ArticleCancers2022
MRI-Based Radiomics Differentiates Skull Base Chordoma and Chondrosarcoma: A Preliminary Study.
Article in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
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Who cites it
19 citing papers in PubMed.
- Advanced Imaging and Biomarker Discovery for Early Diagnosis in Skull Base Pathology.Asian journal of neurosurgery · 2026Article
- Machine learning in automatic detection of chordoma signature, postoperative residuals, and prognosis of skull base chordomas.NPJ precision oncology · 2026Article
- Equity and Generalizability of Radiomics in Orbital Disease: Challenges for Ophthalmology, Otolaryngology, and Plastic Surgery.Diagnostics (Basel, Switzerland) · 2026Review
- Current Trends and Future Prospects of Radiomics and Machine Learning (ML) Models in Spinal Tumors-A Narrative Review.Journal of imaging · 2026Review
- Radiomic Applications in Skull Base Pathology: A Systematic Review of Potential Clinical Uses.Journal of neurological surgery. Part B, Skull base · 2025Article
- MRI-based Habitat Analysis for the Prediction of Progression-Free Survival in Primary Spinal Tumors.Radiology · 2025Article
- Risk calculator for long-term survival prediction of spinal chordoma versus chondrosarcoma: a nationwide analysis.Journal of neuro-oncology · 2025Article
- Quality appraisal of radiomics-based studies on chondrosarcoma using METhodological RadiomICs Score (METRICS) and Radiomics Quality Score (RQS).Insights into imaging · 2025Article
- Radiogenomic method combining DNA methylation profiles and magnetic resonance imaging radiomics predicts patient prognosis in skull base chordoma.Clinical epigenetics · 2025Article
- Artificial intelligence and chordoma: A scoping review of the current landscape and future directions.Brain & spine · 2025Review
- CT and MRI radiomics of bone and soft-tissue sarcomas: an updated systematic review of reproducibility and validation strategies.Insights into imaging · 2024Article
- Applications and Integration of Radiomics for Skull Base Oncology.Advances in experimental medicine and biology · 2024Review
- Machine Learning in Neurosurgery: Toward Complex Inputs, Actionable Predictions, and Generalizable Translations.Cureus · 2024Review
- Artificial Intelligence, Radiomics, and Computational Modeling in Skull Base Surgery.Advances in experimental medicine and biology · 2024Review
- A multicenter retrospective analysis of clinical outcomes of intracranial chondrosarcoma in 26 patients.Scientific reports · 2023Article
- Percutaneous CT-Guided Bone Biopsies: Indications, Feasibility and Diagnostic Yield in the Different Skeletal Sites-From the Skull to the Toe.Diagnostics (Basel, Switzerland) · 2023Review
- Freiburg Neuropathology Case Conference : A 51-year-old Patient Presenting with Epistaxis and Occasional Headaches 16 Years after Diagnosis of a Grade 1 Chondrosarcoma of the Left Petrous Apex.Clinical neuroradiology · 2023Article
- Article
- The future of artificial intelligence in neurosurgery: A narrative review.Surgical neurology international · 2022Review
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Chordoma and chondrosarcoma share common radiographic characteristics yet are distinct clinically. A radiomic machine learning model differentiating these tumors preoperatively would help plan surgery. MR images were acquired from 57 consecutive patients with chordoma (N = 32) or chondrosarcoma (N = 25) treated at the University of Tokyo Hospital between September 2012 and February 2020. Preoperative T1-weighted images with gadolinium enhancement (GdT1) and T2-weighted images were analyzed. Datasets from the first 47 cases were used for model creation, and those from the subsequent 10 cases were used for validation. Feature extraction was performed semi-automatically, and 2438 features were obtained per image sequence. Machine learning models with logistic regression and a support vector machine were created. The model with the highest accuracy incorporated seven features extracted from GdT1 in the logistic regression. The average area under the curve was 0.93 ± 0.06, and accuracy was 0.90 (9/10) in the validation dataset. The same validation dataset was assessed by 20 board-certified neurosurgeons. Diagnostic accuracy ranged from 0.50 to 0.80 (median 0.60, 95% confidence interval 0.60 ± 0.06%), which was inferior to that of the machine learning model (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.