ArticleChild's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery2022
Radiomics signature for the prediction of progression-free survival and radiotherapeutic benefits in pediatric medulloblastoma.
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18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning-based models for prediction of survival in medulloblastoma: a systematic review and meta-analysis.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2025Pooled it
- Fusion of habitat analysis and deep learning on contrast-enhanced T1-weighted imaging for predicting Ki-67 status in pediatric brain tumors.Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2025Article
- Radiomics in pediatric brain tumors: from images to insights.Discover oncology · 2025Review
- Multiparametric MRI-based machine learning system of molecular subgroups and prognosis in medulloblastoma.European radiology · 2025Article
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- Machine learning in image-based outcome prediction after radiotherapy: A review.Journal of applied clinical medical physics · 2025Review
- The predictive value of pre-treatment MRI-based radiomics and clinical characteristics for medulloblastoma recurrence in pediatric patients.Frontiers in neurology · 2025Article
- Delta-Radiomics Using Machine Learning Classifiers With Auxiliary Data Sets to Predict Disease Progression During Magnetic Resonance-Guided Radiotherapy in Adrenal Metastases.JCO clinical cancer informatics · 2025Article
- Advanced imaging techniques and non-invasive biomarkers in pediatric brain tumors: state of the art.Neuroradiology · 2024Review
- Radiomics for differentiation of somaticJournal of medical imaging (Bellingham, Wash.) · 2024Article
- Magnetic Resonance-Guided Cancer Therapy Radiomics and Machine Learning Models for Response Prediction.Tomography (Ann Arbor, Mich.) · 2024Review
- Radiomic- and dosiomic-based clustering development for radio-induced neurotoxicity in pediatric medulloblastoma.Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2024Article
- Article
- Opportunities and Advances in Radiomics and Radiogenomics for Pediatric Medulloblastoma Tumors.Diagnostics (Basel, Switzerland) · 2023Review
- Clinical, Histological, and Molecular Prognostic Factors in Childhood Medulloblastoma: Where Do We Stand?Diagnostics (Basel, Switzerland) · 2023Review
- Construction and prognostic value of enhanced CT image omics model for noninvasive prediction of HRG in bladder cancer based on logistic regression and support vector machine algorithm.Frontiers in oncology · 2022Article
- Radiomic-based prognostic score for survival risk-stratification in pediatric medulloblastoma tumors: A multi-institutional study.Neuro-oncology advancesArticle
- Review
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6 authors.
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Abstract
purposeTo develop and validate a radiomics signature for progression-free survival (PFS) and radiotherapeutic benefits in pediatric medulloblastoma. MATERIALS AND
methodsWe retrospectively enrolled 253 consecutive children with medulloblastoma from two hospitals. A total of 1294 radiomic features were extracted from the region of tumor on the T1-weighted and contrast-enhanced T1-weighted (CE-T1w) MRI. Radiomic feature selection and machine learning modelling were performed to build radiomics signature for the prediction of PFS on the training set. Moreover, the prognostic performance of the clinical parameters was investigated for PFS. The Concordance index (a value of 0.5 indicates no predictive discrimination, and a value of 1 indicates perfect predictive discrimination) was used to measure and compare the prognostic performance of these models.
resultsThe radiomics signature for the prediction of the PFS yielded Concordance indices of 0.711, 0.707, and 0.717 on the training and held-out test sets 1 and 2, respectively. The radiomics nomogram integrating the radiomics signature, age, and metastasis performed better than the nomogram incorporating only clinicopathological factors (C-index, 0.723 vs. 0.665 and 0.722 vs. 0.677 on the held-out test sets 1 and 2, respectively), which was also validated by the good calibration and decision curve analysis. Further analysis demonstrated that patients with lower value of radiomics signature were associated with better clinical outcomes after postoperative radiotherapy (p < 0.001).
conclusionThe radiomics signature and nomogram performed well for the prediction of PFS and could stratify patients underwent postoperative radiotherapy into the high- and low-risk groups with significantly different clinical outcomes.
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