Predicting telomerase reverse transcriptase promoter mutation status in glioblastoma by whole-tumor multi-sequence magnetic resonance texture analysis

Magnetic Resonance Imaging, Journal Year: 2025, Volume and Issue: 118, P. 110360 - 110360

Published: Feb. 20, 2025

Language: Английский

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Diffusion imaging in gliomas: how ADC values forecast glioma genetics

Polish Journal of Radiology, Journal Year: 2025, Volume and Issue: 90, P. 103 - 113

Published: Feb. 20, 2025

Purpose This study investigates the relationship between diffusion-weighted imaging (DWI) and mean apparent diffusion coefficient (ADC) values in predicting genetic molecular features of gliomas. The goal is to enhance non-invasive diagnostic methods support personalised treatment strategies by clarifying association biomarkers tumour genotypes. Material A total 91 glioma patients treated August 2023 March 2024 were included analysis. All underwent preoperative magnetic resonance (MRI), including DWI, had available histopathological test results. Clinical data, characteristics, markers such as IDH1 mutation, MGMT promoter methylation, EGFR amplification, TERT pathogenic variant, CDKN2A deletion collected. Statistical analysis was performed identify correlations ADC values, MRI perfusion parameters, characteristics. Results Significant associations found lower aggressive features, IDH1-wildtype, unmethylated status, amplification. Additionally, distinct patterns observed gliomas with CDKN2A, TP53, PTEN gene deletions. These findings further supported contrast enhancement other indicating their role characteri­sation. Conclusions DWI measurements demonstrate strong potential tools for genetics. can aid characterisation provide valuable insights guiding strategies.

Language: Английский

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Novel Imaging Approaches for Glioma Classification in the Era of the World Health Organization 2021 Update: A Scoping Review

Cancers, Journal Year: 2024, Volume and Issue: 16(10), P. 1792 - 1792

Published: May 8, 2024

The 2021 WHO classification of CNS tumors is a challenge for neuroradiologists due to the central role molecular profile tumors. potential novel data analysis tools in neuroimaging must be harnessed maintain its predicting tumor subgroups. We performed scoping review determine current evidence and research gaps. A comprehensive literature search was conducted regarding glioma subgroups according use MRI, radiomics, machine learning, deep learning algorithms. Sixty-two original articles were included analyzed by extracting on study design results. Only 8% studies pediatric patients. Low-grade gliomas diffuse midline represented one-third papers. Public datasets utilized 22% studies. Conventional imaging sequences prevailed; functional MRI (DWI, PWI, CEST, etc.) are underrepresented. Multiparametric yielded best prediction IDH mutation 1p/19q codeletion status remain focus with limited other Reported AUC values range from 0.6 0.98. Studies designed assess generalizability scarce. Performance worse smaller (e.g., codeleted or IDH1/2 mutated gliomas). More high-quality designs diversity population techniques needed.

Language: Английский

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-New frontiers in domain-inspired radiomics and radiogenomics: increasing role of molecular diagnostics in CNS tumor classification and grading following WHO CNS-5 updates

Cancer Imaging, Journal Year: 2024, Volume and Issue: 24(1)

Published: Oct. 7, 2024

Abstract Gliomas and Glioblastomas represent a significant portion of central nervous system (CNS) tumors associated with high mortality rates variable prognosis. In 2021, the World Health Organization (WHO) updated its Glioma classification criteria, most notably incorporating molecular markers including CDKN2A/B homozygous deletion, TERT promoter mutation, EGFR amplification, + 7/−10 chromosome copy number changes, others into grading adult pediatric Gliomas. The inclusion these corresponding introduction new subtypes has allowed for more specific tailoring clinical interventions inspired wave Radiogenomic studies seeking to leverage medical imaging information explore diagnostic prognostic implications biomarkers. Radiomics, deep learning, combined approaches have enabled development powerful computational tools MRI analysis correlating characteristics various biomarkers integrated WHO CNS-5 guidelines. Recent leveraged methods accurately classify in accordance molecular-based criteria based solely on non-invasive MRI, demonstrating great promise tools. this review, we relative benefits drawbacks frameworks highlight technical innovations presented by recent landscape fast evolving subtyping. Furthermore, potential challenges routine radiological workflows, aiming enhance patient care optimize outcomes field CNS tumor management, been highlighted.

Language: Английский

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MRI radiomics model for predicting TERT promoter mutation status in glioblastoma

Brain and Behavior, Journal Year: 2023, Volume and Issue: 13(12)

Published: Dec. 1, 2023

Abstract Background and purpose The presence of TERT promoter mutations has been associated with worse prognosis resistance to therapy for patients glioblastoma (GBM). This study aimed determine whether the combination model different feature selections classification algorithms based on multiparameter MRI can be used predict subtype in GBM patients. Methods A total 143 were included our retrospective study, 2553 features obtained. datasets randomly divided into training test sets a ratio 7:3. synthetic minority oversampling technique was achieve data balance. Pearson correlation coefficients dimension reduction. Three five selected features. Finally, 10‐fold cross validation applied dataset. Results eight generated by recursive elimination (RFE) linear discriminant analysis (LDA) showed greatest diagnostic performance (area under curve values training, validation, testing sets: 0.983, 0.964, 0.926, respectively), followed relief random forest (RF), variance RF. Furthermore, optimal selection separately evaluating those algorithms, RF most preferable algorithm assessing three selectors. ADC entropy parameter that made contribution discrimination mutations. Conclusions Radiomics RFE LDA mainly good predicting GBM.

Language: Английский

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Evolution of Molecular Biomarkers and Precision Molecular Therapeutic Strategies in Glioblastoma

Cancers, Journal Year: 2024, Volume and Issue: 16(21), P. 3635 - 3635

Published: Oct. 29, 2024

Glioblastoma is the most commonly occurring malignant brain tumor, with a high mortality rate despite current treatments. Its classification has evolved over years to include not only histopathological features but also molecular findings. Given heterogeneity of glioblastoma, biomarkers for diagnosis have become essential initiating treatment therapies, while new technologies detecting specific variations using computational tools are being rapidly developed. Advances in genetics made possible creation tailored therapies based on targets, various degrees success. This review provides an overview latest advances fields histopathology and radiogenomics use markers management as well development targeting common markers. Furthermore, we offer summary results recent preclinical clinical trials recognize trends investigation understand future directions targeted glioblastoma.

Language: Английский

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