ArticleBrain : a journal of neurology2022
An MRI radiomics approach to predict survival and tumour-infiltrating macrophages in gliomas.
Article in Brain : a journal of neurology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 168 papers, 1 of them a synthesis that pooled it.
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Who cites it
168 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Predictive Value of Machine Learning for Platinum Chemotherapy Responses in Ovarian Cancer: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2024Pooled it
- Diagnostic performance and generalizability of a clinical-ultrasound radiomics model for predicting extrathyroidal extension in thyroid carcinoma: a retrospective study.Annals of medicine · 2026Article
- Biomimetic membrane-coated metal nanoplatforms for enhanced BBB penetration and targeted glioblastoma therapy.International journal of pharmaceutics: X · 2026Review
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- Development and internal validation of a multivariable MRI-based radiomics prediction model for preoperative cervical lymph node metastasis in thyroid cancer.Gland surgery · 2026Article
- Prediction of microvascular invasion in hepatocellular carcinoma using contrast-enhanced ultrasound and deep learning.Nature communications · 2026Article
- Multimodal integration of radiomics, pathomics, and clinical data enhances grading of adult diffuse gliomas using machine learning.Neuroradiology · 2026Article
- Radiologic response assessment in patients with desmoid-type fibromatosis treated with percutaneous cryoablation.European radiology experimental · 2026Article
- Predicting glioma survival and extracellular matrix remodeling through MRI radiogenomics.Cell reports. Medicine · 2026Article
- Radiomics model integrating MRI and ECV enhances prediction accuracy for progression in high-grade glioma.NPJ precision oncology · 2026Article
- Application of a multimodal MRI model integrating radiomics and habitat features for predicting glioma pathology and prognosis.BMC medical imaging · 2026Article
- Radiogenomics deciphers tumor heterogeneity in hepatocellular carcinoma and combined hepatocellular-cholangiocarcinoma.Discover oncology · 2026Article
- Downregulation of SLC12A5 in glioblastoma multiforme: a novel prognostic biomarker associated with brain edema and radiomic features.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- SERPINH1, a regulatory factor of oligodendroglioma cell invasion and extracellular matrix secretion, as a novel prognostic biomarker and therapeutic target.Translational cancer research · 2026Article
- Multimodal data-based graph convolutional networks for predicting outcomes in ovarian cancer receiving neoadjuvant chemotherapy.NPJ precision oncology · 2026Article
- Glioblastoma survival prediction through MRI and clinical data integration with transfer learning.International journal of computer assisted radiology and surgery · 2026Article
- Multiparametric MRI radiomics model incorporating ADC improves differentiation of benign and malignant sinonasal tumors.Neuroradiology · 2026Article
- Article
- Radiomics of T2-weighted MRI for pretreatment prediction of prognosis and temozolomide chemosensitivity in glioma.BMC cancer · 2026Article
108 more citing papers are in PubMed but not listed here.
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14 authors.
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Abstract
Preoperative MRI is one of the most important clinical results for the diagnosis and treatment of glioma patients. The objective of this study was to construct a stable and validatable preoperative T2-weighted MRI-based radiomics model for predicting the survival of gliomas. A total of 652 glioma patients across three independent cohorts were covered in this study including their preoperative T2-weighted MRI images, RNA-seq and clinical data. Radiomic features (1731) were extracted from preoperative T2-weighted MRI images of 167 gliomas (discovery cohort) collected from Beijing Tiantan Hospital and then used to develop a radiomics prediction model through a machine learning-based method. The performance of the radiomics prediction model was validated in two independent cohorts including 261 gliomas from the The Cancer Genomae Atlas database (external validation cohort) and 224 gliomas collected in the prospective study from Beijing Tiantan Hospital (prospective validation cohort). RNA-seq data of gliomas from discovery and external validation cohorts were applied to establish the relationship between biological function and the key radiomics features, which were further validated by single-cell sequencing and immunohistochemical staining. The 14 radiomic features-based prediction model was constructed from preoperative T2-weighted MRI images in the discovery cohort, and showed highly robust predictive power for overall survival of gliomas in external and prospective validation cohorts. The radiomic features in the prediction model were associated with immune response, especially tumour macrophage infiltration. The preoperative T2-weighted MRI radiomics prediction model can stably predict the survival of glioma patients and assist in preoperatively assessing the extent of macrophage infiltration in glioma tumours.
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