ArticleNeuro-oncology advances
NS-HGlio: A generalizable and repeatable HGG segmentation and volumetric measurement AI algorithm for the longitudinal MRI assessment to inform RANO in trials and clinics.
Article in Neuro-oncology advances. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Neoadjuvant PD1 plus CTLA-4 immune checkpoint blockade in surgically resectable recurrent glioblastoma: a randomized surgical window-of-opportunity trial.Nature communications · 2026Trial
- Automated imaging response evaluation system: Prototype software tool for standardized radiographic response assessment in glioblastoma clinical trials.Neuro-oncology practice · 2026Review
- Imaging patterns of necrosis after treatment with chemoradiation are prognostic for survival in glioblastoma.European radiology · 2026Article
- Diffusion histogram analysis predicts progression-free survival in contrast enhancing recurrent IDH mutant gliomas treated with bevacizumab.Journal of neuro-oncology · 2026Article
- Spatially identifying regions of tumor recurrence in patients with suspected recurrent glioma using physiologic MRI and machine learning.NPJ digital medicine · 2026Article
- A multifocal IDH-mutant glioma with differential response to IDH inhibition: Role of quantitative neuroimaging.Radiology case reports · 2026Article
- Decoding the Glioblastoma Microenvironment: AI-Driven Analysis of Cellular MRI Signatures for Targeted Therapy.Cellular and molecular neurobiology · 2026Review
- Electrochemical and optical biosensors for periodontitis detection.Biochemistry and biophysics reports · 2026Review
- Spatial tumour characteristics as an indirect marker of metabolic dysregulation: evaluation for non-invasive IDH-genotyping of glioma using hybrid [18 F]FET-PET/MRI.European journal of nuclear medicine and molecular imaging · 2026Article
- Review
- Diffusion and contrast-enhancement MRI phenotypes associated with immune checkpoint inhibitor exposure and with immune cell infiltration in brain metastases.Neuro-oncology · 2025Article
- Super-resolution sodium MRI of human gliomas at 3T using physics-based generative artificial intelligence.Journal of neuro-oncology · 2025Article
- Advanced imaging characterization of post-chemoradiation glioblastoma stratified by diffusion MRI phenotypes known to predict favorable anti-VEGF response.Journal of neuro-oncology · 2025Article
- Leveraging Physics-Based Synthetic MR Images and Deep Transfer Learning for Artifact Reduction in Echo-Planar Imaging.AJNR. American journal of neuroradiology · 2025Article
- 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
- Assessing Glioblastoma Treatment Response Using Machine Learning Approach Based on Magnetic Resonance Images Radiomics: An Exploratory Study.Health science reports · 2025Article
- Radio-pathomic estimates of cellular growth kinetics predict survival in recurrent glioblastoma.CNS oncology · 2024Article
- Robust AI-Driven Segmentation of Glioblastoma T1c and FLAIR MRI Series and the Low Variability of the MRIMath© Smart Manual Contouring Platform.Diagnostics (Basel, Switzerland) · 2024Article
- Artificial Intelligence Detection and Segmentation Models: A Systematic Review and Meta-Analysis of Brain Tumors in Magnetic Resonance Imaging.Mayo Clinic proceedings. Digital health · 2024Article
- Change in volumetric tumor growth rate after cytotoxic therapy is predictive of overall survival in recurrent glioblastoma.Neuro-oncology advancesArticle
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Authors and funding
24 authors.
Funding
Abstract
Background: Accurate and repeatable measurement of high-grade glioma (HGG) enhancing (Enh.) and T2/FLAIR hyperintensity/edema (Ed.) is required for monitoring treatment response. 3D measurements can be used to inform the modified Response Assessment in Neuro-oncology criteria. We aim to develop an HGG volumetric measurement and visualization AI algorithm that is generalizable and repeatable. Methods: A single 3D-Convoluted Neural Network, NS-HGlio, to analyze HGG on MRIs using 5-fold cross validation was developed using retrospective (557 MRIs), multicentre (38 sites) and multivendor (32 scanners) dataset divided into training (70%), validation (20%), and testing (10%). Six neuroradiologists created the ground truth (GT). Additional Internal validation (IV, three institutions) using 70 MRIs, and External validation (EV, single institution) using 40 MRIs through measuring the Dice Similarity Coefficient (DSC) of Enh., Ed. ,and Enh. + Ed. (WholeLesion/WL) tumor tissue and repeatability testing on 14 subjects from the TCIA MGH-QIN-GBM dataset using volume correlations between timepoints were performed. Results: IV Preoperative median DSC Enh. 0.89 (SD 0.11), Ed. 0.88 (0.28), WL 0.88 (0.11). EV Preoperative median DSC Enh. 0.82 (0.09), Ed. 0.83 (0.11), WL 0.86 (0.06). IV Postoperative median DSC Enh. 0.77 (SD 0.20), Ed 0.78. (SD 0.09), WL 0.78 (SD 0.11). EV Postoperative median DSC Enh. 0.75 (0.21), Ed 0.74 (0.12), WL 0.79 (0.07). Repeatability testing; Intraclass Correlation Coefficient of 0.95 Enh. and 0.92 Ed. Conclusion: NS-HGlio is accurate, repeatable, and generalizable. The output can be used for visualization, documentation, treatment response monitoring, radiation planning, intra-operative targeting, and estimation of Residual Tumor Volume among others.
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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.