ArticleNeurosurgery2016
Imaging Surrogates of Infiltration Obtained Via Multiparametric Imaging Pattern Analysis Predict Subsequent Location of Recurrence of Glioblastoma.
Article in Neurosurgery, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 102 papers, 3 of them syntheses that pooled it.
What it found
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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
102 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- The use of artificial intelligence and machine learning to predict tumor recurrence in high-grade gliomas: a systematic review.Neurosurgical review · 2026Pooled it
- Reproducibility analysis of multi-institutional paired expert annotations and radiomic features of the Ivy Glioblastoma Atlas Project (Ivy GAP) dataset.Medical physics · 2020Pooled it
- Machine learning applications to clinical decision support in neurosurgery: an artificial intelligence augmented systematic review.Neurosurgical review · 2020Pooled it
- Personalized machine learning-guided radiation dose escalation in newly diagnosed glioblastoma: prospective pilot study.Nature communications · 2026Trial
- Imaging-Based Algorithm for the Local Grading of Glioma.AJNR. American journal of neuroradiology · 2020Trial
- Arterial Spin Labeling and Dynamic Susceptibility Contrast-enhanced MR Imaging for evaluation of arteriovenous shunting and tumor hypoxia in glioblastoma.Scientific reports · 2019Trial
- Guiding the first biopsy in glioma patients using estimated Ki-67 maps derived from MRI: conventional versus advanced imaging.Neuro-oncology · 2019Trial
- Optical microscopy predictions of focal recurrence in glioblastoma.Science advances · 2026Article
- Integrating Radiogenomics and CSF-Based Liquid Biopsy Sequencing for Precision Neuro-Oncology.International journal of molecular sciences · 2026Review
- Review
- Radiomic analysis of the peritumoral zone identifies imaging signatures of glioma invasion associated with HSP70 expression.Frontiers in oncology · 2026Article
- Radiomics-based quantification of tumor infiltration in the non-enhancing peritumoral region on postoperative MRI is associated with survival in glioblastoma.Scientific reports · 2025Article
- Artificial intelligence in neuro-oncology: methodological bases, practical applications and ethical and regulatory issues.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Review
- Radiomics and Radiogenomics in Differentiating Progression, Pseudoprogression, and Radiation Necrosis in Gliomas.Biomedicines · 2025Review
- Exploring the associations between features from multi-parametric MR images in Glioblastoma using radiomics.BMC medical imaging · 2025Article
- Article
- Editorial: Spatiotemporal & AI trends in neuroscience, neuroimaging, and neurooncology.Frontiers in neuroimaging · 2025Article
- Individual Brain Tumor Invasion Mapping Based on Diffusion Kurtosis Imaging.Sovremennye tekhnologii v meditsine · 2025Article
- Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 2: recommendations for standardisation, validation, and good clinical practice.The Lancet. Oncology · 2024Review
- Intraoperative glioblastoma surgery-current challenges and clinical trials: An update.Cancer pathogenesis and therapy · 2024Review
42 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
backgroundGlioblastoma is an aggressive and highly infiltrative brain cancer. Standard surgical resection is guided by enhancement on postcontrast T1-weighted (T1) magnetic resonance imaging, which is insufficient for delineating surrounding infiltrating tumor.
objectiveTo develop imaging biomarkers that delineate areas of tumor infiltration and predict early recurrence in peritumoral tissue. Such markers would enable intensive, yet targeted, surgery and radiotherapy, thereby potentially delaying recurrence and prolonging survival.
methodsPreoperative multiparametric magnetic resonance images (T1, T1-gadolinium, T2-weighted, T2-weighted fluid-attenuated inversion recovery, diffusion tensor imaging, and dynamic susceptibility contrast-enhanced magnetic resonance images) from 31 patients were combined using machine learning methods, thereby creating predictive spatial maps of infiltrated peritumoral tissue. Cross-validation was used in the retrospective cohort to achieve generalizable biomarkers. Subsequently, the imaging signatures learned from the retrospective study were used in a replication cohort of 34 new patients. Spatial maps representing the likelihood of tumor infiltration and future early recurrence were compared with regions of recurrence on postresection follow-up studies with pathology confirmation.
resultsThis technique produced predictions of early recurrence with a mean area under the curve of 0.84, sensitivity of 91%, specificity of 93%, and odds ratio estimates of 9.29 (99% confidence interval: 8.95-9.65) for tissue predicted to be heavily infiltrated in the replication study. Regions of tumor recurrence were found to have subtle, yet fairly distinctive multiparametric imaging signatures when analyzed quantitatively by pattern analysis and machine learning.
conclusionVisually imperceptible imaging patterns discovered via multiparametric pattern analysis methods were found to estimate the extent of infiltration and location of future tumor recurrence, paving the way for improved targeted treatment.
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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.