning models for differentiation of glioma from brain metastasis. Neuro-Oncology Advances 2021, 3: iii17-iii18. PMCID: PMC8351195, DOI: 10.1093/noajnl/vdab071.070.
• Incidence and characteristics of metastatic intracranial lesions in stage III and IV melanoma: a single institute retrospective analysisSandhu MRS, Chiang VL, Tran T, Yu JB, Weiss S, Goldberg S, Aboian M, Kluger HM, Mahajan A. Incidence and characteristics of metastatic intracranial lesions in stage III and IV melanoma: a single institute retrospective analysis. Journal Of Neuro-Oncology 2021, 154: 197-203. PMID: 34351544, DOI: 10.1007/s11060-021-03813-8.
• Type of bony involvement predicts genomic subgroup in sphenoid wing meningiomasJin L, Youngblood MW, Gupte TP, Vetsa S, Nadar A, Barak T, Yalcin K, Aguilera SM, Mishra-Gorur K, Blondin NA, Gorelick E, Omay SB, Pointdujour-Lim R, Judson BL, Alperovich M, Aboian MS, McGuone D, Gunel M, Erson-Omay Z, Fulbright RK, Moliterno J. Type of bony involvement predicts genomic subgroup in sphenoid wing meningiomas. Journal Of Neuro-Oncology 2021, 154: 237-246. PMID: 34350560, DOI: 10.1007/s11060-021-03819-2.
• Radiation Necrosis with Proton Therapy in a Patient with Aarskog-Scott Syndrome and MedulloblastomaPuthenpura V, DeNunzio NJ, Zeng X, Giantsoudi D, Aboian M, Ebb D, Kahle KT, Yock TI, Marks AM. Radiation Necrosis with Proton Therapy in a Patient with Aarskog-Scott Syndrome and Medulloblastoma. International Journal Of Particle Therapy 2021, 8: 58-65. PMID: 35127977, PMCID: PMC8768897, DOI: 10.14338/ijpt-21-00013.1.
• Topographic correlates of driver mutations and endogenous gene expression in pediatric diffuse midline gliomas and hemispheric high-grade gliomasKazarian E, Marks A, Cui J, Darbinyan A, Tong E, Mueller S, Cha S, Aboian MS. Topographic correlates of driver mutations and endogenous gene expression in pediatric diffuse midline gliomas and hemispheric high-grade gliomas. Scientific Reports 2021, 11: 14377. PMID: 34257334, PMCID: PMC8277861, DOI: 10.1038/s41598-021-92943-0.
• Theranostics in Brain TumorsShooli H, Nemati R, Ahmadzadehfar H, Aboian M, Jafari E, Jokar N, Nabipour I, Dadgar H, Gholamrezanezhad A, Larvie M, Assadi M. Theranostics in Brain Tumors. PET Clinics 2021, 16: 397-418. PMID: 34053584, DOI: 10.1016/j.cpet.2021.03.005.
• Letter regarding "Contribution of PET imaging to radiotherapy planning and monitoring in glioma patients-a report of the PET/RANO group": 18F-fluciclovine and target volume delineation.Kotecha R, Aboian M, Nabavizadeh SA, Parent EE, Trifiletti DM, Chao ST. Letter regarding "Contribution of PET imaging to radiotherapy planning and monitoring in glioma patients-a report of the PET/RANO group": 18F-fluciclovine and target volume delineation. Neuro-Oncology 2021, 23: 1408-1409. PMID: 34081125, PMCID: PMC8328036, DOI: 10.1093/neuonc/noab097.
• LGG-09. CORRELATING GENETIC SIGNATURE OF PILOMYXOID ASTROCYTOMAS AND PILOCYTIC ASTROCYTOMAS WITH QUALITATIVE AND QUANTITATIVE MR IMAGING CHARACTERISTICSFadel S, Omay Z, Darbinyan A, Bronen R, Fulbright R, Mahajan A, Aboian M. LGG-09. CORRELATING GENETIC SIGNATURE OF PILOMYXOID ASTROCYTOMAS AND PILOCYTIC ASTROCYTOMAS WITH QUALITATIVE AND QUANTITATIVE MR IMAGING CHARACTERISTICS. Neuro-Oncology 2020, 22: iii367-iii368. PMCID: PMC7715503, DOI: 10.1093/neuonc/noaa222.392.
• LGG-53. PNOC001 (NCT01734512): A PHASE II STUDY OF EVEROLIMUS FOR RECURRENT OR PROGRESSIVE PEDIATRIC LOW-GRADE GLIOMAS (pLGG)Mueller S, Aboian M, Nazemi K, Gauvain K, Yoon J, Minturn J, Leary S, AbdelBaki M, Goldman S, Elster J, Resnick A, Molinaro A, Phillips J, Prados M, Haas-Kogan D. LGG-53. PNOC001 (NCT01734512): A PHASE II STUDY OF EVEROLIMUS FOR RECURRENT OR PROGRESSIVE PEDIATRIC LOW-GRADE GLIOMAS (pLGG). Neuro-Oncology 2020, 22: iii376-iii376. PMCID: PMC7715067, DOI: 10.1093/neuonc/noaa222.431.
• DDRE-12. PNOC001 (NCT01734512): A PHASE II STUDY OF EVEROLIMUS FOR RECURRENT OR PROGRESSIVE PEDIATRIC LOW-GRADE GLIOMAS (pLGG)Mueller S, Aboian M, Nazemi K, Gauvain K, Yoon J, Minturn J, Leary S, AbdelBaki M, Goldman S, Elster J, Resnick A, Molinaro A, Phillips J, Prados M, Haas-Kogan D. DDRE-12. PNOC001 (NCT01734512): A PHASE II STUDY OF EVEROLIMUS FOR RECURRENT OR PROGRESSIVE PEDIATRIC LOW-GRADE GLIOMAS (pLGG). Neuro-Oncology 2020, 22: ii63-ii64. PMCID: PMC7651691, DOI: 10.1093/neuonc/noaa215.257.
• Maximizing the use of batch production of 18F-FDOPA for imaging of brain tumors to increase availability of hybrid PET/MR imaging in clinical settingAboian M, Barajas R, Shatalov J, Ravanfar V, Bahroos E, Tong E, Taylor JW, Bush NO, Sneed P, Seo Y, Cha S, Hernandez-Pampaloni M. Maximizing the use of batch production of 18F-FDOPA for imaging of brain tumors to increase availability of hybrid PET/MR imaging in clinical setting. Neuro-Oncology Practice 2020, 8: 91-97. PMID: 33664973, PMCID: PMC7906261, DOI: 10.1093/nop/npaa065.
• Phase I study of vemurafenib in children with recurrent or progressive BRAFV600E mutant brain tumors: Pacific Pediatric Neuro-Oncology Consortium study (PNOC-002)Nicolaides T, Nazemi KJ, Crawford J, Kilburn L, Minturn J, Gajjar A, Gauvain K, Leary S, Dhall G, Aboian M, Robinson G, Long-Boyle J, Wang H, Molinaro AM, Mueller S, Prados M. Phase I study of vemurafenib in children with recurrent or progressive BRAFV600E mutant brain tumors: Pacific Pediatric Neuro-Oncology Consortium study (PNOC-002). Oncotarget 2020, 11: 1942-1952. PMID: 32523649, PMCID: PMC7260122, DOI: 10.18632/oncotarget.27600.
• 124I-MIBG PET/CT to Monitor Metastatic Disease in Children with Relapsed NeuroblastomaAboian M, Huang SY, Hernandez-Pampaloni M, Hawkins RA, VanBrocklin HF, Huh Y, Vo KT, Gustafson WC, Matthay KK, Seo Y. 124I-MIBG PET/CT to Monitor Metastatic Disease in Children with Relapsed Neuroblastoma. Journal Of Nuclear Medicine 2020, 62: 43-47. PMID: 32414950, PMCID: PMC9364877, DOI: 10.2967/jnumed.120.243139.
• Machine Learning Decision Tree Models for Differentiation of Posterior Fossa Tumors Using Diffusion Histogram Analysis and Structural MRI FindingsPayabvash S, Aboian M, Tihan T, Cha S. Machine Learning Decision Tree Models for Differentiation of Posterior Fossa Tumors Using Diffusion Histogram Analysis and Structural MRI Findings. Frontiers In Oncology 2020, 10: 71. PMID: 32117728, PMCID: PMC7018938, DOI: 10.3389/fonc.2020.00071.
• Diffusion Characteristics of Pediatric Diffuse Midline Gliomas with Histone H3-K27M Mutation Using Apparent Diffusion Coefficient Histogram AnalysisAboian MS, Tong E, Solomon DA, Kline C, Gautam A, Vardapetyan A, Tamrazi B, Li Y, Jordan CD, Felton E, Weinberg B, Braunstein S, Mueller S, Cha S. Diffusion Characteristics of Pediatric Diffuse Midline Gliomas with Histone H3-K27M Mutation Using Apparent Diffusion Coefficient Histogram Analysis. American Journal Of Neuroradiology 2019, 40: 1804-1810. PMID: 31694820, PMCID: PMC6907069, DOI: 10.3174/ajnr.a6302.
• Brainstem Injury in Pediatric Patients Receiving Posterior Fossa Photon RadiationDevine CA, Liu KX, Ioakeim-Ioannidou M, Susko M, Poussaint TY, Huisman TAGM, Aboian M, Brown D, Zaslowe-Dude C, Rao AD, Orlina LT, Rawal B, Mueller S, Marcus KJ, Terezakis SA, Braunstein SE, Haas-Kogan DA. Brainstem Injury in Pediatric Patients Receiving Posterior Fossa Photon Radiation. International Journal Of Radiation Oncology • Biology • Physics 2019, 105: 1034-1042. PMID: 31472183, DOI: 10.1016/j.ijrobp.2019.08.039.
• MLTI-18. PRECISION IMAGING OF METASTATIC AND PRIMARY BRAIN TUMORS AFTER RADIATION WITH 18F-FDOPA PET-MRI IS FEASIBLE AND COST EFFECTAboian M, Ravanfar V, Bahroos E, Tong E, Taylor J, Oberheim Bush N, Seo Y, Cha S, Pampaloni M. MLTI-18. PRECISION IMAGING OF METASTATIC AND PRIMARY BRAIN TUMORS AFTER RADIATION WITH 18F-FDOPA PET-MRI IS FEASIBLE AND COST EFFECT. Neuro-Oncology Advances 2019, 1: i18-i18. PMCID: PMC7213305, DOI: 10.1093/noajnl/vdz014.077.
• High‐grade neuroepithelial tumor with BCOR exon 15 internal tandem duplication—a comprehensive clinical, radiographic, pathologic, and genomic analysisFerris SP, Vega J, Aboian M, Lee JC, Van Ziffle J, Onodera C, Grenert JP, Saunders T, Chen Y, Banerjee A, Kline CN, Gupta N, Raffel C, Samuel D, Ruiz‐Diaz I, Magaki S, Wilson D, Neltner J, Al‐Hajri Z, Phillips JJ, Pekmezci M, Bollen AW, Tihan T, Schniederjan M, Cha S, Perry A, Solomon DA. High‐grade neuroepithelial tumor with BCOR exon 15 internal tandem duplication—a comprehensive clinical, radiographic, pathologic, and genomic analysis. Brain Pathology 2019, 30: 46-62. PMID: 31104347, PMCID: PMC6859193, DOI: 10.1111/bpa.12747.
• DIPG-15. PNOC-003: CLINICAL IMPACT OF A PRECISION MEDICINE STRATEGY FOR CHILDREN WITH DIFFUSE INTRINSIC PONTINE GLIOMAMueller S, Kline C, Kilburn L, Liang W, Jain P, Gupta N, Panditharatna E, Nazemi K, Magge S, Crawford J, Banerjee A, Packer R, Roos A, Zhang B, Zhu Y, Aboian M, Tamrazi B, Philips J, Solomon D, Molinaro A, Kuhn J, Byron S, Nazarian J, Resnick A, Berens M, Prados M. DIPG-15. PNOC-003: CLINICAL IMPACT OF A PRECISION MEDICINE STRATEGY FOR CHILDREN WITH DIFFUSE INTRINSIC PONTINE GLIOMA. Neuro-Oncology 2019, 21: ii71-ii71. PMCID: PMC6477269, DOI: 10.1093/neuonc/noz036.036.
• THER-26. PHARMACOKINETIC AND UPDATED OUTCOME DATA FROM PNOC-002: A SAFETY STUDY OF VEMURAFENIB, AN ORAL INHIBITOR OF BRAFV600E, IN CHILDREN WITH RECURRENT/REFRACTORY BRAFV600E MUTANT BRAIN TUMORSNicolaides T, Margol A, Gajjar A, Goldman S, Gauvain K, Kilburn L, Nazemi K, Minturn J, Leary S, Whipple N, Gururangan S, Crawford J, Long-Boyle J, Wang H, Ivaturi V, Aboian M, Molinaro A, Mueller S, Prados M. THER-26. PHARMACOKINETIC AND UPDATED OUTCOME DATA FROM PNOC-002: A SAFETY STUDY OF VEMURAFENIB, AN ORAL INHIBITOR OF BRAFV600E, IN CHILDREN WITH RECURRENT/REFRACTORY BRAFV600E MUTANT BRAIN TUMORS. Neuro-Oncology 2019, 21: ii119-ii119. PMCID: PMC6477532, DOI: 10.1093/neuonc/noz036.231.
• 3D Printed Absorber for Capturing Chemotherapy Drugs before They Spread through the BodyOh HJ, Aboian MS, Yi MYJ, Maslyn JA, Loo WS, Jiang X, Parkinson DY, Wilson MW, Moore T, Yee CR, Robbins GR, Barth FM, DeSimone JM, Hetts SW, Balsara NP. 3D Printed Absorber for Capturing Chemotherapy Drugs before They Spread through the Body. ACS Central Science 2019, 5: 419-427. PMID: 30937369, PMCID: PMC6439445, DOI: 10.1021/acscentsci.8b00700.
• Clinically Relevant and Minimally Invasive Tumor Surveillance of Pediatric Diffuse Midline Gliomas Using Patient-Derived Liquid BiopsyPanditharatna E, Kilburn LB, Aboian MS, Kambhampati M, Gordish-Dressman H, Magge SN, Gupta N, Myseros JS, Hwang EI, Kline C, Crawford JR, Warren KE, Cha S, Liang WS, Berens ME, Packer RJ, Resnick AC, Prados M, Mueller S, Nazarian J. Clinically Relevant and Minimally Invasive Tumor Surveillance of Pediatric Diffuse Midline Gliomas Using Patient-Derived Liquid Biopsy. Clinical Cancer Research 2018, 24: 5850-5859. PMID: 30322880, PMCID: PMC6279526, DOI: 10.1158/1078-0432.ccr-18-1345.
• A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using 18F-FDG PET of the Brain.Ding Y, Sohn JH, Kawczynski MG, Trivedi H, Harnish R, Jenkins NW, Lituiev D, Copeland TP, Aboian MS, Mari Aparici C, Behr SC, Flavell RR, Huang SY, Zalocusky KA, Nardo L, Seo Y, Hawkins RA, Hernandez Pampaloni M, Hadley D, Franc BL. A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using 18F-FDG PET of the Brain. Radiology 2018, 290: 456-464. PMID: 30398430, PMCID: PMC6358051, DOI: 10.1148/radiol.2018180958.
• Brainstem Toxicity in Pediatric Patients Receiving Posterior Fossa Photon RadiationDevine C, Liu K, Ioakeim-Ioannidou M, Susko M, Poussaint T, Huisman T, Aboian M, Brown D, Zaslowe-Dude C, Rao A, Orlina L, Rawal B, Mueller S, Marcus K, Terezakis S, Braunstein S, Haas-Kogan D. Brainstem Toxicity in Pediatric Patients Receiving Posterior Fossa Photon Radiation. International Journal Of Radiation Oncology • Biology • Physics 2018, 102: s51-s52. DOI: 10.1016/j.ijrobp.2018.06.153.
• Delayed Fluorodeoxyglucose Positron Emission Tomography Imaging in the Differentiation of Tumor Recurrence and Radiation Necrosis in Pediatric Central Nervous System Tumors: Case Report and Review of the LiteratureWadhwa EL, Franc BL, Aboian M, Kim JY, Pampaloni M, Nicolaides T. Delayed Fluorodeoxyglucose Positron Emission Tomography Imaging in the Differentiation of Tumor Recurrence and Radiation Necrosis in Pediatric Central Nervous System Tumors: Case Report and Review of the Literature. Cureus 2018, 10: e3364. PMID: 30510874, PMCID: PMC6257469, DOI: 10.7759/cureus.3364.
• Drug capture materials based on genomic DNA-functionalized magnetic nanoparticlesBlumenfeld CM, Schulz MD, Aboian MS, Wilson MW, Moore T, Hetts SW, Grubbs RH. Drug capture materials based on genomic DNA-functionalized magnetic nanoparticles. Nature Communications 2018, 9: 2870. PMID: 30030447, PMCID: PMC6054622, DOI: 10.1038/s41467-018-05305-2.
• DIPG-32. CLINICALLY RELEVANT AND MINIMALLY INVASIVE TUMOR SURVEILLANCE IN PEDIATRIC GLIOMAS USING LIQUID BIOMEPanditharatna E, Kilburn L, Aboian M, Kambhampati M, Gordish-Dressman H, Magge S, Gupta N, Myseros J, Hwang E, Crawford J, Warren K, Resnick A, Packer R, Prados M, Mueller S, Nazarian J. DIPG-32. CLINICALLY RELEVANT AND MINIMALLY INVASIVE TUMOR SURVEILLANCE IN PEDIATRIC GLIOMAS USING LIQUID BIOME. Neuro-Oncology 2018, 20: i55-i55. PMCID: PMC6012100, DOI: 10.1093/neuonc/noy059.125.
• Early detection of recurrent medulloblastoma: the critical role of diffusion-weighted imaging.Aboian MS, Kline CN, Li Y, Solomon DA, Felton E, Banerjee A, Braunstein SE, Mueller S, Dillon WP, Cha S. Early detection of recurrent medulloblastoma: the critical role of diffusion-weighted imaging. Neuro-Oncology Practice 2018, 5: 234-240. PMID: 30402262, PMCID: PMC6213943, DOI: 10.1093/nop/npx036.
• NIMG-71. DIFFUSION CHARACTERISTICS OF PEDIATRIC DIFFUSE MIDLINE GLIOMAS WITH HISTONE H3 K27M MUTATION USING APPARENT DIFFUSION COEFFICIENT HISTOGRAM ANALYSISAboian M, Solomon D, Kline-Nunnally C, Vardapetyan A, Tong E, Li Y, Felton E, Braunstein S, Mueller S, Cha S. NIMG-71. DIFFUSION CHARACTERISTICS OF PEDIATRIC DIFFUSE MIDLINE GLIOMAS WITH HISTONE H3 K27M MUTATION USING APPARENT DIFFUSION COEFFICIENT HISTOGRAM ANALYSIS. Neuro-Oncology 2017, 19: vi158-vi158. PMCID: PMC5692475, DOI: 10.1093/neuonc/nox168.643.
• PDCT-19. A SAFETY STUDY OF VEMURAFENIB, AN ORAL INHIBITOR OF BRAFV600E, IN CHILDREN WITH RECURRENT/REFRACTORY BRAFV600E MUTANT BRAIN TUMORS: PNOC-002Nicolaides T, Nazemi K, Crawford J, Kilburn L, Minturn J, Gajjar A, Gauvain K, Leary S, Dhall G, Aboian M, Robinson G, Molinaro A, Mueller S, Prados M. PDCT-19. A SAFETY STUDY OF VEMURAFENIB, AN ORAL INHIBITOR OF BRAFV600E, IN CHILDREN WITH RECURRENT/REFRACTORY BRAFV600E MUTANT BRAIN TUMORS: PNOC-002. Neuro-Oncology 2017, 19: vi188-vi188. PMCID: PMC5692489, DOI: 10.1093/neuonc/nox168.761.
• Case 245: Erdheim-Chester Disease.Mamlouk MD, Aboian MS, Glastonbury CM. Case 245: Erdheim-Chester Disease. Radiology 2017, 284: 910-917. PMID: 28825890, DOI: 10.1148/radiol.2017141151.
• Case-based review: pediatric medulloblastomaKline CN, Packer RJ, Hwang EI, Raleigh DR, Braunstein S, Raffel C, Bandopadhayay P, Solomon DA, Aboian M, Cha S, Mueller S. Case-based review: pediatric medulloblastoma. Neuro-Oncology Practice 2017, 4: 138-150. PMID: 29692919, PMCID: PMC5909805, DOI: 10.1093/nop/npx011.
• MEDU-40. DETECTION OF NON-ENHANCING RECURRENT MEDULLOBLASTOMA ON DIFFUSION WEIGHTED IMAGINGAboian M, Kline C, Li Y, Solomon D, Banerjee A, Braunstein S, Mueller S, Dillon W, Cha S. MEDU-40. DETECTION OF NON-ENHANCING RECURRENT MEDULLOBLASTOMA ON DIFFUSION WEIGHTED IMAGING. Neuro-Oncology 2017, 19: iv46-iv46. PMCID: PMC5475162, DOI: 10.1093/neuonc/nox083.190.
• Case 245.Mamlouk MD, Aboian MS, Glastonbury CM. Case 245. Radiology 2017, 283: 609-612. PMID: 28418827, DOI: 10.1148/radiol.2017141150.
• Imaging Characteristics of Pediatric Diffuse Midline Gliomas with Histone H3 K27M MutationAboian MS, Solomon DA, Felton E, Mabray MC, Villanueva-Meyer JE, Mueller S, Cha S. Imaging Characteristics of Pediatric Diffuse Midline Gliomas with Histone H3 K27M Mutation. American Journal Of Neuroradiology 2017, 38: 795-800. PMID: 28183840, PMCID: PMC5394943, DOI: 10.3174/ajnr.a5076.
• Targeted next-generation sequencing of pediatric neuro-oncology patients improves diagnosis, identifies pathogenic germline mutations, and directs targeted therapy.Kline CN, Joseph NM, Grenert JP, van Ziffle J, Talevich E, Onodera C, Aboian M, Cha S, Raleigh DR, Braunstein S, Torkildson J, Samuel D, Bloomer M, Campomanes AGA, Banerjee A, Butowski N, Raffel C, Tihan T, Bollen AW, Phillips JJ, Korn WM, Yeh I, Bastian BC, Gupta N, Mueller S, Perry A, Nicolaides T, Solomon DA. Targeted next-generation sequencing of pediatric neuro-oncology patients improves diagnosis, identifies pathogenic germline mutations, and directs targeted therapy. Neuro-Oncology 2016, 19: 699-709. PMID: 28453743, PMCID: PMC5464451, DOI: 10.1093/neuonc/now254.
• In vitro clearance of doxorubicin with a DNA-based filtration device designed for intravascular use with intra-arterial chemotherapyAboian MS, Yu JF, Gautam A, Sze CH, Yang JK, Chan J, Lillaney PV, Jordan CD, Oh HJ, Wilson DM, Patel AS, Wilson MW, Hetts SW. In vitro clearance of doxorubicin with a DNA-based filtration device designed for intravascular use with intra-arterial chemotherapy. Biomedical Microdevices 2016, 18: 98. PMID: 27778226, PMCID: PMC5441460, DOI: 10.1007/s10544-016-0124-5.
• Apparent diffusion coefficient and pituitary macroadenomas: pre-operative assessment of tumor atypiaTamrazi B, Pekmezci M, Aboian M, Tihan T, Glastonbury CM. Apparent diffusion coefficient and pituitary macroadenomas: pre-operative assessment of tumor atypia. Pituitary 2016, 20: 195-200. PMID: 27734275, DOI: 10.1007/s11102-016-0759-5.
• TB-02UPFRONT, REAL-TIME TUMOR AND GERMLINE SEQUENCING OF PEDIATRIC BRAIN TUMOR PATIENTS: THE UCSF EXPERIENCEKline C, Solomon D, Perry A, Bastian B, Joseph N, Phillips J, Tihan T, Yeh I, Van Ziffle J, Grenert J, Bollen A, Mueller S, Banerjee A, Gupta N, Raffel C, Braunstein S, Cha S, Aboian M, Samuel D, Torkildson J, Campomanes A, Nicolaides T. TB-02UPFRONT, REAL-TIME TUMOR AND GERMLINE SEQUENCING OF PEDIATRIC BRAIN TUMOR PATIENTS: THE UCSF EXPERIENCE. Neuro-Oncology 2016, 18: iii169.2-iii169. PMCID: PMC4903852, DOI: 10.1093/neuonc/now084.02.
• HG-92IMAGING CHARACTERISTICS OF PEDIATRIC DIFFUSE MIDLINE GLIOMAS BASED ON THE PRESENCE OF A POOR PROGNOSTIC MARKER HISTONE H3 K27M MUTATIONAboian M, Solomon D, Felton E, Mueller S, Cha S. HG-92IMAGING CHARACTERISTICS OF PEDIATRIC DIFFUSE MIDLINE GLIOMAS BASED ON THE PRESENCE OF A POOR PROGNOSTIC MARKER HISTONE H3 K27M MUTATION. Neuro-Oncology 2016, 18: iii69-iii69. PMCID: PMC4903465, DOI: 10.1093/neuonc/now073.88.
• A Family of Tetraspans Organizes Cargo for Sorting into Multivesicular BodiesMacDonald C, Payne JA, Aboian M, Smith W, Katzmann DJ, Piper RC. A Family of Tetraspans Organizes Cargo for Sorting into Multivesicular Bodies. Developmental Cell 2015, 33: 328-342. PMID: 25942624, PMCID: PMC4421094, DOI: 10.1016/j.devcel.2015.03.007.
• PropofolAboian M, Johnson J, Ginat D. Propofol. 2015, 347-349. DOI: 10.1007/978-3-319-12715-6_50.
• SISCOM in Children with Tuberous Sclerosis Complex-Related EpilepsyAboian MS, Wong-Kisiel LC, Rank M, Wetjen NM, Wirrell EC, Witte RJ. SISCOM in Children with Tuberous Sclerosis Complex-Related Epilepsy. Pediatric Neurology 2011, 45: 83-88. PMID: 21763947, DOI: 10.1016/j.pediatrneurol.2011.05.001.
• The putative role of the venous system in the genesis of vascular malformations.Aboian MS, Daniels DJ, Rammos SK, Pozzati E, Lanzino G. The putative role of the venous system in the genesis of vascular malformations. Neurosurgical FOCUS 2009, 27: e9. PMID: 19877799, DOI: 10.3171/2009.8.focus09161.
• Mesial Temporal Sclerosis After Posterior Reversible Encephalopathy SyndromeAboian MS, Junna MR, Krecke KN, Wirrell EC. Mesial Temporal Sclerosis After Posterior Reversible Encephalopathy Syndrome. Pediatric Neurology 2009, 41: 226-228. PMID: 19664544, DOI: 10.1016/j.pediatrneurol.2009.03.007.
• Characterization of Multiple Multivesicular Body Sorting Determinants within Sna3: A Role for the Ubiquitin Ligase Rsp5Oestreich AJ, Aboian M, Lee J, Azmi I, Payne J, Issaka R, Davies BA, Katzmann DJ. Characterization of Multiple Multivesicular Body Sorting Determinants within Sna3: A Role for the Ubiquitin Ligase Rsp5. Molecular Biology Of The Cell 2006, 18: 707-720. PMID: 17182849, PMCID: PMC1783786, DOI: 10.1091/mbc.e06-08-0680.
• Protein binding to expanded telomere repeats in Tetrahymena thermophila.McGuire JM, Gana JA, Petcherskaia M, Kirk KE. Protein binding to expanded telomere repeats in Tetrahymena thermophila. The Journal Of Eukaryotic Microbiology 2003, 50: 341-8. PMID: 14563172, DOI: 10.1111/j.1550-7408.2003.tb00146.x.
• Loss of cap structure causes mitotic defect in Tetrahymena thermophila telomerase mutants.Petcherskaia M, McGuire JM, Pherson JM, Kirk KE. Loss of cap structure causes mitotic defect in Tetrahymena thermophila telomerase mutants. Chromosoma 2003, 111: 429-37. PMID: 12707780, DOI: 10.1007/s00412-003-0233-9.