ArticleJournal of clinical medicine2021
A Multiparametric MRI-Based Radiomics Analysis to Efficiently Classify Tumor Subregions of Glioblastoma: A Pilot Study in Machine Learning.
Article in Journal of clinical medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- AI for prognosis and treatment stratification in glioblastoma neurosurgery: a systematic review.Journal of neuro-oncology · 2026Pooled it
- Glioma Grade Classification Using Machine Learning and MRI Radiomics: A Single-Center Prospective Study Comparing Original and Wavelet-Transformed Features From Anatomical, Diffusion-Weighted, and Post-Contrast Imaging.Health science reports · 2026Article
- Ethnic Disparities in Glioblastoma Markers: Impact of Chromosome 7 Gain and 10 Loss Alterations on Clinical Survival Outcomes.Oncology research · 2026Article
- The Role of Artificial Intelligence in Modern Medicine: Clinical Applications, Economic Implications, and Ethical Considerations.Current pharmaceutical design · 2026Review
- Predicting the molecular subtypes of 2021 WHO grade 4 glioma by a multiparametric MRI-based machine learning model.BMC cancer · 2025Article
- A Review of Artificial Intelligence-Based Systems for Non-Invasive Glioblastoma Diagnosis.Life (Basel, Switzerland) · 2025Review
- Prediction of early recurrence of adult-type diffuse gliomas following radiotherapy using multi-modal magnetic resonance images.Medical physics · 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
- Interpreting deep learning models for glioma survival classification using visualization and textual explanations.BMC medical informatics and decision making · 2023Article
- Radiomics characterization of tissues in an animal brain tumor model imaged using dynamic contrast enhanced (DCE) MRI.Scientific reports · 2023Article
- Imaging biomarkers for clinical applications in neuro-oncology: current status and future perspectives.Biomarker research · 2023Review
- Magnetic Resonance Imaging of Primary Adult Brain Tumors: State of the Art and Future Perspectives.Biomedicines · 2023Review
- MRI radiomics combined with clinicopathologic features to predict disease-free survival in patients with early-stage cervical cancer.The British journal of radiology · 2022Article
- Pseudoprogression prediction in high grade primary CNS tumors by use of radiomics.Scientific reports · 2022Article
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3 authors.
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Abstract
Glioblastoma multiforme (GBM) carries a poor prognosis and usually presents with heterogenous regions of a necrotic core, solid part, peritumoral tissue, and peritumoral edema. Accurate demarcation on magnetic resonance imaging (MRI) between the active tumor region and perifocal edematous extension is essential for planning stereotactic biopsy, GBM resection, and radiotherapy. We established a set of radiomics features to efficiently classify patients with GBM and retrieved cerebral multiparametric MRI, including contrast-enhanced T1-weighted (T1-CE), T2-weighted, T2-weighted fluid-attenuated inversion recovery, and apparent diffusion coefficient images from local patients with GBM. A total of 1316 features on the raw MR images were selected for each annotated area. A leave-one-out cross-validation was performed on the whole dataset, the different machine learning and deep learning techniques tested; random forest achieved the best performance (average accuracy: 93.6% necrosis, 90.4% solid part, 95.8% peritumoral tissue, and 90.4% peritumoral edema). The features from the enhancing tumor and the tumor shape elongation of peritumoral edema region for high-risk groups from T1-CE. The multiparametric MRI-based radiomics model showed the efficient classification of tumor subregions of GBM and suggests that prognostic radiomic features from a routine MRI exam may also be significantly associated with key biological processes that affect the response to chemotherapy in GBM.
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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.