ArticleFrontiers in computational neuroscience2020
Overall Survival Prediction in Glioblastoma With Radiomic Features Using Machine Learning.
Article in Frontiers in computational neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers, 1 of them a synthesis that pooled it.
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Who cites it
56 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Survival prediction of glioblastoma patients using machine learning and deep learning: a systematic review.BMC cancer · 2024Pooled it
- Individualized Treatment Effect Inference of Head and Neck Cancer with Multimodal Data.APSIPA Transactions on Signal and Information Processing · 2026Article
- A Radiomics-Driven Model to Distinguish Between Clinically Similar Myxopapillary Ependymomas and Lumbosacral Schwannomas.Neurosurgery practice · 2026Article
- End-to-end 2.5D multisequence-multichannel fusion model for preoperative survival prediction in glioma: a retrospective study.BMC medical imaging · 2026Article
- Machine learning-based integration of dosiomics and pre-radiotherapy multimodal MRI radiomics for survival stratification in patients with glioblastoma multiforme.Radiation oncology (London, England) · 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
- A Novel Serum-Free Triculture Model of Glioblastoma, Astrocytes, and Macrophages.International journal of molecular sciences · 2025Article
- Multimodal Explainable Artificial Intelligence for Prognostic Stratification of Patients With Glioblastoma.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2025Article
- Development of a web-based tool for estimating individualized survival curves in glioblastoma using clinical, mRNA, and tumor microenvironment features with fusion techniques.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Article
- Advanced Deep Learning and Machine Learning Techniques for MRI Brain Tumor Analysis: A Review.Sensors (Basel, Switzerland) · 2025Review
- Radiomics-Based Machine Learning with Natural Gradient Boosting for Continuous Survival Prediction in Glioblastoma.Cancers · 2024Article
- Combining the constitutive TRAIL-secreting induced neural stem cell therapy with the novel anti-cancer drug TR-107 in glioblastoma.Molecular therapy. Oncology · 2024Article
- Prediction of prognosis in glioblastoma with radiomics features extracted by synthetic MRI images using cycle-consistent GAN.Physical and engineering sciences in medicine · 2024Article
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- Genetic Prognostic Factors in Adult Diffuse Gliomas: A 10-Year Experience at a Single Institution.Cancers · 2024Article
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- Radio-anatomical evaluation of clinical and radiomic profile of multi-parametric magnetic resonance imaging of de novo glioblastoma multiforme.Journal of the Egyptian National Cancer Institute · 2024Article
- Integrating imaging and genomic data for the discovery of distinct glioblastoma subtypes: a joint learning approach.Scientific reports · 2024Article
- Treatment-wise Glioblastoma Survival Inference with Multi-parametric Preoperative MRI.Proceedings of SPIE--the International Society for Optical Engineering · 2024Article
Corrections and comments
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7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Glioblastoma is a WHO grade IV brain tumor, which leads to poor overall survival (OS) of patients. For precise surgical and treatment planning, OS prediction of glioblastoma (GBM) patients is highly desired by clinicians and oncologists. Radiomic research attempts at predicting disease prognosis, thus providing beneficial information for personalized treatment from a variety of imaging features extracted from multiple MR images. In this study, first-order, intensity-based volume and shape-based and textural radiomic features are extracted from fluid-attenuated inversion recovery (FLAIR) and T1ce MRI data. The region of interest is further decomposed with stationary wavelet transform with low-pass and high-pass filtering. Further, radiomic features are extracted on these decomposed images, which helped in acquiring the directional information. The efficiency of the proposed algorithm is evaluated on Brain Tumor Segmentation (BraTS) challenge training, validation, and test datasets. The proposed approach achieved 0.695, 0.571, and 0.558 on BraTS training, validation, and test datasets. The proposed approach secured the third position in BraTS 2018 challenge for the OS prediction task.
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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.