Trial reportAJNR. American journal of neuroradiology2022
Radiomics-Based Machine Learning for Outcome Prediction in a Multicenter Phase II Study of Programmed Death-Ligand 1 Inhibition Immunotherapy for Glioblastoma.
Trial report in AJNR. American journal of neuroradiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 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
20 citing papers in PubMed, 30 citations in OpenAlex.
- Glioblastoma radiomics can delineate systemic immune activity states like blood abundance of T cell populations or transcription factors.Journal of neuroinflammation · 2026Article
- Artificial intelligence for optimization of immunotherapy: current applications and transformative potential.Frontiers in immunology · 2026Review
- Current strategies and novel immunotherapeutic approaches for overcoming immune resistance in glioblastoma.Discover oncology · 2025Review
- Review
- Artificial intelligence in precision medicine: transforming disease subtyping, medical imaging, and pharmacogenomics.Emerging topics in life sciences · 2025Article
- A scoping review of artificial intelligence applications in clinical trial risk assessment.NPJ digital medicine · 2025Article
- Hybrid Deep Learning for Survival Prediction in Brain Metastases Using Multimodal MRI and Clinical Data.Diagnostics (Basel, Switzerland) · 2025Article
- Radiomics in glioma: emerging trends and challenges.Annals of clinical and translational neurology · 2025Review
- Immune checkpoint inhibitors for glioblastoma: emerging science, clinical advances, and future directions.Journal of neuro-oncology · 2025Review
- Comparative analysis of deep learning and radiomic signatures for overall survival prediction in recurrent high-grade glioma treated with immunotherapy.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Recent advances in immunotherapy for gliomas: overcoming barriers and advancing precision strategies.Frontiers in immunology · 2025Review
- Biomarkers of immunotherapy in glioblastoma.Neuro-oncology practice · 2024Review
- Imaging Genomics of Glioma Revisited: Analytic Methods to Understand Spatial and Temporal Heterogeneity.AJNR. American journal of neuroradiology · 2024Review
- Combined Immunotherapy Improves Outcome for Replication-Repair-Deficient (RRD) High-Grade Glioma Failing Anti-PD-1 Monotherapy: A Report from the International RRD Consortium.Cancer discovery · 2024Article
- MRI-derived radiomics assessing tumor-infiltrating macrophages enable prediction of immune-phenotype, immunotherapy response and survival in glioma.Biomarker research · 2024Article
- Advances in Neuro-Oncological Imaging: An Update on Diagnostic Approach to Brain Tumors.Cancers · 2024Review
- Radiomics for characterization of the glioma immune microenvironment.NPJ precision oncology · 2023Review
- ICI efficacy information portal: a knowledgebase for responder prediction to immune checkpoint inhibitors.NAR cancer · 2023Article
- Clinical Effects of Immuno-Oncology Therapy on Glioblastoma Patients: A Systematic Review.Brain sciences · 2023Review
- Radiogenomic biomarkers for immunotherapy in glioblastoma: A systematic review of magnetic resonance imaging studies.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
8 authors at 6 institutions in 2 countries.
Funding
Abstract
background and purposeImaging assessment of an immunotherapy response in glioblastoma is challenging due to overlap in the appearance of treatment-related changes with tumor progression. Our purpose was to determine whether MR imaging radiomics-based machine learning can predict progression-free survival and overall survival in patients with glioblastoma on programmed death-ligand 1 inhibition immunotherapy. MATERIALS AND
methodsPost hoc analysis was performed of a multicenter trial on the efficacy of durvalumab in glioblastoma (
resultsThe mean age was 55.2 (SD, 11.5) years, and 69% of patients were male. Pretreatment MR imaging features had a poor predictive value for overall survival and progression-free survival (concordance index = 0.472-0.524). First on-treatment MR imaging features had high predictive value for overall survival (concordance index = 0.692-0.750) and progression-free survival (concordance index = 0.680-0.715).
conclusionsA radiomics-based machine learning model from first on-treatment MR imaging predicts survival in patients with glioblastoma on programmed death-ligand 1 inhibition immunotherapy.
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.