ArticleAJNR. American journal of neuroradiology2019
Accurate Patient-Specific Machine Learning Models of Glioblastoma Invasion Using Transfer Learning.
Article in AJNR. American journal of neuroradiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Differentiation between Nonenhancing Tumor in Glioblastoma and Vasogenic Edema Using Diffusion-Weighted and Dynamic Susceptibility Contrast MR Imaging.AJNR. American journal of neuroradiology · 2025Article
- Knowledge-Informed Machine Learning for Cancer Diagnosis and Prognosis: A Review.IEEE transactions on automation science and engineering : a publication of the IEEE Robotics and Automation Society · 2025Article
- Review
- Weakly-Supervised Transfer Learning with Application in Precision Medicine.IEEE transactions on automation science and engineering : a publication of the IEEE Robotics and Automation Society · 2024Article
- Assessment and prediction of glioblastoma therapy response: challenges and opportunities.Brain : a journal of neurology · 2023Article
- Applying machine learning classifiers to automate quality assessment of paediatric dynamic susceptibility contrast (DSC-) MRI data.The British journal of radiology · 2023Article
- Article
- Machine learning approach for obstructive sleep apnea screening using brain diffusion tensor imaging.Journal of sleep research · 2023Article
- Image-localized biopsy mapping of brain tumor heterogeneity: A single-center study protocol.PloS one · 2023Article
- Ellipsoid calculations versus manual tumor delineations for glioblastoma tumor volume evaluation.Scientific reports · 2022Article
- Applications of Artificial Intelligence Based on Medical Imaging in Glioma: Current State and Future Challenges.Frontiers in oncology · 2022Review
- Transfer Learning in Magnetic Resonance Brain Imaging: A Systematic Review.Journal of imaging · 2021Review
- Visualizing Glioma Infiltration by the Combination of Multimodality Imaging and Artificial Intelligence, a Systematic Review of the Literature.Diagnostics (Basel, Switzerland) · 2021Review
- Article
- Roadmap for the clinical integration of radiomics in neuro-oncology.Neuro-oncology · 2020Article
- Imaging of intratumoral heterogeneity in high-grade glioma.Cancer letters · 2020Review
- From cells to tissue: How cell scale heterogeneity impacts glioblastoma growth and treatment response.PLoS computational biology · 2020Article
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Authors and funding
31 authors.
Funding
Abstract
background and purposeMR imaging-based modeling of tumor cell density can substantially improve targeted treatment of glioblastoma. Unfortunately, interpatient variability limits the predictive ability of many modeling approaches. We present a transfer learning method that generates individualized patient models, grounded in the wealth of population data, while also detecting and adjusting for interpatient variabilities based on each patient's own histologic data. MATERIALS AND
methodsWe recruited patients with primary glioblastoma undergoing image-guided biopsies and preoperative imaging, including contrast-enhanced MR imaging, dynamic susceptibility contrast MR imaging, and diffusion tensor imaging. We calculated relative cerebral blood volume from DSC-MR imaging and mean diffusivity and fractional anisotropy from DTI. Following image coregistration, we assessed tumor cell density for each biopsy and identified corresponding localized MR imaging measurements. We then explored a range of univariate and multivariate predictive models of tumor cell density based on MR imaging measurements in a generalized one-model-fits-all approach. We then implemented both univariate and multivariate individualized transfer learning predictive models, which harness the available population-level data but allow individual variability in their predictions. Finally, we compared Pearson correlation coefficients and mean absolute error between the individualized transfer learning and generalized one-model-fits-all models.
resultsTumor cell density significantly correlated with relative CBV (
conclusionsTransfer learning significantly improves predictive modeling performance for quantifying tumor cell density in glioblastoma.
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