ArticleMedical physics2019
Reliability of tumor segmentation in glioblastoma: Impact on the robustness of MRI-radiomic features.
Article in Medical physics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.
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Who cites it
35 citing papers in PubMed, 1 synthesis or guideline pooled 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
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- Towards automatical tumor segmentation in radiomics: a comparative analysis of various methods and radiologists for both region extraction and downstream diagnosis.BMC medical imaging · 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
- A critical assessment of artificial intelligence in magnetic resonance imaging of cancer.Npj imaging · 2025Review
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- Robustness and classification capabilities of MRI radiomic features in identifying carotid plaque vulnerability.The British journal of radiology · 2024Article
- Marker-controlled watershed algorithm and fuzzy C-means clustering machine learning: automated segmentation of glioblastoma from MRI images in a case series.Annals of medicine and surgery (2012) · 2024Article
- Fractal-Based Morphometrics of Glioblastoma.Advances in neurobiology · 2024Article
- Machine learning-based CT radiomics model to discriminate the primary and secondary intracranial hemorrhage.Scientific reports · 2023Article
- Bibliometric research on the developments of artificial intelligence in radiomics toward nervous system diseases.Frontiers in neurology · 2023Article
- A multi-modality physical phantom for mimicking tumor heterogeneity patterns in PET/CT and PET/MRI.Medical physics · 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
- Radiomics feature reliability assessed by intraclass correlation coefficient: a systematic review.Quantitative imaging in medicine and surgery · 2021Review
- A systematic review and quality of reporting checklist for repeatability and reproducibility of radiomic features.Physics and imaging in radiation oncology · 2021Review
- Effects of interobserver and interdisciplinary segmentation variabilities on CT-based radiomics for pancreatic cancer.Scientific reports · 2021Article
- The clinical guiding value of a radiomics model for androgen deprivation therapy for prostate cancer.The Journal of international medical research · 2021Article
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4 authors.
Funding
Abstract
purposeThe use of radiomic features as biomarkers of treatment response and outcome or as correlates to genomic variations requires that the computed features are robust and reproducible. Segmentation, a crucial step in radiomic analysis, is a major source of variability in the computed radiomic features. Therefore, we studied the impact of tumor segmentation variability on the robustness of MRI radiomic features.
methodFluid-attenuated inversion recovery (FLAIR) and contrast-enhanced T1-weighted (T1WI
resultsOur results show that both segmentation methods are reliable with ICC ≥ 0.96 and standard deviation (SD) of mean differences between the two raters (SD
conclusionSemiautomatic and interactive segmentation methods using two raters are both reliable. The interactive method produced more robust features than the semiautomatic method. We also found that the robustness of radiomic features varied by categories. Therefore, this study could help motivate segmentation methods and feature selection in MRI radiomic studies.
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