SynthesisMedical physics2020
Reproducibility analysis of multi-institutional paired expert annotations and radiomic features of the Ivy Glioblastoma Atlas Project (Ivy GAP) dataset.
Synthesis in Medical physics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it, 42 citations in OpenAlex.
- Reproducibility analysis of multi-institutional paired expert annotations and radiomic features of the Ivy Glioblastoma Atlas Project (Ivy GAP) dataset.Medical physics · 2020Pooled it
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- NCT-CXR: Enhancing Pulmonary Abnormality Segmentation on Chest X-Rays Using Improved Coordinate Geometric Transformations.Journal of imaging · 2025Article
- Diagnostic Accuracy of Deep Learning Models in Predicting Glioma Molecular Markers: A Systematic Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 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
- Implication of tumor morphology and MRI characteristics on the accuracy of automated versus human segmentation of GBM areas.Scientific reports · 2025Article
- Radiomics in radiology: What the radiologist needs to know about technical aspects and clinical impact.La Radiologia medica · 2024Review
- Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 2: recommendations for standardisation, validation, and good clinical practice.The Lancet. Oncology · 2024Review
- An accessible deep learning tool for voxel-wise classification of brain malignancies from perfusion MRI.Cell reports. Medicine · 2024Article
- pH-Weighted amine chemical exchange saturation transfer echo planar imaging visualizes infiltrating glioblastoma cells.Neuro-oncology · 2024Article
- Predicting the spatio-temporal response of recurrent glioblastoma treated with rhenium-186 labelled nanoliposomes.Brain multiphysics · 2023Article
- Dual adversarial deconfounding autoencoder for joint batch-effects removal from multi-center and multi-scanner radiomics data.Scientific reports · 2023Article
- Assessment of brain cancer atlas maps with multimodal imaging features.Journal of translational medicine · 2023Review
- The federated tumor segmentation (FeTS) tool: an open-source solution to further solid tumor research.Physics in medicine and biology · 2022Article
- Hypoxia-induced polypoid giant cancer cells in glioma promote the transformation of tumor-associated macrophages to a tumor-supportive phenotype.CNS neuroscience & therapeutics · 2022Article
- Stable and Discriminatory Radiomic Features from the Tumor and Its Habitat Associated with Progression-Free Survival in Glioblastoma: A Multi-Institutional Study.AJNR. American journal of neuroradiology · 2022Article
Corrections and comments
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Authors and funding
16 authors at 6 institutions in 1 country.
Funding
Abstract
purposeThe availability of radiographic magnetic resonance imaging (MRI) scans for the Ivy Glioblastoma Atlas Project (Ivy GAP) has opened up opportunities for development of radiomic markers for prognostic/predictive applications in glioblastoma (GBM). In this work, we address two critical challenges with regard to developing robust radiomic approaches: (a) the lack of availability of reliable segmentation labels for glioblastoma tumor sub-compartments (i.e., enhancing tumor, non-enhancing tumor core, peritumoral edematous/infiltrated tissue) and (b) identifying "reproducible" radiomic features that are robust to segmentation variability across readers/sites. ACQUISITION AND VALIDATION
methodsFrom TCIA's Ivy GAP cohort, we obtained a paired set (n = 31) of expert annotations approved by two board-certified neuroradiologists at the Hospital of the University of Pennsylvania (UPenn) and at Case Western Reserve University (CWRU). For these studies, we performed a reproducibility study that assessed the variability in (a) segmentation labels and (b) radiomic features, between these paired annotations. The radiomic variability was assessed on a comprehensive panel of 11 700 radiomic features including intensity, volumetric, morphologic, histogram-based, and textural parameters, extracted for each of the paired sets of annotations. Our results demonstrated (a) a high level of inter-rater agreement (median value of DICE ≥0.8 for all sub-compartments), and (b) ≈24% of the extracted radiomic features being highly correlated (based on Spearman's rank correlation coefficient) to annotation variations. These robust features largely belonged to morphology (describing shape characteristics), intensity (capturing intensity profile statistics), and COLLAGE (capturing heterogeneity in gradient orientations) feature families. DATA FORMAT AND USAGE NOTES: We make publicly available on TCIA's Analysis Results Directory (https://doi.org/10.7937/9j41-7d44), the complete set of (a) multi-institutional expert annotations for the tumor sub-compartments, (b) 11 700 radiomic features, and (c) the associated reproducibility meta-analysis. POTENTIAL APPLICATIONS: The annotations and the associated meta-data for Ivy GAP are released with the purpose of enabling researchers toward developing image-based biomarkers for prognostic/predictive applications in GBM.
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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.