ArticleFrontiers in oncology2022
3D vs. 2D MRI radiomics in skeletal Ewing sarcoma: Feature reproducibility and preliminary machine learning analysis on neoadjuvant chemotherapy response prediction.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 citations in OpenAlex.
- Performance comparison and future perspectives of deep learning and classical machine learning in bone tumor applications: a systematic review (2019-2025).BMC medical informatics and decision making · 2026Pooled it
- Imaging of ewing sarcoma: an updated analysis including presenting features, prognostic imaging biomarkers, and treatment response assessment.Skeletal radiology · 2026Review
- Radiomic analysis of medical imaging for classification in oncology and recommendations for clinically initiated research: a literature review.Quantitative imaging in medicine and surgery · 2026Review
- A proposed radiological model for preoperative chemotherapy response prediction in patients with skeletal Ewing sarcoma.Skeletal radiology · 2026Article
- Deep learning for automated segmentation of central cartilage tumors on MRI.European radiology experimental · 2025Article
- Article
- Predictive Performance of Radiomic Features Extracted from Breast MR Imaging in Postoperative Upgrading of Ductal Carcinoma in Situ to Invasive Carcinoma.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025Article
- Artificial Intelligence in Primary Malignant Bone Tumor Imaging: A Narrative Review.Diagnostics (Basel, Switzerland) · 2025Review
- Harnessing Artificial Intelligence in Pediatric Oncology Diagnosis and Treatment: A Review.Cancers · 2025Review
- Preoperative prediction of the Lauren classification in gastric cancer using automated nnU-Net and radiomics: a multicenter study.Insights into imaging · 2025Article
- A reduction in tumor volume exceeding 65% predicts a good histological response to neoadjuvant chemotherapy in patients with Ewing sarcoma.Skeletal radiology · 2024Article
- Applications of Artificial Intelligence for Pediatric Cancer Imaging.AJR. American journal of roentgenology · 2024Review
- Finding the Pieces to Treat the Whole: Using Radiomics to Identify Tumor Habitats.Radiology. Artificial intelligence · 2024Article
- CT and MRI radiomics of bone and soft-tissue sarcomas: an updated systematic review of reproducibility and validation strategies.Insights into imaging · 2024Article
- AI applications in musculoskeletal imaging: a narrative review.European radiology experimental · 2024Review
- CT radiomics to differentiate neuroendocrine neoplasm from adenocarcinoma in patients with a peripheral solid pulmonary nodule: a multicenter study.Frontiers in oncology · 2024Article
- MRI radiomics-based machine learning for classification of deep-seated lipoma and atypical lipomatous tumor of the extremities.La Radiologia medica · 2023Article
Corrections and comments
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Authors and funding
18 authors at 7 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: The extent of response to neoadjuvant chemotherapy predicts survival in Ewing sarcoma. This study focuses on MRI radiomics of skeletal Ewing sarcoma and aims to investigate feature reproducibility and machine learning prediction of response to neoadjuvant chemotherapy. Materials and methods: This retrospective study included thirty patients with biopsy-proven skeletal Ewing sarcoma, who were treated with neoadjuvant chemotherapy before surgery at two tertiary sarcoma centres. 7 patients were poor responders and 23 were good responders based on pathological assessment of the surgical specimen. On pre-treatment T1-weighted and T2-weighted MRI, 2D and 3D tumour segmentations were manually performed. Features were extracted from original and wavelet-transformed images. Feature reproducibility was assessed through small geometrical transformations of the regions of interest mimicking multiple manual delineations, and intraclass correlation coefficient >0.75 defined feature reproducibility. Feature selection also consisted of collinearity and significance analysis. After class balancing in the training cohort, three machine learning classifiers were trained and tested on unseen data using hold-out cross-validation. Results: 1303 (77%) 3D and 620 (65%) 2D radiomic features were reproducible. 4 3D and 4 2D features passed feature selection. Logistic regression built upon 3D features achieved the best performance with 85% accuracy (AUC=0.9) in predicting response to neoadjuvant chemotherapy. Conclusion: Compared to 2D approach, 3D MRI radiomics of Ewing sarcoma had superior reproducibility and higher accuracy in predicting response to neoadjuvant chemotherapy, particularly when using logistic regression classifier.
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