Focused ultrasound capsulotomy: predicting the probability of successful lesioning based on skull morphology
Bibliographic record
Abstract
OBJECTIVE: MR-guided focused ultrasound anterior capsulotomy (MRgFUS-AC) is an incisionless ablative procedure, which has shown reassuring safety and compelling efficacy in the treatment of refractory obsessive-compulsive disorder and major depressive disorder. However, in some patients lesions cannot be reliably generated due to patient-specific skull morphologies and properties. Despite screening patients for MRgFUS-AC using skull density ratio (SDR), up to 25% of cases experience treatment failure. This variability in technical success limits the real-world applicability of an otherwise highly impactful treatment, and a better predictor of success is needed. METHODS: This study analyzed data from 60 attempted MRgFUS-AC treatments in 57 patients between 2017 and 2024. Treatments were categorized as success or failure based on lesion volume. Preoperative parameters, including SDR, skull thickness, angle of incidence, CSF volume, brain and head volumes, and lesion side, were recorded. Logistic and machine learning models were evaluated to construct a preoperative model to predict the probability of technical success. RESULTS: A total of 157 lesions were treated, of which 31 experienced treatment failure. Higher SDR, thinner skulls, and lower incident angles were significantly associated with successful outcomes (all p < 0.05). The logistic regression model performed the best among the models tested, with an accuracy of 0.81 ± 0.07 and an F1 score of 0.89 ± 0.04. The model was incorporated into a predictive tool to aid in identifying candidates for MRgFUS-AC. CONCLUSIONS: SDR, skull thickness, and angle of incidence significantly influenced the likelihood of successful MRgFUS-AC lesioning. Incorporating these three parameters into a predictive tool can dramatically reduce technical failure rates and may be especially informative in patients with an SDR between 0.35 and 0.55.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".