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Purpose: The present study aimed to preoperatively predict the status of 1p/19q based on radiomics analysis in patients with World Health Organization (WHO) grade II gliomas.
Methods: This retrospective study enrolled 157 patients with WHO grade II gliomas (76 patients with astrocytomas with mutant IDH, 16 patients with astrocytomas with wild-type IDH, and 65 patients with oligodendrogliomas with mutant IDH and 1p/19q codeletion). Radiomic features were extracted from magnetic resonance images, including T1-weighted, T2-weighted, and contrast T1-weighted images. Elastic net and support vector machines with radial basis function kernel were applied in nested 10-fold cross-validation loops to predict the 1p/19q status. Receiver operating characteristic analysis and precision-recall analysis were used to evaluate the model performance. Student's -tests were then used to compare the posterior probabilities of 1p/19q co-deletion prediction in the group with different 1p/19q status.
Results: Six valuable radiomic features, along with age, were selected with the nested 10-fold cross-validation loops. Five features showed significant difference in patients with different 1p/19q status. The area under curve and accuracy of the predictive model were 0.8079 (95% confidence interval, 0.733-0.8755) and 0.758 (0.6879-0.8217), respectively, and the F1-score of the precision-recall curve achieved 0.6667 (0.5201-0.7705). The posterior probabilities in the 1p/19q co-deletion group were significantly different from the non-deletion group.
Conclusion: Combined radiomics analysis and machine learning showed potential clinical utility in the preoperative prediction of 1p/19q status, which can aid in making customized neurosurgery plans and glioma management strategies before postoperative pathology.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8290517 | PMC |
http://dx.doi.org/10.3389/fonc.2021.616740 | DOI Listing |
Curr Treat Options Oncol
December 2024
Department of Radiation Oncology, University of Washington/Fred Hutchinson Cancer Center, 1959 NE Pacific St, Box 356043, Seattle, WA, 98195, USA.
Discov Oncol
November 2024
Department of Radiation Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, 420 Fuma Rd, Jin'an District, Fuzhou, 350011, Fujian, China.
Background: Ferroptosis is a novel type of programmed cell death in various tumors; however, underlying mechanisms remain unclear. We aimed to develop ferroptosis-related long non-coding RNA (FRlncRNA) risk scores to predict lower-grade glioma (LGG) prognosis and to conduct functional analyses to explore potential mechanisms.
Methods: LGG-related RNA sequencing data were extracted from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases.
J Neurooncol
November 2024
Department of Neurosurgery and Tays Cancer Center, Tampere University Hospital and Tampere University, Tampere, Finland.
Purpose: Extent of brain tumor resection continues to be one of the central decisions taken during standard of care in glioma patients. Here, we aimed to evaluate the most essential molecular factors, such as IDH (isocitrate dehydrogenase) mutation in gliomas classification with patient-derived glioma organoids (PGOs) using differential mobility spectrometry (DMS).
Methods: we prospectively recruited 12 glioma patients, 6 IDH-mutated and 6 IDH wild-type tumors, from which PGOs were generated ex-vivo.
Front Oncol
October 2024
Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Objectives: To investigate the clinical value of hemodynamic parameters derived from dynamic contrast-enhanced MRI (DCE-MRI) in predicting glioma genotypes including isocitrate dehydrogenase () mutation, codeletion status and the tumor proliferation index () noninvasively. And to compare the diagnostic performance of parameters of distributed parameter (DP)model and extended Tofts (Ex-Tofts) model.
Materials And Methods: Dynamic contrast-enhanced MRI (DCE-MRI) data of patients with glioma were prospectively enrolled from April 2021 to May 2023.
Eur Radiol
October 2024
Amsterdam UMC Location Vrije Universiteit Amsterdam, Radiology & Nuclear Medicine Department, Amsterdam, The Netherlands.
Objectives: To develop a gadolinium-free MRI-based diagnosis prediction decision tree (DPDT) for adult-type diffuse gliomas and to assess the added value of gadolinium-based contrast agent (GBCA) enhanced images.
Materials And Methods: This study included preoperative grade 2-4 adult-type diffuse gliomas (World Health Organization 2021) scanned between 2010 and 2021. The DPDT, incorporating eleven GBCA-free MRI features, was developed using 18% of the dataset based on consensus readings.
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