ArticleFrontiers in oncology2023
Deep-learning and conventional radiomics to predict
Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic performance of deep learning for predicting glioma isocitrate dehydrogenase and 1p/19q co-deletion in MRI: a systematic review and meta-analysis.European radiology · 2026Pooled it
- CSF1R and macrophage infiltration: Integrated magnetic resonance imaging radiomics and deep learning-driven models for the preoperative assessment of glioma.Chinese medical journal · 2026Article
- Development and evaluation of multi-strategy fusion machine learning models for predicting postoperative recurrence in non-small cell lung cancer.BMC medical imaging · 2026Article
- A generalizable foundation model for analysis of human brain MRI.Nature neuroscience · 2026Article
- A multimodal ConvNeXt-Tiny deep learning model for simultaneous prediction of IDH mutation and Ki-67 expression in gliomas.PloS one · 2026Article
- Adaptive Vision-Language Transformer for Multimodal CNS Tumor Diagnosis.Biomedicines · 2025Article
- Perilesional dominance: radiomics of multiparametric MRI enhances differentiation of IgG4-Related ophthalmic disease and orbital MALT lymphoma.BMC medical imaging · 2025Article
- Comparisons among radiologist, MR findings and radiomics-clinical models in predicting placenta accreta spectrum disorders: a multicenter study.Archives of gynecology and obstetrics · 2025Article
- Customized SAM-Med3D with multi-view adapter and T2-FLAIR mismatch features for glioma IDH genotyping and grading.Frontiers in behavioral neuroscience · 2025Article
- Novel Imaging Approaches for Glioma Classification in the Era of the World Health Organization 2021 Update: A Scoping Review.Cancers · 2024Article
- Bridging the clinical gap: Confidence informed IDH prediction in brain gliomas using MRI and deep learning.Neuro-oncology advancesArticle
Corrections and comments
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: In adult diffuse glioma, preoperative detection of isocitrate dehydrogenase ( Methods: In this study, a total of 486 patients with adult diffuse gliomas were retrospectively collected from our medical center (n=268) and the public database (TCGA, n=218). All included patients were randomly divided into the training and validation sets by using nested 10-fold cross-validation. A total of 6,736 CR features were extracted from four MRI modalities in each patient, namely T1WI, T1CE, T2WI, and FLAIR. The LASSO algorithm was performed for CR feature selection. In each MRI modality, we applied a CNN+LSTM-based neural network to extract DL features and integrate these features into a DL signature after the fully connected layer with sigmoid activation. Eight classic machine-learning models were analyzed and compared in terms of their prediction performance and stability in Results: Logistic regression (LR) achieved favorable prediction performance (AUC: 0.920 ± 0.043, accuracy: 0.843 ± 0.044), whereas support vector machine with the linear kernel (l-SVM) displayed low prediction performance (AUC: 0.812 ± 0.052, accuracy: 0.821 ± 0.050). With regard to stability, LR also showed high robustness against data perturbation (RSD Conclusion: In
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