ArticleAdvances in radiation oncology
Radiomic Analysis to Predict Histopathologically Confirmed Pseudoprogression in Glioblastoma Patients.
Article in Advances in radiation oncology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
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
16 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Radiomics for differentiating radiation-induced brain injury from recurrence in gliomas: systematic review, meta-analysis, and methodological quality evaluation using METRICS and RQS.European radiology · 2025Pooled it
- AI-driven estimation of O6 methylguanine-DNA-methyltransferase (MGMT) promoter methylation in glioblastoma patients: a systematic review with bias analysis.Journal of cancer research and clinical oncology · 2024Pooled it
- Fluorescein-stained confocal laser endomicroscopy versus conventional frozen section for intraoperative histopathological assessment of intracranial tumors.Neuro-oncology · 2024Trial
- Harnessing Machine Learning for Personalized Care of Patients With Idiopathic Sudden Sensorineural Hearing Loss: A Multicenter Cohort Study.Clinical and experimental otorhinolaryngology · 2026Article
- Integrating Genomics, Radiomics, and Pathomics in Oncology: A Scoping Review and a Framework for AI-Enabled Surgomics.Bioengineering (Basel, Switzerland) · 2026Review
- AI-driven radiomics and radiogenomics: supporting the assessment and differentiation of pseudoprogression in cellular immunotherapy for glioblastoma.Frontiers in immunology · 2026Review
- Timepoint-Specific Benchmarking of Deep Learning Models for Glioblastoma Follow-Up MRI.Cancers · 2025Article
- Radiomics and Radiogenomics in Differentiating Progression, Pseudoprogression, and Radiation Necrosis in Gliomas.Biomedicines · 2025Review
- Mathematical modeling for glioblastoma treatment: scenario generation and validation for clinical patient counseling.Frontiers in oncology · 2025Article
- Biologically informed deep neural networks provide quantitative assessment of intratumoral heterogeneity in post treatment glioblastoma.NPJ digital medicine · 2024Article
- Identification of new hit to lead magmas inhibitors as potential therapeutics for glioblastoma.Bioorganic & medicinal chemistry letters · 2023Article
- The relationship between radiomics and pathomics in Glioblastoma patients: Preliminary results from a cross-scale association study.Frontiers in oncology · 2022Article
- Distinction of pseudoprogression from true progression in glioblastomas using machine learning based on multiparametric magnetic resonance imaging and ONeuro-oncology advancesArticle
- Postsurgical detection of glioma recurrence using MRI radiomics.Neuro-oncology advancesArticle
- Article
- miRNA liquid biopsy combined with MRI radiomics for improved outcome prediction in glioblastoma: integrated machine learning analysis of longitudinal data from 73 patients.Neuro-oncology advancesArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
Abstract
Purpose: Pseudoprogression mimicking recurrent glioblastoma remains a diagnostic challenge that may adversely confound or delay appropriate treatment or clinical trial enrollment. We sought to build a radiomic classifier to predict pseudoprogression in patients with primary isocitrate dehydrogenase wild type glioblastoma. Methods and Materials: We retrospectively examined a training cohort of 74 patients with isocitrate dehydrogenase wild type glioblastomas with brain magnetic resonance imaging including dynamic contrast enhanced T1 perfusion before resection of an enhancing lesion indeterminate for recurrent tumor or pseudoprogression. A recursive feature elimination random forest classifier was built using nested cross-validation without and with O Results: A classifier constructed with cross-validation on the training cohort achieved an area under the receiver operating curve of 81% for predicting pseudoprogression. This was further improved to 89% with the addition of O Conclusions: Our results suggest that radiomic analysis of contrast T1-weighted images and magnetic resonance imaging perfusion images can assist the prompt diagnosis of pseudoprogression. Validation on external and independent data sets is necessary to verify these advanced analyses, which can be performed on routinely acquired clinical images and may help inform clinical treatment decisions.
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.