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Sex differences in GBM revealed by analysis of patient imaging, transcriptome, and survival data

Science translational medicine, 2019-01, Vol.11 (473)

2019

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Integration of machine learning and mechanistic models accurately predicts variation in cell density of glioblastoma using multiparametric MRI

Scientific reports, 2019-07, Vol.9 (1), p.10063-9, Article 10063

2019

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Image-based metric of invasiveness predicts response to adjuvant temozolomide for primary glioblastoma

PloS one, 2020-03, Vol.15 (3), p.e0230492

2020

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Uncertainty quantification in the radiogenomics modeling of EGFR amplification in glioblastoma

Scientific reports, 2021-02, Vol.11 (1), p.3932-3932, Article 3932

2021

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Project QUIT (Quit Using Drugs Intervention Trial): a randomized controlled trial of a primary care-based multi-component brief intervention to reduce risky drug use

Addiction (Abingdon, England), 2015-11, Vol.110 (11), p.1777-1790

2015

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A Deep Convolutional Neural Network for Annotation of Magnetic Resonance Imaging Sequence Type

Journal of digital imaging, 2020-04, Vol.33 (2), p.439-446

2020

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A pilot replication of QUIT, a randomized controlled trial of a brief intervention for reducing risky drug use, among Latino primary care patients

Drug and alcohol dependence, 2017-10, Vol.179, p.433-440

2017

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Weakly Supervised Skull Stripping of Magnetic Resonance Imaging of Brain Tumor Patients

Frontiers in neuroimaging, 2022-04, Vol.1, p.832512

2022

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Transfer and transport: incorporating causal methods for improving predictive models

Journal of the American Medical Informatics Association : JAMIA, 2014-10, Vol.21 (e2), p.e374-e375

2014

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Deep neural network to locate and segment brain tumors outperformed the expert technicians who created the training data

Journal of medical imaging (Bellingham, Wash.), 2020-09, Vol.7 (5), p.055501-055501

2020

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Days gained response discriminates treatment response in patients with recurrent glioblastoma receiving bevacizumab-based therapies

Neuro-oncology advances, 2020-01, Vol.2 (1), p.vdaa085-vdaa085

2020

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NIMG-86. ESTIMATING BRAIN TUMOR RELATED EDEMA FROM NON-INVASIVE IMAGING IN A CLINICALLY RELEVANT TIMELINE

Neuro-oncology (Charlottesville, Va.), 2023-11, Vol.25 (Supplement_5), p.v205-v206

2023

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Motivating the additional use of external validity: examining transportability in a model of glioblastoma multiforme

AMIA ... Annual Symposium proceedings, 2014, Vol.2014, p.1930-1939

2014

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Comparing predictive models of glioblastoma multiforme built using multi-institutional and local data sources

AMIA ... Annual Symposium proceedings, 2012, Vol.2012, p.1385-1392

2012

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NIMG-23. DEEP LEARNING FOR ACCURATE, RAPID, FULLY AUTOMATIC MEASUREMENT OF BRAIN TUMOR-ASSOCIATED ABNORMALITY SEEN ON MRI

Neuro-oncology (Charlottesville, Va.), 2018-11, Vol.20 (suppl_6), p.vi180-vi181

2018

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Wireless data collection of self-administered surveys using tablet computers

AMIA ... Annual Symposium proceedings, 2011, Vol.2011, p.1261-1269

2011

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NIMG-06. KINETICS-BASED RESPONSE METRIC DISCRIMINATE IMPROVED OUTCOMES FOR PATIENTS RECEIVING BEVACIZUMAB-BASED THERAPIES

Neuro-oncology (Charlottesville, Va.), 2018-11, Vol.20 (suppl_6), p.vi176-vi177

2018

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NIMG-40. MRI-BASED ESTIMATION OF THE ABUNDANCE OF IMMUNOHISTOCHEMISTRY MARKERS IN GBM BRAIN USING DEEP LEARNING

Neuro-oncology (Charlottesville, Va.), 2021-11, Vol.23 (Supplement_6), p.vi137-vi138

2021

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NIMG-85. EVALUATING THE PERFORMANCE OF A DEEP LEARNING GLIOMA SEGMENTATION MODEL USING COMBINED PRE- AND POST-TREATMENT MRI TRAINING DATA

Neuro-oncology (Charlottesville, Va.), 2023-11, Vol.25 (Supplement_5), p.v205-v205

2023

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NIMG-72. QUANTIFYING INTRA-TUMOR MULTI-GENE HETEROGENEITY OF GBM FROM MRI USING A DATA-INCLUSIVE MACHINE LEARNING ALGORITHM

Neuro-oncology (Charlottesville, Va.), 2021-11, Vol.23 (Supplement_6), p.vi145-vi146

2021

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EPCO-12. SEX-SPECIFIC PATTERNS CONNECTING LOCOREGIONAL MRI FEATURES AND IMMUNOHISTOCHEMISTRY OF IMAGE-LOCALIZED BIOPSIES OF GLIOBLASTOMA

Neuro-oncology (Charlottesville, Va.), 2022-11, Vol.24 (Supplement_7), p.vii118-vii118

2022

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NIMG-19. IMAGE-BASED MODELING MAP OF EDEMA IS CORRELATED WITH MULTIPLE BLOOD-BRAIN-BARRIER PERMEABILITY RELEVANT TRANSCRIPTOMIC MARKERS IN BRAIN TUMOR PATIENTS

Neuro-oncology (Charlottesville, Va.), 2022-11, Vol.24 (Supplement_7), p.vii165-vii165

2022

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BIOM-44. PRE-SURGICAL ADVANCED MRI IS USEFUL FOR FORECASTING DRUG DISTRIBUTION IN BRAIN TUMORS

Neuro-oncology (Charlottesville, Va.), 2021-11, Vol.23 (Supplement_6), p.vi20-vi21

2021

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Sex differences in GBM revealed by analysis of patient imaging, transcriptome, and survival data

Integration of machine learning and mechanistic models accurately predicts variation in cell density of glioblastoma using multiparametric MRI

Image-based metric of invasiveness predicts response to adjuvant temozolomide for primary glioblastoma

Quantifying intra-tumoral genetic heterogeneity of glioblastoma toward precision medicine using MRI and a data-inclusive machine learning algorithm

Uncertainty quantification in the radiogenomics modeling of EGFR amplification in glioblastoma

Project QUIT (Quit Using Drugs Intervention Trial): a randomized controlled trial of a primary care-based multi-component brief intervention to reduce risky drug use

Image-localized biopsy mapping of brain tumor heterogeneity: A single-center study protocol

A Deep Convolutional Neural Network for Annotation of Magnetic Resonance Imaging Sequence Type

A pilot replication of QUIT, a randomized controlled trial of a brief intervention for reducing risky drug use, among Latino primary care patients

Weakly Supervised Skull Stripping of Magnetic Resonance Imaging of Brain Tumor Patients

Transfer and transport: incorporating causal methods for improving predictive models

Deep neural network to locate and segment brain tumors outperformed the expert technicians who created the training data

Days gained response discriminates treatment response in patients with recurrent glioblastoma receiving bevacizumab-based therapies

NIMG-86. ESTIMATING BRAIN TUMOR RELATED EDEMA FROM NON-INVASIVE IMAGING IN A CLINICALLY RELEVANT TIMELINE

Motivating the additional use of external validity: examining transportability in a model of glioblastoma multiforme

Comparing predictive models of glioblastoma multiforme built using multi-institutional and local data sources

NIMG-23. DEEP LEARNING FOR ACCURATE, RAPID, FULLY AUTOMATIC MEASUREMENT OF BRAIN TUMOR-ASSOCIATED ABNORMALITY SEEN ON MRI

Wireless data collection of self-administered surveys using tablet computers

NIMG-06. KINETICS-BASED RESPONSE METRIC DISCRIMINATE IMPROVED OUTCOMES FOR PATIENTS RECEIVING BEVACIZUMAB-BASED THERAPIES

NIMG-40. MRI-BASED ESTIMATION OF THE ABUNDANCE OF IMMUNOHISTOCHEMISTRY MARKERS IN GBM BRAIN USING DEEP LEARNING

NIMG-85. EVALUATING THE PERFORMANCE OF A DEEP LEARNING GLIOMA SEGMENTATION MODEL USING COMBINED PRE- AND POST-TREATMENT MRI TRAINING DATA

NIMG-72. QUANTIFYING INTRA-TUMOR MULTI-GENE HETEROGENEITY OF GBM FROM MRI USING A DATA-INCLUSIVE MACHINE LEARNING ALGORITHM

EPCO-12. SEX-SPECIFIC PATTERNS CONNECTING LOCOREGIONAL MRI FEATURES AND IMMUNOHISTOCHEMISTRY OF IMAGE-LOCALIZED BIOPSIES OF GLIOBLASTOMA

NIMG-19. IMAGE-BASED MODELING MAP OF EDEMA IS CORRELATED WITH MULTIPLE BLOOD-BRAIN-BARRIER PERMEABILITY RELEVANT TRANSCRIPTOMIC MARKERS IN BRAIN TUMOR PATIENTS

BIOM-44. PRE-SURGICAL ADVANCED MRI IS USEFUL FOR FORECASTING DRUG DISTRIBUTION IN BRAIN TUMORS

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