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Article ; Online: Artificial Intelligence for Neuroimaging and Musculoskeletal Radiology: Overview of Current Commercial Algorithms.

Berson, Elisa R / Aboian, Mariam S / Malhotra, Ajay / Payabvash, Seyedmehdi

2023 Volume 58, Issue 2, Page(s) 178–183

MeSH term(s)	Humans ; Artificial Intelligence ; Algorithms ; Radiography ; Radiology/methods ; Neuroimaging
Language	English
Publishing date	2023-03-31
Publishing country	United States
Document type	Journal Article
ZDB-ID	80310-8
ISSN	1558-4658 ; 0037-198X
ISSN (online)	1558-4658
ISSN	0037-198X
DOI	10.1053/j.ro.2023.03.002
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Article: Clinical Evaluation of Nuclear Imaging Agents in Breast Cancer.

Li, Ziqi / Aboian, Mariam S / Zhu, Xiaohua / Marquez-Nostra, Bernadette

Cancers

2022 Volume 14, Issue 9

Abstract: Precision medicine is the customization of therapy for specific groups of patients using genetic or molecular profiling. Noninvasive imaging is one strategy for molecular profiling and is the focus of this review. The combination of imaging and therapy ... ...

Abstract	Precision medicine is the customization of therapy for specific groups of patients using genetic or molecular profiling. Noninvasive imaging is one strategy for molecular profiling and is the focus of this review. The combination of imaging and therapy for precision medicine gave rise to the field of theranostics. In breast cancer, the detection and quantification of therapeutic targets can help assess their heterogeneity, especially in metastatic disease, and may help guide clinical decisions for targeted treatments. Positron emission tomography (PET) or single-photon emission tomography (SPECT) imaging has the potential to play an important role in the molecular profiling of therapeutic targets in vivo for the selection of patients who are likely to respond to corresponding targeted therapy. In this review, we discuss the state-of-the-art nuclear imaging agents in clinical research for breast cancer. We reviewed 17 clinical studies on PET or SPECT agents that target 10 different receptors in breast cancer. We also discuss the limitations of the study designs and of the imaging agents in these studies. Finally, we offer our perspective on which imaging agents have the highest potential to be used in clinical practice in the future.
Language	English
Publishing date	2022-04-23
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2527080-1
ISSN	2072-6694
ISSN	2072-6694
DOI	10.3390/cancers14092103
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Article ; Online: Brain Tumor Imaging: Applications of Artificial Intelligence.

Afridi, Muhammad / Jain, Abhi / Aboian, Mariam / Payabvash, Seyedmehdi

Seminars in ultrasound, CT, and MR

2022 Volume 43, Issue 2, Page(s) 153–169

Abstract: 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, ... ...

Abstract	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.
MeSH term(s)	Artificial Intelligence ; Brain Neoplasms/diagnostic imaging ; Diagnostic Imaging ; Humans ; Image Processing, Computer-Assisted
Language	English
Publishing date	2022-02-11
Publishing country	United States
Document type	Journal Article ; Multicenter Study
ZDB-ID	1353113-x
ISSN	1558-5034 ; 0887-2171
ISSN (online)	1558-5034
ISSN	0887-2171
DOI	10.1053/j.sult.2022.02.005
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Article ; Online: Case 245.

Mamlouk, Mark D / Aboian, Mariam S / Glastonbury, Christine M

Radiology

2017 Volume 283, Issue 2, Page(s) 609–612

Language	English
Publishing date	2017-05
Publishing country	United States
Document type	Journal Article
ZDB-ID	80324-8
ISSN	1527-1315 ; 0033-8419
ISSN (online)	1527-1315
ISSN	0033-8419
DOI	10.1148/radiol.2017141150
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Article ; Online: Comparison of Volumetric and 2D Measurements and Longitudinal Trajectories in the Response Assessment of

Ramakrishnan, Divya / Brüningk, Sarah C / von Reppert, Marc / Memon, Fatima / Maleki, Nazanin / Aneja, Sanjay / Kazerooni, Anahita Fathi / Nabavizadeh, Ali / Lin, MingDe / Bousabarah, Khaled / Molinaro, Annette / Nicolaides, Theodore / Prados, Michael / Mueller, Sabine / Aboian, Mariam S

AJNR. American journal of neuroradiology

2024 Volume 45, Issue 4, Page(s) 475–482

Abstract: Background and purpose: Response on imaging is widely used to evaluate treatment efficacy in clinical trials of pediatric gliomas. While conventional criteria rely on 2D measurements, volumetric analysis may provide a more comprehensive response ... ...

Abstract	Background and purpose: Response on imaging is widely used to evaluate treatment efficacy in clinical trials of pediatric gliomas. While conventional criteria rely on 2D measurements, volumetric analysis may provide a more comprehensive response assessment. There is sparse research on the role of volumetrics in pediatric gliomas. Our purpose was to compare 2D and volumetric analysis with the assessment of neuroradiologists using the Brain Tumor Reporting and Data System (BT-RADS) in Materials and methods: Manual volumetric segmentations of whole and solid tumors were compared with 2D measurements in 31 participants (292 follow-up studies) in the Pacific Pediatric Neuro-Oncology Consortium 002 trial (NCT01748149). Two neuroradiologists evaluated responses using BT-RADS. Receiver operating characteristic analysis compared classification performance of 2D and volumetrics for partial response. Agreement between volumetric and 2D mathematically modeled longitudinal trajectories for 25 participants was determined using the model-estimated time to best response. Results: Of 31 participants, 20 had partial responses according to BT-RADS criteria. Receiver operating characteristic curves for the classification of partial responders at the time of first detection (median = 2 months) yielded an area under the curve of 0.84 (95% CI, 0.69-0.99) for 2D area, 0.91 (95% CI, 0.80-1.00) for whole-volume, and 0.92 (95% CI, 0.82-1.00) for solid volume change. There was no significant difference in the area under the curve between 2D and solid ( Conclusions: Although there was no overall difference between volumetrics and 2D in classifying partial response assessment using BT-RADS, further prospective studies will be critical to elucidate how the observed differences in tumor 2D and volumetric trajectories affect clinical decision-making and outcomes in some individuals.
MeSH term(s)	Child ; Humans ; Brain Neoplasms/diagnostic imaging ; Brain Neoplasms/genetics ; Brain Neoplasms/pathology ; Glioma/diagnostic imaging ; Glioma/genetics ; Glioma/therapy ; Magnetic Resonance Imaging/methods ; Prospective Studies ; Proto-Oncogene Proteins B-raf ; Treatment Outcome
Chemical Substances	BRAF protein, human (EC 2.7.11.1) ; Proto-Oncogene Proteins B-raf (EC 2.7.11.1)
Language	English
Publishing date	2024-04-08
Publishing country	United States
Document type	Clinical Trial ; Journal Article
ZDB-ID	603808-6
ISSN	1936-959X ; 0195-6108
ISSN (online)	1936-959X
ISSN	0195-6108
DOI	10.3174/ajnr.A8189
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Article: Comparing 3D, 2.5D, and 2D Approaches to Brain Image Auto-Segmentation.

Avesta, Arman / Hossain, Sajid / Lin, MingDe / Aboian, Mariam / Krumholz, Harlan M / Aneja, Sanjay

Bioengineering (Basel, Switzerland)

2023 Volume 10, Issue 2

Abstract: Deep-learning methods for auto-segmenting brain images either segment one slice of the image (2D), five consecutive slices of the image (2.5D), or an entire volume of the image (3D). Whether one approach is superior for auto-segmenting brain images is ... ...

Abstract	Deep-learning methods for auto-segmenting brain images either segment one slice of the image (2D), five consecutive slices of the image (2.5D), or an entire volume of the image (3D). Whether one approach is superior for auto-segmenting brain images is not known. We compared these three approaches (3D, 2.5D, and 2D) across three auto-segmentation models (capsule networks, UNets, and nnUNets) to segment brain structures. We used 3430 brain MRIs, acquired in a multi-institutional study, to train and test our models. We used the following performance metrics: segmentation accuracy, performance with limited training data, required computational memory, and computational speed during training and deployment. The 3D, 2.5D, and 2D approaches respectively gave the highest to lowest Dice scores across all models. 3D models maintained higher Dice scores when the training set size was decreased from 3199 MRIs down to 60 MRIs. 3D models converged 20% to 40% faster during training and were 30% to 50% faster during deployment. However, 3D models require 20 times more computational memory compared to 2.5D or 2D models. This study showed that 3D models are more accurate, maintain better performance with limited training data, and are faster to train and deploy. However, 3D models require more computational memory compared to 2.5D or 2D models.
Language	English
Publishing date	2023-02-01
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2746191-9
ISSN	2306-5354
ISSN	2306-5354
DOI	10.3390/bioengineering10020181
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Article ; Online: Author Correction: Topographic correlates of driver mutations and endogenous gene expression in pediatric diffuse midline gliomas and hemispheric high-grade gliomas.

Kazarian, Eve / Marks, Asher / Cui, Jin / Darbinyan, Armine / Tong, Elizabeth / Mueller, Sabine / Cha, Soonmee / Aboian, Mariam S

Scientific reports

2021 Volume 11, Issue 1, Page(s) 16638

Language	English
Publishing date	2021-08-11
Publishing country	England
Document type	Published Erratum
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-021-96015-1
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Article ; Online: Radiomics: A Primer on Processing Workflow and Analysis.

Avery, Emily / Sanelli, Pina C / Aboian, Mariam / Payabvash, Seyedmehdi

Seminars in ultrasound, CT, and MR

2022 Volume 43, Issue 2, Page(s) 142–146

Abstract: 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 ... ...

Abstract	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.
MeSH term(s)	Humans ; Image Processing, Computer-Assisted/methods ; Workflow
Language	English
Publishing date	2022-02-12
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	1353113-x
ISSN	1558-5034 ; 0887-2171
ISSN (online)	1558-5034
ISSN	0887-2171
DOI	10.1053/j.sult.2022.02.003
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Article ; Online: Evolution and implementation of radiographic response criteria in neuro-oncology.

Ramakrishnan, Divya / von Reppert, Marc / Krycia, Mark / Sala, Matthew / Mueller, Sabine / Aneja, Sanjay / Nabavizadeh, Ali / Galldiks, Norbert / Lohmann, Philipp / Raji, Cyrus / Ikuta, Ichiro / Memon, Fatima / Weinberg, Brent D / Aboian, Mariam S

Neuro-oncology advances

2023 Volume 5, Issue 1, Page(s) vdad118

Abstract: Radiographic response assessment in neuro-oncology is critical in clinical practice and trials. Conventional criteria, such as the MacDonald and response assessment in neuro-oncology (RANO) criteria, rely on bidimensional (2D) measurements of a single ... ...

Abstract	Radiographic response assessment in neuro-oncology is critical in clinical practice and trials. Conventional criteria, such as the MacDonald and response assessment in neuro-oncology (RANO) criteria, rely on bidimensional (2D) measurements of a single tumor cross-section. Although RANO criteria are established for response assessment in clinical trials, there is a critical need to address the complexity of brain tumor treatment response with multiple new approaches being proposed. These include volumetric analysis of tumor compartments, structured MRI reporting systems like the Brain Tumor Reporting and Data System, and standardized approaches to advanced imaging techniques to distinguish tumor response from treatment effects. In this review, we discuss the strengths and limitations of different neuro-oncology response criteria and summarize current research findings on the role of novel response methods in neuro-oncology clinical trials and practice.
Language	English
Publishing date	2023-09-13
Publishing country	England
Document type	Journal Article ; Review
ZDB-ID	3009682-0
ISSN	2632-2498 ; 2632-2498
ISSN (online)	2632-2498
ISSN	2632-2498
DOI	10.1093/noajnl/vdad118
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Article ; Online: Clinical implications of Peri-hematomal edema microperfusion fraction in intracerebral hemorrhage intravoxel incoherent motion imaging - A pilot study.

Abou Karam, Gaby / Tharmaseelan, Hishan / Aboian, Mariam S / Malhotra, Ajay / Gilmore, Emily J / Falcone, Guido J / de Havenon, Adam / Sheth, Kevin N / Payabvash, Seyedmehdi

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association

2023 Volume 32, Issue 11, Page(s) 107375

Abstract: Background and purpose: Perihematomal edema (PHE) represents the secondary brain injury after intracerebral hemorrhage (ICH). However, neurobiological characteristics of post-ICH parenchymal injury other than PHE volume have not been fully characterized. ...

Abstract	Background and purpose: Perihematomal edema (PHE) represents the secondary brain injury after intracerebral hemorrhage (ICH). However, neurobiological characteristics of post-ICH parenchymal injury other than PHE volume have not been fully characterized. Using intravoxel incoherent motion imaging (IVIM), we explored the clinical correlates of PHE diffusion and (micro)perfusion metrics in subacute ICH. Materials and methods: In 41 consecutive patients scanned 1-to-7 days after supratentorial ICH, we determined the mean diffusion (D), pseudo-diffusion (D), and perfusion fraction (F) within manually segmented PHE. Using univariable and multivariable statistics, we evaluated the relationship of these IVIM metrics with 3-month outcome based on the modified Rankin Scale (mRS). Results:* In our cohort, the average (± standard deviation) age of patients was 68.6±15.6 years, median (interquartile) baseline National Institute of Health Stroke Scale (NIHSS) was 7 (3-13), 11 (27 %) patients had poor outcomes (mRS>3), and 4 (10 %) deceased during the follow-up period. In univariable analyses, admission NIHSS (p < 0.001), ICH volume (p = 0.019), ICH+PHE volume (p = 0.016), and average F of the PHE (p = 0.005) had significant correlation with 3-month mRS. In multivariable model, the admission NIHSS (p = 0.006) and average F perfusion fraction of the PHE (p = 0.003) were predictors of 3-month mRS. Conclusion: The IVIM perfusion fraction (F) maps represent the blood flow within microvasculature. Our pilot study shows that higher PHE microperfusion in subacute ICH is associated with worse outcomes. Once validated in larger cohorts, IVIM metrics may provide insight into neurobiology of post-ICH secondary brain injury and identify at-risk patients who may benefit from neuroprotective therapy.
MeSH term(s)	Humans ; Middle Aged ; Aged ; Aged, 80 and over ; Pilot Projects ; Cerebral Hemorrhage/complications ; Cerebral Hemorrhage/diagnostic imaging ; Diffusion Magnetic Resonance Imaging ; Brain Neoplasms ; Edema ; Hematoma ; Brain Injuries ; Brain Edema/diagnostic imaging ; Brain Edema/etiology
Language	English
Publishing date	2023-09-20
Publishing country	United States
Document type	Journal Article
ZDB-ID	1131675-5
ISSN	1532-8511 ; 1052-3057
ISSN (online)	1532-8511
ISSN	1052-3057
DOI	10.1016/j.jstrokecerebrovasdis.2023.107375
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