Measuring the optical characteristics of medulloblastoma with optical coherence tomography
Bibliographic record
Abstract
Medulloblastoma is the most common malignant pediatric brain tumor. Standard treatment consists of surgical resection, followed by radiation and high-dose chemotherapy. Despite these efforts, recurrence is common, leading to reduced patient survival. Even with successful treatment, there are often severe long-term neurologic impacts on the developing nervous system. We present two quantitative techniques that use a high-resolution optical imaging modality: optical coherence tomography (OCT) to measure refractive index, and the optical attenuation coefficient. To the best of our knowledge, this study is the first to demonstrate OCT analysis of medulloblastoma. Refractive index and optical attenuation coefficient were able to differentiate between normal brain tissue and medulloblastoma in mouse models. More specifically, optical attenuation coefficient imaging of normal cerebellum displayed layers of grey matter and white matter, which were indistinguishable in the structural OCT image. The morphology of the tumor was distinct in the optical attenuation coefficient imaging. These inherent properties may be useful during neurosurgical intervention to better delineate tumor boundaries and minimize resection of normal tissue.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".