ArticleJournal of the Royal Statistical Society. Series C, Applied statistics2018
Radiologic image-based statistical shape analysis of brain tumours.
Article in Journal of the Royal Statistical Society. Series C, Applied statistics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
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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
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Who cites it
17 citing papers in PubMed, 35 citations in OpenAlex.
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- Enhancing Empathic Accuracy: Penalized Functional Alignment Method to Correct Temporal Misalignment in Real-Time Emotional Perception.Psychometrika · 2025Article
- Landmark-free statistical shape modelling reveals effects of age and sex on whole muscle morphology among the triceps surae.Royal Society open science · 2025Article
- Back to the Roots: Reconstructing Large and Complex Cranial Defects using an Image-based Statistical Shape Model.Journal of medical systems · 2024Article
- Artificial intelligence in neuro-oncology: advances and challenges in brain tumor diagnosis, prognosis, and precision treatment.NPJ precision oncology · 2024Review
- A Wasserstein-Type Distance for Gaussian Mixtures on Vector Bundles with Applications to Shape Analysis.SIAM journal on imaging sciences · 2024Article
- Assessment of Glioblastoma Multiforme Tumor Heterogeneity via MRI-derived Shape and Intensity Features.Data science in science · 2024Article
- ADASSM: Adversarial Data Augmentation in Statistical Shape Models From Images.Shape in medical imaging : International Workshop, ShapeMI 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings. ShapeMI (Workshop) (2023 : Vancouver, B.C.) · 2023Article
- SAFARI: shape analysis for AI-segmented images.BMC medical imaging · 2022Article
- Association testing for binary trees-A Markov branching process approach.Statistics in medicine · 2022Article
- Tangent functional canonical correlation analysis for densities and shapes, with applications to multimodal imaging data.Journal of multivariate analysis · 2022Article
- Exploration of Multiparameter Hematoma 3D Image Analysis for Predicting Outcome After Intracerebral Hemorrhage.Neurocritical care · 2020Article
- Intracranial glioma xenograft model rapidly reestablishes blood-brain barrier integrity for longitudinal imaging of tumor progression using fluorescence molecular tomography and contrast agents.Journal of biomedical optics · 2020Article
- Machine learning and glioma imaging biomarkers.Clinical radiology · 2020Review
- Elastic Statistical Shape Analysis of Biological Structures with Case Studies: A Tutorial.Bulletin of mathematical biology · 2019Article
- Shape Features of the Lesion Habitat to Differentiate Brain Tumor Progression from Pseudoprogression on Routine Multiparametric MRI: A Multisite Study.AJNR. American journal of neuroradiology · 2018Article
- Analysis of shape data: From landmarks to elastic curves.Wiley interdisciplinary reviews. Computational statisticsArticle
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
Abstract
We propose a curve-based Riemannian geometric approach for general shape-based statistical analyses of tumours obtained from radiologic images. A key component of the framework is a suitable metric that enables comparisons of tumour shapes, provides tools for computing descriptive statistics and implementing principal component analysis on the space of tumour shapes and allows for a rich class of continuous deformations of a tumour shape. The utility of the framework is illustrated through specific statistical tasks on a data set of radiologic images of patients diagnosed with
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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.