ArticleClinical cancer research : an official journal of the American Association for Cancer Research2020
Diffusion Histology Imaging Combining Diffusion Basis Spectrum Imaging (DBSI) and Machine Learning Improves Detection and Classification of Glioblastoma Pathology.
Article in Clinical cancer research : an official journal of the American Association for Cancer Research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 35 citations in OpenAlex.
- Impact of MRI modality selection on glioma sub-region segmentation using 3D U-Net and Attention U-Net: A comparative evaluation across ten MRI sequences.European journal of radiology open · 2026Article
- Clinically feasible liver tumour cell size measurement through histology-informed in vivo diffusion MRI.Communications medicine · 2025Article
- MRI-based Habitat Imaging for Noninvasive Prediction of High-Grade Prostate Cancer.Radiology. Imaging cancer · 2025Article
- Diffusion Basis Restricted Fraction as a Putative Magnetic Resonance Imaging Marker of Neuroinflammation: Histological Evidence, Diagnostic Accuracy, and Translational Potential.Life (Basel, Switzerland) · 2025Review
- An Artificial Intelligence Model Using Diffusion Basis Spectrum Imaging Metrics Accurately Predicts Clinically Significant Prostate Cancer.The Journal of urology · 2025Article
- Understanding tractography in edematous brain regions: challenges and solutions.Brain structure & function · 2025Review
- Multi-modal fusion and feature enhancement U-Net coupling with stem cell niches proximity estimation for voxel-wise GBM recurrence predictionPhysics in medicine and biology · 2024Article
- A subregion-based RadioFusionOmics model discriminates between grade 4 astrocytoma and glioblastoma on multisequence MRI.Journal of cancer research and clinical oncology · 2024Article
- Review
- Applications of Radiomics and Radiogenomics in High-Grade Gliomas in the Era of Precision Medicine.Cancers · 2021Article
- Evaluation of interim MRI changes during limited-field radiation therapy for glioblastoma and implications for treatment planning.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2021Article
- Diffusion histology imaging differentiates distinct pediatric brain tumor histology.Scientific reports · 2021Article
- The impact of edema and fiber crossing on diffusion MRI metrics assessed in an ex vivo nerve phantom: Multi-tensor model vs. diffusion orientation distribution function.NMR in biomedicine · 2021Article
- Diffusion basis spectrum imaging as an adjunct to conventional MRI leads to earlier diagnosis of high-grade glioma tumor progression versus treatment effect.Neuro-oncology advancesArticle
- Standard clinical approaches and emerging modalities for glioblastoma imaging.Neuro-oncology advancesReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
19 authors at 4 institutions in 2 countries.
Funding
Abstract
purposeGlioblastoma (GBM) is one of the deadliest cancers with no cure. While conventional MRI has been widely adopted to examine GBM clinically, accurate neuroimaging assessment of tumor histopathology for improved diagnosis, surgical planning, and treatment evaluation remains an unmet need in the clinical management of GBMs. EXPERIMENTAL
designWe employ a novel diffusion histology imaging (DHI) approach, combining diffusion basis spectrum imaging (DBSI) and machine learning, to detect, differentiate, and quantify areas of high cellularity, tumor necrosis, and tumor infiltration in GBM.
resultsGadolinium-enhanced T1-weighted or hyperintense fluid-attenuated inversion recovery failed to reflect the morphologic complexity underlying tumor in patients with GBM. Contrary to the conventional wisdom that apparent diffusion coefficient (ADC) negatively correlates with increased tumor cellularity, we demonstrate disagreement between ADC and histologically confirmed tumor cellularity in GBM specimens, whereas DBSI-derived restricted isotropic diffusion fraction positively correlated with tumor cellularity in the same specimens. By incorporating DBSI metrics as classifiers for a supervised machine learning algorithm, we accurately predicted high tumor cellularity, tumor necrosis, and tumor infiltration with 87.5%, 89.0%, and 93.4% accuracy, respectively.
conclusionsOur results suggest that DHI could serve as a favorable alternative to current neuroimaging techniques in guiding biopsy or surgery as well as monitoring therapeutic response in the treatment of GBM.
Indexed as
Identifiers
What OpenQuestion holds
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.