Sci‐Sat AM(1): Imaging‐06: Proximity‐based modification to an automatic method for tumor delineation using MRSI
Bibliographic record
Abstract
Quantifying the relative levels of Choline (Cho) to N-Acetylaspartate (NAA) has been the method of choice by many groups as a mean to biologically identify tumors in the brain. Mcknight et al. have introduced an automatic technique for delineating tumors biologically using MRSI. A statistical model is used to separate tumors from normal tissue based on the relative concentrations of Choline and NAA in both tissue types; that method is commonly referred to as the Choline-to-NAA Index (CNI). In their work, it is assumed that the variation in the relative levels of Cho to NAA in normal brain is unnoticeable to within 2 standard deviations of the mean. However, developments in MRSI sequences have enabled the detectablity of more variations within the relative levels of Cho to NAA in normal tissue. With the uncertainty in the Cho to NAA levels of normal tissue increasing, it is essential to modify the CNI method to improve its specificity. This work introduces a modification to the CNI method developed by McKnight et al. that would address such increase in uncertainty. Instead of relying on an arbitrary CNI value of 2 to define the tumor boundaries, our method defines a high certainty tumor volume, surrounded by a region of uncertainty. Then based on their proximity to high certainty tumor regions, the voxels in the uncertainty region are segmented to either tumor or normal tissue. Preliminary results suggest that the proposed modified method decreases the number of false positive resulting from the original CNI method.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.041 | 0.019 |
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".