ArticleScientific reports2022
Predicting survival of glioblastoma from automatic whole-brain and tumor segmentation of MR images.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.
- Survival prediction of glioblastoma patients using machine learning and deep learning: a systematic review.BMC cancer · 2024Pooled it
- ASLNet: an explainable deep learning framework for glioma grading and survival prediction.Frontiers in oncology · 2026Article
- Uterine collagen deposition fluctuates throughout the estrous cycle and provides a scaffold for gland visualization via theiScience · 2025Article
- Predicting medical prognosis in patients with glioblastoma during inpatient rehabilitation using bed mobility function.PM & R : the journal of injury, function, and rehabilitation · 2025Article
- Artificial Intelligence in Glioblastoma-Transforming Diagnosis and Treatment.Chinese neurosurgical journal · 2025Review
- Integrating Rapid Evaporative Ionization Mass Spectrometry Classification with Matrix-Assisted Laser Desorption Ionization Mass Spectrometry Imaging and Liquid Chromatography-Tandem Mass Spectrometry to Unveil Glioblastoma Overall Survival Prediction.ACS chemical neuroscience · 2025Article
- A comprehensive evaluation of MRI-based radiogenomics and prognosis prediction in glioma.Frontiers in oncology · 2025Article
- Influence of RNA Methylation on Cancerous Cells: A Prospective Approach for Alteration of In Vivo Cellular Composition.Advances in experimental medicine and biology · 2025Review
- Artificial intelligence innovations in neurosurgical oncology: a narrative review.Journal of neuro-oncology · 2024Review
- Deep Learning Segmentation of Infiltrative and Enhancing Cellular Tumor at Pre- and Posttreatment Multishell Diffusion MRI of Glioblastoma.Radiology. Artificial intelligence · 2024Article
- Cluster-based prognostication in glioblastoma: Unveiling heterogeneity based on diffusion and perfusion similarities.Neuro-oncology · 2024Article
- Estimating overall survival of glioblastoma patients using clinical variables, tumor size, and location.Neuro-oncology advancesArticle
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
Abstract
Survival prediction models can potentially be used to guide treatment of glioblastoma patients. However, currently available MR imaging biomarkers holding prognostic information are often challenging to interpret, have difficulties generalizing across data acquisitions, or are only applicable to pre-operative MR data. In this paper we aim to address these issues by introducing novel imaging features that can be automatically computed from MR images and fed into machine learning models to predict patient survival. The features we propose have a direct anatomical-functional interpretation: They measure the deformation caused by the tumor on the surrounding brain structures, comparing the shape of various structures in the patient's brain to their expected shape in healthy individuals. To obtain the required segmentations, we use an automatic method that is contrast-adaptive and robust to missing modalities, making the features generalizable across scanners and imaging protocols. Since the features we propose do not depend on characteristics of the tumor region itself, they are also applicable to post-operative images, which have been much less studied in the context of survival prediction. Using experiments involving both pre- and post-operative data, we show that the proposed features carry prognostic value in terms of overall- and progression-free survival, over and above that of conventional non-imaging features.
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