ArticleNeuro-oncology2023
Deep learning based on preoperative magnetic resonance (MR) images improves the predictive power of survival models in primary spinal cord astrocytomas.
Article in Neuro-oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- Automated Determination of the H3 K27-Altered Status in Spinal Cord Diffuse Midline Glioma by Radiomics Based on T2-Weighted MR Images.AJNR. American journal of neuroradiology · 2023Trial
- TiLCA-Net: nuclei segmentation and classification in pathological images via task-specialized attention and efficient decoding.Quantitative imaging in medicine and surgery · 2026Article
- Radiomics and machine learning for differentiating pediatric intramedullary spinal tumors: a pilot study.European radiology experimental · 2026Article
- Current Trends and Future Prospects of Radiomics and Machine Learning (ML) Models in Spinal Tumors-A Narrative Review.Journal of imaging · 2026Review
- Non-invasive prediction of Ki-67 and p53 biomarkers in spinal ependymoma via deep learning: using multimodal magnetic resonance imaging and clinical data.Biomarker research · 2025Article
- Multi-modal and Multi-view Cervical Spondylosis Imaging Dataset.Scientific data · 2025Article
- Deep transfer learning approach for automated cell death classification reveals novel ferroptosis-inducing agents in subsets of B-ALL.Cell death & disease · 2025Article
- Independent Prognostic Factors and Nomogram Prediction of Cancer-Specific Survival in Postoperative Patients With Spinal Cord Astrocytoma.Global spine journal · 2025Article
- Advances in Molecular Pathology, Diagnosis and Treatment of Spinal Cord Astrocytomas.Technology in cancer research & treatmentReview
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14 authors.
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Abstract
backgroundPrognostic models for spinal cord astrocytoma patients are lacking due to the low incidence of the disease. Here, we aim to develop a fully automated deep learning (DL) pipeline for stratified overall survival (OS) prediction based on preoperative MR images.
methodsA total of 587 patients diagnosed with intramedullary tumors were retrospectively enrolled in our hospital to develop an automated pipeline for tumor segmentation and OS prediction. The automated pipeline included a T2WI-based tumor segmentation model and 3 cascaded binary OS prediction models (1-year, 3-year, and 5-year models). For the tumor segmentation model, 439 cases of intramedullary tumors were used to model training and testing using a transfer learning strategy. A total of 138 patients diagnosed with astrocytomas were included to train and test the OS prediction models via 10 × 10-fold cross-validation using CNNs.
resultsThe dice of the tumor segmentation model with the test set was 0.852. The results indicated that the best input of OS prediction models was a combination of T2W and T1C images and the tumor mask. The 1-year, 3-year, and 5-year automated OS prediction models achieved accuracies of 86.0%, 84.0%, and 88.0% and AUCs of 0.881 (95% CI 0.839-0.918), 0.862 (95% CI 0.827-0.901), and 0.905 (95% CI 0.867-0.942), respectively. The automated DL pipeline achieved 4-class OS prediction (<1 year, 1-3 years, 3-5 years, and >5 years) with 75.3% accuracy.
conclusionsWe proposed an automated DL pipeline for segmenting spinal cord astrocytomas and stratifying OS based on preoperative MR images.
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