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Article ; Online: Repeatability of Automated Image Segmentation with BraTumIA in Patients with Recurrent Glioblastoma.

Abu Khalaf, N / Desjardins, A / Vredenburgh, J J / Barboriak, D P

AJNR. American journal of neuroradiology

2021 Volume 42, Issue 6, Page(s) 1080–1086

Abstract: Background and purpose: Despite high interest in machine-learning algorithms for automated segmentation of MRIs of patients with brain tumors, there are few reports on the variability of segmentation results. The purpose of this study was to obtain ... ...

Abstract	Background and purpose: Despite high interest in machine-learning algorithms for automated segmentation of MRIs of patients with brain tumors, there are few reports on the variability of segmentation results. The purpose of this study was to obtain benchmark measures of repeatability for a widely accessible software program, BraTumIA (Versions 1.2 and 2.0), which uses a machine-learning algorithm to segment tumor features on contrast-enhanced brain MR imaging. Materials and methods: Automatic segmentation of enhancing tumor, tumor edema, nonenhancing tumor, and necrosis was performed on repeat MR imaging scans obtained approximately 2 days apart in 20 patients with recurrent glioblastoma. Measures of repeatability and spatial overlap, including repeatability and Dice coefficients, are reported. Results: Larger volumes of enhancing tumor were obtained on later compared with earlier scans (mean, 26.3 versus 24.2 mL for BraTumIA 1.2; Conclusions: Repeatability and overlap metrics varied by segmentation type, with better performance for segmentations of enhancing tumor and tumor edema compared with other components. Incomplete washout of gadolinium contrast agents could account for increasing enhancing tumor volumes on later scans.
MeSH term(s)	Algorithms ; Brain Neoplasms/diagnostic imaging ; Glioblastoma/diagnostic imaging ; Humans ; Image Processing, Computer-Assisted ; Magnetic Resonance Imaging ; Tumor Burden
Language	English
Publishing date	2021-03-18
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	603808-6
ISSN	1936-959X ; 0195-6108
ISSN (online)	1936-959X
ISSN	0195-6108
DOI	10.3174/ajnr.A7071
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Article ; Online: Ethical Considerations and Fairness in the Use of Artificial Intelligence for Neuroradiology.

Filippi, C G / Stein, J M / Wang, Z / Bakas, S / Liu, Y / Chang, P D / Lui, Y / Hess, C / Barboriak, D P / Flanders, A E / Wintermark, M / Zaharchuk, G / Wu, O

AJNR. American journal of neuroradiology

2023 Volume 44, Issue 11, Page(s) 1242–1248

Abstract: In this review, concepts of algorithmic bias and fairness are defined qualitatively and mathematically. Illustrative examples are given of what can go wrong when unintended bias or unfairness in algorithmic development occurs. The importance of ... ...

Abstract	In this review, concepts of algorithmic bias and fairness are defined qualitatively and mathematically. Illustrative examples are given of what can go wrong when unintended bias or unfairness in algorithmic development occurs. The importance of explainability, accountability, and transparency with respect to artificial intelligence algorithm development and clinical deployment is discussed. These are grounded in the concept of "primum no nocere" (first, do no harm). Steps to mitigate unfairness and bias in task definition, data collection, model definition, training, testing, deployment, and feedback are provided. Discussions on the implementation of fairness criteria that maximize benefit and minimize unfairness and harm to neuroradiology patients will be provided, including suggestions for neuroradiologists to consider as artificial intelligence algorithms gain acceptance into neuroradiology practice and become incorporated into routine clinical workflow.
MeSH term(s)	Humans ; Artificial Intelligence ; Algorithms ; Radiologists ; Workflow
Language	English
Publishing date	2023-08-31
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	603808-6
ISSN	1936-959X ; 0195-6108
ISSN (online)	1936-959X
ISSN	0195-6108
DOI	10.3174/ajnr.A7963
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Article ; Online: Artificial Intelligence in Neuroradiology: Current Status and Future Directions.

Lui, Y W / Chang, P D / Zaharchuk, G / Barboriak, D P / Flanders, A E / Wintermark, M / Hess, C P / Filippi, C G

AJNR. American journal of neuroradiology

2020 Volume 41, Issue 8, Page(s) E52–E59

Abstract: Fueled by new techniques, computational tools, and broader availability of imaging data, artificial intelligence has the potential to transform the practice of neuroradiology. The recent exponential increase in publications related to artificial ... ...

Abstract	Fueled by new techniques, computational tools, and broader availability of imaging data, artificial intelligence has the potential to transform the practice of neuroradiology. The recent exponential increase in publications related to artificial intelligence and the central focus on artificial intelligence at recent professional and scientific radiology meetings underscores the importance. There is growing momentum behind leveraging artificial intelligence techniques to improve workflow and diagnosis and treatment and to enhance the value of quantitative imaging techniques. This article explores the reasons why neuroradiologists should care about the investments in new artificial intelligence applications, highlights current activities and the roles neuroradiologists are playing, and renders a few predictions regarding the near future of artificial intelligence in neuroradiology.
MeSH term(s)	Artificial Intelligence/trends ; Humans ; Neurology/methods ; Neurology/trends ; Radiology/methods ; Radiology/trends
Language	English
Publishing date	2020-07-30
Publishing country	United States
Document type	Journal Article
ZDB-ID	603808-6
ISSN	1936-959X ; 0195-6108
ISSN (online)	1936-959X
ISSN	0195-6108
DOI	10.3174/ajnr.A6681
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Article ; Online: Quantitative Delta T1 (dT1) as a Replacement for Adjudicated Central Reader Analysis of Contrast-Enhancing Tumor Burden: A Subanalysis of the American College of Radiology Imaging Network 6677/Radiation Therapy Oncology Group 0625 Multicenter Brain Tumor Trial.

Schmainda, K M / Prah, M A / Zhang, Z / Snyder, B S / Rand, S D / Jensen, T R / Barboriak, D P / Boxerman, J L

AJNR. American journal of neuroradiology

2019 Volume 40, Issue 7, Page(s) 1132–1139

Abstract: Background and purpose: Brain tumor clinical trials requiring solid tumor assessment typically rely on the 2D manual delineation of enhancing tumors by ≥2 expert readers, a time-consuming step with poor interreader agreement. As a solution, we developed ...

Abstract	Background and purpose: Brain tumor clinical trials requiring solid tumor assessment typically rely on the 2D manual delineation of enhancing tumors by ≥2 expert readers, a time-consuming step with poor interreader agreement. As a solution, we developed quantitative dT1 maps for the delineation of enhancing lesions. This retrospective analysis compares dT1 with 2D manual delineation of enhancing tumors acquired at 2 time points during the post therapeutic surveillance period of the American College of Radiology Imaging Network 6677/Radiation Therapy Oncology Group 0625 (ACRIN 6677/RTOG 0625) clinical trial. Materials and methods: Patients enrolled in ACRIN 6677/RTOG 0625, a multicenter, randomized Phase II trial of bevacizumab in recurrent glioblastoma, underwent standard MR imaging before and after treatment initiation. For 123 patients from 23 institutions, both 2D manual delineation of enhancing tumors and dT1 datasets were evaluable at weeks 8 ( Results: For identification of progression, dT1 and adjudicated 2D manual delineation of enhancing tumor reads were in perfect agreement at week 8, with 73.7% agreement at week 16. Both methods showed significant differences in overall survival at each time point. When nonprogressors were further divided into responders versus nonresponders/nonprogressors, the agreement decreased to 70.3% and 52.6%, yet dT1 showed a significant difference in overall survival at week 8 ( Conclusions: This study shows that dT1 can predict early progression comparable with the standard method but offers the potential for substantial time and cost savings for clinical trials.
MeSH term(s)	Adult ; Aged ; Antineoplastic Agents, Immunological/therapeutic use ; Bevacizumab/therapeutic use ; Brain Neoplasms/diagnostic imaging ; Brain Neoplasms/drug therapy ; Brain Neoplasms/pathology ; Female ; Glioblastoma/diagnostic imaging ; Glioblastoma/drug therapy ; Glioblastoma/pathology ; Humans ; Image Interpretation, Computer-Assisted/methods ; Magnetic Resonance Imaging/methods ; Male ; Middle Aged ; Neuroimaging/methods ; Retrospective Studies ; Tumor Burden
Chemical Substances	Antineoplastic Agents, Immunological ; Bevacizumab (2S9ZZM9Q9V)
Language	English
Publishing date	2019-06-27
Publishing country	United States
Document type	Clinical Trial, Phase II ; Journal Article ; Multicenter Study ; Randomized Controlled Trial ; Research Support, Non-U.S. Gov't
ZDB-ID	603808-6
ISSN	1936-959X ; 0195-6108
ISSN (online)	1936-959X
ISSN	0195-6108
DOI	10.3174/ajnr.A6110
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Article ; Online: Assessing the Performance of Artificial Intelligence Models: Insights from the American Society of Functional Neuroradiology Artificial Intelligence Competition.

Jiang, Bin / Ozkara, Burak Berksu / Zhu, Guangming / Boothroyd, Derek / Allen, Jason W / Barboriak, Daniel P / Chang, Peter / Chan, Cynthia / Chaudhari, Ruchir / Chen, Hui / Chukus, Anjeza / Ding, Victoria / Douglas, David / Filippi, Christopher G / Flanders, Adam E / Godwin, Ryan / Hashmi, Syed / Hess, Christopher / Hsu, Kevin /

Lui, Yvonne W / Maldjian, Joseph A / Michel, Patrik / Nalawade, Sahil S / Patel, Vishal / Raghavan, Prashant / Sair, Haris I / Tanabe, Jody / Welker, Kirk / Whitlow, Chris / Zaharcuk, Greg / Wintermark, Max

AJNR. American journal of neuroradiology

2024

Abstract: Background and purpose: Artificial intelligence (AI) models in radiology are frequently developed and validated using datasets from a single institution and are rarely tested on independent, external datasets, raising questions about their ... ...

Abstract	Background and purpose: Artificial intelligence (AI) models in radiology are frequently developed and validated using datasets from a single institution and are rarely tested on independent, external datasets, raising questions about their generalizability and applicability in clinical practice. The American Society of Functional Neuroradiology (ASFNR) organized a multi-center AI competition to evaluate the proficiency of developed models in identifying various pathologies on NCCT, assessing age-based normality and estimating medical urgency. Materials and methods: In total, 1201 anonymized, full-head NCCT clinical scans from five institutions were pooled to form the dataset. The dataset encompassed normal studies as well as pathologies including acute ischemic stroke, intracranial hemorrhage, traumatic brain injury, and mass effect (detection of these-task 1). NCCTs were also assessed to determine if findings were consistent with expected brain changes for the patient's age (task 2: age-based normality assessment) and to identify any abnormalities requiring immediate medical attention (task 3: evaluation of findings for urgent intervention). Five neuroradiologists labeled each NCCT, with consensus interpretations serving as the ground truth. The competition was announced online, inviting academic institutions and companies. Independent central analysis assessed each model's performance. Accuracy, sensitivity, specificity, positive and negative predictive values, and receiver operating characteristic (ROC) curves were generated for each AI model, along with the area under the ROC curve (AUROC). Results: 1177 studies were processed by four teams. The median age of patients was 62, with an interquartile range of 33. 19 teams from various academic institutions registered for the competition. Of these, four teams submitted their final results. No commercial entities participated in the competition. For task 1, AUROCs ranged from 0.49 to 0.59. For task 2, two teams completed the task with AUROC values of 0.57 and 0.52. For task 3, teams had little to no agreement with the ground truth. Conclusions: To assess the performance of AI models in real-world clinical scenarios, we analyzed their performance in the ASFNR AI Competition. The first ASFNR Competition underscored the gap between expectation and reality; the models largely fell short in their assessments. As the integration of AI tools into clinical workflows increases, neuroradiologists must carefully recognize the capabilities, constraints, and consistency of these technologies. Before institutions adopt these algorithms, thorough validation is essential to ensure acceptable levels of performance in clinical settings.
Language	English
Publishing date	2024-04-25
Publishing country	United States
Document type	Journal Article
ZDB-ID	603808-6
ISSN	1936-959X ; 0195-6108
ISSN (online)	1936-959X
ISSN	0195-6108
DOI	10.3174/ajnr.A8317
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Article ; Online: Repeatability of quantitative metrics derived from MR diffusion tractography in paediatric patients with epilepsy.

Paldino, M J / Hedges, K / Rodrigues, K M / Barboriak, D P

The British journal of radiology

2014 Volume 87, Issue 1037, Page(s) 20140095

Abstract: ... 993; p < 0.0001) and FA (r = 0.990; p < 0.0001). For each tract, coefficients of variation ranged ...

Abstract	Objective: To quantify the test-retest repeatability of mean diffusivity (MD) and fractional anisotropy (FA) derived from diffusion tensor imaging (DTI) tractography in a cohort of paediatric patients with localization-related epilepsy. Methods: 30 patients underwent 2 DTI acquisitions [repetition time/echo time (ms), 7000/90; flip, 90°; b-value, 1000 s mm(-2); voxel (mm), 2 × 2 × 2]. Two observers used Diffusion Toolkit and TrackVis ( www.trackvis.org ) to segment and analyse the following tracts: corpus callosum, corticospinal tracts, arcuate fasciculi, inferior longitudinal fasciculi and inferior fronto-occipital fasciculi. Mean MD and mean FA were calculated for each tract. Each observer independently analysed one of the DTI data sets for every patient. Results: Segmentation identified all tracts in all subjects, except the arcuate fasciculus. There was a highly consistent relationship between repeated observations of MD (r = 0.993; p < 0.0001) and FA (r = 0.990; p < 0.0001). For each tract, coefficients of variation ranged from 0.9% to 2.1% for MD and from 1.5% to 2.8% for FA. The 95% confidence limits (CLs) for change ranged from 2.8% to 6% for MD and from 4.3% to 8.6% for FA. For the arcuate fasciculus, Cohen's κ for agreement between the observers (identifiable vs not identifiable) was 1.0. Conclusion: We quantified the repeatability of two commonly utilized scalar metrics derived from DTI tractography. For an individual patient, changes greater than the repeatability coefficient or 95% CLs for change are unlikely to be related to variability in their measurement. Advances in knowledge: Reproducibility of these metrics will aid in the design of future studies and might one day be used to guide management in patients with epilepsy.
MeSH term(s)	Anisotropy ; Child ; Diffusion Tensor Imaging/methods ; Epilepsy/pathology ; Female ; Humans ; Image Interpretation, Computer-Assisted ; Male ; Reproducibility of Results ; Retrospective Studies
Language	English
Publishing date	2014-02-24
Publishing country	England
Document type	Journal Article
ZDB-ID	2982-8
ISSN	1748-880X ; 0007-1285
ISSN (online)	1748-880X
ISSN	0007-1285
DOI	10.1259/bjr.20140095
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Article ; Online: Ruptured maxillary retention cyst: cause of unilateral rhinorrhea after trauma.

Hoang, J K / Smith, E C / Barboriak, D P

AJNR. American journal of neuroradiology

2009 Volume 30, Issue 6, Page(s) 1121–1122

Abstract: This study describes a case of a patient with traumatic rupture of a maxillary sinus retention cyst, which had an interesting clinical presentation of unilateral rhinorrhea, mimicking a CSF leak. The diagnosis was made fortuitously by comparison of a ... ...

Abstract	This study describes a case of a patient with traumatic rupture of a maxillary sinus retention cyst, which had an interesting clinical presentation of unilateral rhinorrhea, mimicking a CSF leak. The diagnosis was made fortuitously by comparison of a posttraumatic CT brain examination with a CT sinus study performed 1 day earlier.
MeSH term(s)	Cerebrospinal Fluid Rhinorrhea/diagnostic imaging ; Cerebrospinal Fluid Rhinorrhea/etiology ; Craniocerebral Trauma/complications ; Craniocerebral Trauma/diagnostic imaging ; Cysts/diagnostic imaging ; Cysts/etiology ; Female ; Humans ; Maxillary Diseases/diagnostic imaging ; Maxillary Diseases/etiology ; Radiography ; Rupture/complications ; Rupture/diagnostic imaging ; Young Adult
Language	English
Publishing date	2009-05-13
Publishing country	United States
Document type	Case Reports ; Journal Article
ZDB-ID	603808-6
ISSN	1936-959X ; 0195-6108
ISSN (online)	1936-959X
ISSN	0195-6108
DOI	10.3174/ajnr.A1457
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Article ; Online: Response to Letter to Editor.

Neuro-oncology

2020 Volume 22, Issue 11, Page(s) 1706–1707

MeSH term(s)	Brain Neoplasms ; Consensus ; Diagnostic Imaging ; Humans
Language	English
Publishing date	2020-08-21
Publishing country	England
Document type	Letter ; Comment
ZDB-ID	2028601-6
ISSN	1523-5866 ; 1522-8517
ISSN (online)	1523-5866
ISSN	1522-8517
DOI	10.1093/neuonc/noaa202
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Article ; Online: Consensus recommendations for a standardized brain tumor imaging protocol for clinical trials in brain metastases.

Neuro-oncology

2020 Volume 22, Issue 6, Page(s) 757–772

Abstract: A recent meeting was held on March 22, 2019, among the FDA, clinical scientists, pharmaceutical and biotech companies, clinical trials cooperative groups, and patient advocacy groups to discuss challenges and potential solutions for increasing ... ...

Abstract	A recent meeting was held on March 22, 2019, among the FDA, clinical scientists, pharmaceutical and biotech companies, clinical trials cooperative groups, and patient advocacy groups to discuss challenges and potential solutions for increasing development of therapeutics for central nervous system metastases. A key issue identified at this meeting was the need for consistent tumor measurement for reliable tumor response assessment, including the first step of standardized image acquisition with an MRI protocol that could be implemented in multicenter studies aimed at testing new therapeutics. This document builds upon previous consensus recommendations for a standardized brain tumor imaging protocol (BTIP) in high-grade gliomas and defines a protocol for brain metastases (BTIP-BM) that addresses unique challenges associated with assessment of CNS metastases. The "minimum standard" recommended pulse sequences include: (i) parameter matched pre- and post-contrast inversion recovery (IR)-prepared, isotropic 3D T1-weighted gradient echo (IR-GRE); (ii) axial 2D T2-weighted turbo spin echo acquired after injection of gadolinium-based contrast agent and before post-contrast 3D T1-weighted images; (iii) axial 2D or 3D T2-weighted fluid attenuated inversion recovery; (iv) axial 2D, 3-directional diffusion-weighted images; and (v) post-contrast 2D T1-weighted spin echo images for increased lesion conspicuity. Recommended sequence parameters are provided for both 1.5T and 3T MR systems. An "ideal" protocol is also provided, which replaces IR-GRE with 3D TSE T1-weighted imaging pre- and post-gadolinium, and is best performed at 3T, for which dynamic susceptibility contrast perfusion is included. Recommended perfusion parameters are given.
MeSH term(s)	Brain Neoplasms/diagnostic imaging ; Consensus ; Contrast Media ; Gadolinium ; Humans ; Magnetic Resonance Imaging
Chemical Substances	Contrast Media ; Gadolinium (AU0V1LM3JT)
Language	English
Publishing date	2020-01-20
Publishing country	England
Document type	Journal Article
ZDB-ID	2028601-6
ISSN	1523-5866 ; 1522-8517
ISSN (online)	1523-5866
ISSN	1522-8517
DOI	10.1093/neuonc/noaa030
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Article ; Online: A change in the apparent diffusion coefficient after treatment with bevacizumab is associated with decreased survival in patients with recurrent glioblastoma multiforme.

Paldino, M J / Desjardins, A / Friedman, H S / Vredenburgh, J J / Barboriak, D P

The British journal of radiology

2011 Volume 85, Issue 1012, Page(s) 382–389

Abstract: ... shorter overall survival (p=0.032) and progression free survival (p=0.046) than those with no change ...

Abstract	Objectives: The aim of this study was to determine the prognostic significance of changes in parameters derived from diffusion tensor imaging (DTI) that occur in response to treatment with bevacizumab and irinotecan in patients with recurrent glioblastoma multiforme. Methods: 15 patients with recurrent glioblastoma multiforme underwent serial 1.5 T MRI. Axial single-shot echo planar DTI was obtained on scans performed 3 days and 1 day prior to and 6 weeks after initiation of therapy with bevacizumab and irinotecan. Apparent diffusion coefficient (ADC) and fractional anisotropy (FA) maps were registered to whole brain contrast-enhanced three-dimensional (3D) spoiled gradient recalled and 3D fluid attenuation inversion recovery (FLAIR) image volumes. Anatomic image volumes were segmented to isolate regions of interest defined by tumour-related enhancement (TRE) and FLAIR signal abnormality (FSA). Mean ADC and mean FA were calculated for each region. A Bland-Altman repeatability coefficient was also calculated for each parameter based on the two pre-treatment studies. A patient was considered to have a change in FA or ADC after therapy if the difference between the pre- and post-treatment values was greater than the repeatability coefficient for that parameter. Survival was compared using a Cox proportional hazard model. Results: DTI detected a change in ADC within FSA after therapy in nine patients (five in whom ADC was increased; four in whom it was decreased). Patients with a change in ADC within FSA had significantly shorter overall survival (p=0.032) and progression free survival (p=0.046) than those with no change. Conclusion: In patients with recurrent glioblastoma multiforme treated with bevacizumab and irinotecan, a change in ADC after therapy in FSA is associated with decreased survival.
MeSH term(s)	Adult ; Angiogenesis Inhibitors/administration & dosage ; Angiogenesis Inhibitors/therapeutic use ; Anisotropy ; Antibodies, Monoclonal, Humanized/administration & dosage ; Antibodies, Monoclonal, Humanized/therapeutic use ; Antineoplastic Agents, Phytogenic/administration & dosage ; Antineoplastic Agents, Phytogenic/therapeutic use ; Antineoplastic Combined Chemotherapy Protocols/therapeutic use ; Bevacizumab ; Camptothecin/administration & dosage ; Camptothecin/analogs & derivatives ; Camptothecin/therapeutic use ; Diffusion Tensor Imaging ; Female ; Glioblastoma/diagnosis ; Glioblastoma/drug therapy ; Glioblastoma/mortality ; Humans ; Irinotecan ; Kaplan-Meier Estimate ; Male ; Middle Aged ; Neoplasm Recurrence, Local ; Prognosis
Chemical Substances	Angiogenesis Inhibitors ; Antibodies, Monoclonal, Humanized ; Antineoplastic Agents, Phytogenic ; Bevacizumab (2S9ZZM9Q9V) ; Irinotecan (7673326042) ; Camptothecin (XT3Z54Z28A)
Language	English
Publishing date	2011-01-11
Publishing country	England
Document type	Journal Article
ZDB-ID	2982-8
ISSN	1748-880X ; 0007-1285
ISSN (online)	1748-880X
ISSN	0007-1285
DOI	10.1259/bjr/24774491
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