ArticleScientific reports2019
Integration of machine learning and mechanistic models accurately predicts variation in cell density of glioblastoma using multiparametric MRI.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed, 87 citations in OpenAlex.
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- Liquid crystal-like self-organization of glioblastoma is associated with consistent migration for high cell densities.Scientific reports · 2026Article
- Generative AI for spatial tumor growth on MRI: a proof-of-principle study in pediatric diffuse midline glioma.BMC medicine · 2026Article
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- 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
- Mechanistic in silico explorations of the immunogenic and synergistic effects of radiotherapy and immunotherapy: a critical review.Physical and engineering sciences in medicine · 2024Review
- Revisiting the role of computational neuroimaging in the era of integrative neuroscience.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2024Review
- Biologically informed deep neural networks provide quantitative assessment of intratumoral heterogeneity in post treatment glioblastoma.NPJ digital medicine · 2024Article
- Calibrating tumor growth and invasion parameters with spectral spatial analysis of cancer biopsy tissues.NPJ systems biology and applications · 2024Article
- 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
- Biologically-informed deep neural networks provide quantitative assessment of intratumoral heterogeneity in post-treatment glioblastoma.Research square · 2024Article
- Designing clinical trials for patients who are not average.iScience · 2024Review
- A Framework for Interpretability in Machine Learning for Medical Imaging.IEEE access : practical innovations, open solutions · 2024Article
- Article
- Treatment of evolving cancers will require dynamic decision support.Annals of oncology : official journal of the European Society for Medical Oncology · 2023Review
- Perineural invasion detection in pancreatic ductal adenocarcinoma using artificial intelligence.Scientific reports · 2023Article
- Assessment and prediction of glioblastoma therapy response: challenges and opportunities.Brain : a journal of neurology · 2023Article
- Image-localized biopsy mapping of brain tumor heterogeneity: A single-center study protocol.PloS one · 2023Article
- An image-based modeling framework for predicting spatiotemporal brain cancer biology within individual patients.Frontiers in oncology · 2023Article
Corrections and comments
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Authors and funding
18 authors at 6 institutions in 1 country.
Funding
Abstract
Glioblastoma (GBM) is a heterogeneous and lethal brain cancer. These tumors are followed using magnetic resonance imaging (MRI), which is unable to precisely identify tumor cell invasion, impairing effective surgery and radiation planning. We present a novel hybrid model, based on multiparametric intensities, which combines machine learning (ML) with a mechanistic model of tumor growth to provide spatially resolved tumor cell density predictions. The ML component is an imaging data-driven graph-based semi-supervised learning model and we use the Proliferation-Invasion (PI) mechanistic tumor growth model. We thus refer to the hybrid model as the ML-PI model. The hybrid model was trained using 82 image-localized biopsies from 18 primary GBM patients with pre-operative MRI using a leave-one-patient-out cross validation framework. A Relief algorithm was developed to quantify relative contributions from the data sources. The ML-PI model statistically significantly outperformed (p < 0.001) both individual models, ML and PI, achieving a mean absolute predicted error (MAPE) of 0.106 ± 0.125 versus 0.199 ± 0.186 (ML) and 0.227 ± 0.215 (PI), respectively. Associated Pearson correlation coefficients for ML-PI, ML, and PI were 0.838, 0.518, and 0.437, respectively. The Relief algorithm showed the PI model had the greatest contribution to the result, emphasizing the importance of the hybrid model in achieving the high accuracy.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.