Radiological response assessment after stereotactic body radiotherapy for spine metastases using magnetic resonance imaging: a systematic review
Bibliographic record
Abstract
Background and purpose: Magnetic resonance imaging (MRI) plays a central role in evaluating treatment response after stereotactic body radiotherapy (SBRT) for spinal metastases. However, current guidelines focus mainly on conventional MRI sequences and lack standardized, comprehensive criteria for post-treatment assessment. This systematic review aimed to summarize available evidence on MRI-based response assessment following spine SBRT, emphasizing the potential of advanced MRI techniques and computational tools to improve clinical decision-making. Materials and methods: We systematically searched PubMed, Scopus, Web of Science, and Embase from their inception to August 1, 2024. Two reviewers independently screened studies on MRI-based response assessment after SBRT for spinal metastases, evaluated eligibility, and extracted data on MRI techniques, response criteria, imaging biomarkers, and clinical outcomes. Results: , correlated with improved outcomes, including pain relief and local control. Pseudo-progression and intralesional fatty content were identified as key imaging features that may mimic progression. Diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) mapping showed promise as response biomarkers, but lack clinical validation. Radiomics and machine learning models improved predictive accuracy for treatment outcomes and individual follow-up strategies. Conclusions: MRI provides essential morphological and functional biomarkers for response assessment after spine SBRT. Standardized, multi-parametric MRI protocols and computational tools are needed to optimize patient care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".