Single-cell Raman spectroscopy detects pediatric focal cortical dysplasia
Bibliographic record
Abstract
Significance: Focal cortical dysplasia (FCD) type II is the leading cause of drug-resistant focal epilepsy in children. While surgical resection offers the only definitive cure, its success is hindered by the challenge of precisely identifying the lesion and its boundaries. Despite advancements in neuroimaging, FCD type II often remains elusive, complicating surgical planning and outcome optimization. Enhanced detection methods are crucial to improving the precision of resection and, ultimately, achieving seizure freedom in affected patients. Aim: Advanced techniques for detecting FCD type II margins during surgery are critically needed to enhance postoperative outcomes. Spontaneous Raman spectroscopy is a label-free optical method that allows the characterization of the tissue's biochemical composition. The goal of this proof-of-concept study was to compare-in pediatric patients-the spectral signature of abnormal cells in FCD tissue with cells associated with the normal cortex. Approach: A Raman microspectroscopy imaging workflow was developed and applied to 70 surgical specimens from 30 focal epilepsy patients diagnosed with FCD type II. Raman spectra from individual cells were recorded from FCD type II specimens (dysmorphic neurons and balloon cells) and normal brains (neurons). Machine learning models (support vector machines) were trained, validated, and tested to distinguish FCD tissue from the normal brain as well as to distinguish between two disease subtypes, i.e., FCD types IIa and IIb. Results: A total of 1420 single-cell spectra were acquired and spectral differences determined between FCD type II and normal cortex, as well as between FCD type IIa and type IIb. Machine learning distinguished FCD type II from the normal cortex with 96% accuracy, 100% sensitivity, and 95% specificity. FCD types IIa and IIb specimens were distinguished with 92% accuracy, 100% sensitivity, and 86% specificity. Conclusions: The Raman spectroscopy signature of single cells associated with FCD tissue was established. This provides credence to the hypothesis that Raman spectroscopy as a technique-if implemented using a fiber optics system-has the potential for safely optimizing the extent of FCD type II resection in pediatric focal epilepsy surgery. In addition, this technique provides insights into multiple biochemical alterations within dysplastic tissues, which may contribute to the underlying mechanisms of epileptogenesis.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".