ArticleNeuro-oncology2024
Added prognostic value of 3D deep learning-derived features from preoperative MRI for adult-type diffuse gliomas.
Article in Neuro-oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06829147 (Development and Validation of a Deep Learning Model for Diagnosing Lymph Node Metastasis in Nasopharyngeal Carcinoma Using Histologic Whole Slide Images and Time-dependent Magnetic Resonance Images), which is not on this map. Cited by 17 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Development and Validation of a Deep Learning Model for Diagnosing Lymph Node Metastasis in Nasopharyngeal Carcinoma Using Histologic Whole Slide Images and Time-dependent Magnetic Resonance Images
Who cites it
17 citing papers in PubMed, 19 citations in OpenAlex.
- Automatic Segmentation and Classification of Glioblastoma and Solitary Brain Metastasis Using a Deep Learning Model on Multiparametric MRI.Journal of imaging informatics in medicine · 2026Article
- MRI Deep Learning for Differentiating Glioblastoma, IDH Wild-type from Central Nervous System Diffuse Large B-cell Lymphoma.Cancer research communications · 2026Article
- Tumor habitat analysis using multiparametric MRI as a noninvasive correlate of longitudinal changes in telomerase activity in recurrent IDH-wildtype glioblastoma.Scientific reports · 2026Article
- Habitat-aware radiomics and adaptive 2.5D deep learning predict treatment response and long-term survival in ESCC patients undergoing neoadjuvant chemoimmunotherapy.European journal of nuclear medicine and molecular imaging · 2026Article
- FA-DeepMSM: a few-shot adapted interpretable multimodal survival model for improved prognostic prediction in glioblastoma.Scientific reports · 2026Article
- Revisiting prognosis of oligodendroglioma patients in the 2021 WHO classification: incremental value of imaging features.European radiology · 2026Article
- Adult-type diffuse glioma prediction using MnasNet optimized by the advanced single candidate optimizer.Frontiers in oncology · 2026Article
- GlioSurv: interpretable transformer for multimodal, individualized survival prediction in diffuse glioma.NPJ digital medicine · 2025Article
- A three-classification machine learning model for non-invasive prediction of molecular subtypes in diffuse glioma: a two-center study.Quantitative imaging in medicine and surgery · 2025Article
- A comprehensive evaluation of MRI-based radiogenomics and prognosis prediction in glioma.Frontiers in oncology · 2025Article
- A CT-Based Deep Learning Radiomics Scoring System for Predicting the Prognosis to Repeat TACE in Patients with Hepatocellular Carcinoma: A Multicenter Cohort Study.Journal of hepatocellular carcinoma · 2025Article
- AI-driven 3D CT imaging prediction model for improving preoperative detection of visceral pleural invasion in early-stage lung cancer.PloS one · 2025Article
- Biologically interpretable multi-task deep learning pipeline predicts molecular alterations, grade, and prognosis in glioma patients.NPJ precision oncology · 2024Article
- MultiCubeNet: Multitask deep learning for molecular subtyping and prognostic prediction in gliomas.Neuro-oncology advancesArticle
- Comprehensive multimodal deep learning survival prediction enabled by a transformer architecture: A multicenter study in glioblastoma.Neuro-oncology advancesArticle
- Postsurgical detection of glioma recurrence using MRI radiomics.Neuro-oncology advancesArticle
- Estimating overall survival of glioblastoma patients using clinical variables, tumor size, and location.Neuro-oncology advancesArticle
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11 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundTo investigate the prognostic value of spatial features from whole-brain MRI using a three-dimensional (3D) convolutional neural network for adult-type diffuse gliomas.
methodsIn a retrospective, multicenter study, 1925 diffuse glioma patients were enrolled from 5 datasets: SNUH (n = 708), UPenn (n = 425), UCSF (n = 500), TCGA (n = 160), and Severance (n = 132). The SNUH and Severance datasets served as external test sets. Precontrast and postcontrast 3D T1-weighted, T2-weighted, and T2-FLAIR images were processed as multichannel 3D images. A 3D-adapted SE-ResNeXt model was trained to predict overall survival. The prognostic value of the deep learning-based prognostic index (DPI), a spatial feature-derived quantitative score, and established prognostic markers were evaluated using Cox regression. Model evaluation was performed using the concordance index (C-index) and Brier score.
resultsThe MRI-only median DPI survival prediction model achieved C-indices of 0.709 and 0.677 (BS = 0.142 and 0.215) and survival differences (P < 0.001 and P = 0.002; log-rank test) for the SNUH and Severance datasets, respectively. Multivariate Cox analysis revealed DPI as a significant prognostic factor, independent of clinical and molecular genetic variables: hazard ratio = 0.032 and 0.036 (P < 0.001 and P = 0.004) for the SNUH and Severance datasets, respectively. Multimodal prediction models achieved higher C-indices than models using only clinical and molecular genetic variables: 0.783 vs. 0.774, P = 0.001, SNUH; 0.766 vs. 0.748, P = 0.023, Severance.
conclusionsThe global morphologic feature derived from 3D CNN models using whole-brain MRI has independent prognostic value for diffuse gliomas. Combining clinical, molecular genetic, and imaging data yields the best performance.
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