ArticleNeuro-oncology2019
Automatic assessment of glioma burden: a deep learning algorithm for fully automated volumetric and bidimensional measurement.
Article in Neuro-oncology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 106 papers, 3 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
106 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Automated longitudinal treatment response assessment of brain tumors: A systematic review.Neuro-oncology · 2025Pooled it
- Deep learning in neurosurgery: a systematic literature review with a structured analysis of applications across subspecialties.Frontiers in neurology · 2025Pooled it
- Artificial Intelligence Technologies in Neurosurgery: a Systematic Literature Review Using Topic Modeling. Part II: Research Objectives and Perspectives.Sovremennye tekhnologii v meditsine · 2021Pooled it
- Expert-centered Evaluation of Deep Learning Algorithms for Brain Tumor Segmentation.Radiology. Artificial intelligence · 2024Trial
- Radiomics-Based Machine Learning for Outcome Prediction in a Multicenter Phase II Study of Programmed Death-Ligand 1 Inhibition Immunotherapy for Glioblastoma.AJNR. American journal of neuroradiology · 2022Trial
- Value of Rehabilitation Training for Children with Cerebral Palsy Diagnosed and Analyzed by Computed Tomography Imaging Information Features under Deep Learning.Journal of healthcare engineering · 2021Trial
- Automatic estimation of single-tooth width from standardized two-dimensional occlusal photographs using deep learning.Head & face medicine · 2026Article
- [European journal of nuclear medicine and molecular imaging · 2026Article
- Challenges of Applying the RANO-BM Criteria for Characterization of Brain Metastases Treatment Response.Current oncology (Toronto, Ont.) · 2026Review
- Editorial: Intraoperative Visualization Techniques and Advanced Imaging in Brain Tumors.Cancers · 2026Article
- The University of California San Diego Post-Treatment Glioblastoma (UCSD-PTGBM) annotated multimodal MRI Dataset.Scientific data · 2026Article
- Personalised medicine through AI-enhanced integration of diagnostic imaging and radiation therapy.European radiology experimental · 2025Review
- Advances in Artificial Intelligence for Glioblastoma Radiotherapy Planning and Treatment.Cancers · 2025Review
- Artificial Intelligence in the Diagnosis and Treatment of Brain Gliomas.Biomedicines · 2025Review
- Establishment and evaluation of an automatic multi?sequence MRI segmentation model of primary central nervous system lymphoma based on the nnU?Net deep learning network method.Oncology letters · 2025Article
- Development and Evaluation of Automated Artificial Intelligence-Based Brain Tumor Response Assessment in Patients with Glioblastoma.AJNR. American journal of neuroradiology · 2025Article
- A review of deep learning for brain tumor analysis in MRI.NPJ precision oncology · 2025Review
- Enhancing neuro-oncology care through equity-driven applications of artificial intelligence.Neuro-oncology · 2024Review
- Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 1: review of current advancements.The Lancet. Oncology · 2024Review
- Dilated multi-scale residual attention (DMRA) U-Net: three-dimensional (3D) dilated multi-scale residual attention U-Net for brain tumor segmentation.Quantitative imaging in medicine and surgery · 2024Article
46 more citing papers are in PubMed but not listed here.
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28 authors.
Funding
Abstract
backgroundLongitudinal measurement of glioma burden with MRI is the basis for treatment response assessment. In this study, we developed a deep learning algorithm that automatically segments abnormal fluid attenuated inversion recovery (FLAIR) hyperintensity and contrast-enhancing tumor, quantitating tumor volumes as well as the product of maximum bidimensional diameters according to the Response Assessment in Neuro-Oncology (RANO) criteria (AutoRANO).
methodsTwo cohorts of patients were used for this study. One consisted of 843 preoperative MRIs from 843 patients with low- or high-grade gliomas from 4 institutions and the second consisted of 713 longitudinal postoperative MRI visits from 54 patients with newly diagnosed glioblastomas (each with 2 pretreatment "baseline" MRIs) from 1 institution.
resultsThe automatically generated FLAIR hyperintensity volume, contrast-enhancing tumor volume, and AutoRANO were highly repeatable for the double-baseline visits, with an intraclass correlation coefficient (ICC) of 0.986, 0.991, and 0.977, respectively, on the cohort of postoperative GBM patients. Furthermore, there was high agreement between manually and automatically measured tumor volumes, with ICC values of 0.915, 0.924, and 0.965 for preoperative FLAIR hyperintensity, postoperative FLAIR hyperintensity, and postoperative contrast-enhancing tumor volumes, respectively. Lastly, the ICCs for comparing manually and automatically derived longitudinal changes in tumor burden were 0.917, 0.966, and 0.850 for FLAIR hyperintensity volume, contrast-enhancing tumor volume, and RANO measures, respectively.
conclusionsOur automated algorithm demonstrates potential utility for evaluating tumor burden in complex posttreatment settings, although further validation in multicenter clinical trials will be needed prior to widespread implementation.
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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.