Imaging-based techniques for ablation zone definition and volumetry after laser interstitial thermal therapy (LITT) for intracranial lesions: a systematic review
Bibliographic record
Abstract
PURPOSE: MRI-guided laser interstitial thermal therapy (LITT) is a minimally invasive technique for treating intracranial pathologies. Although the extent of ablation appears prognostically relevant, standardized imaging methods for post-LITT ablation zone measurements are lacking. This systematic review evaluates imaging-based approaches used to measure the ablation zone in patients undergoing LITT. As effect assessment is an integral part of the technique, this study aims to support the development of standardized imaging-based outcome metrics. METHODS: A systematic literature search was conducted in PubMed and Embase (March 15, 2024; updated April 2, 2025). Studies were included if they reported imaging-based methods for determining ablation extent or volume after LITT; studies without methodological detail, non-original research, or non-human studies were excluded. Study selection, data extraction, and risk of bias assessment (Newcastle-Ottawa Scale) were conducted independently by multiple reviewers. RESULTS: A total of 77 studies (2,312 patients) were included. Most studies (82%) were retrospective case series, with 74 (96%) categorized as having moderate risk of bias. All studies utilized MRI to assess post-LITT ablation volume. Conventional MRI sequences were used in 65 studies (84%), among which 54 (83%) used contrast-enhanced imaging. Forty-six studies (60%) reported a single time-point volume assessment. Of the 60 studies using contrast-enhanced imaging, 50% specified inclusion or exclusion of the enhancing rim. CONCLUSION: Our results show considerable variation and underreporting regarding rim inclusion, measurement timing, and volume definitions. Standardized imaging protocols, covering timing, modalities, and rim handling, are essential to improve LITT research and outcomes. We propose four recommendations to guide future reporting of imaging methods. CLINICAL TRIAL NUMBER: Not applicable.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".