Poster — Wed Eve—44: CO‐Registered Multi‐Modality Pattern Analysis Segmentation System (COMPASS) for Radiation Targeting of Head and Neck Cancer Using FDG PET/CT
Bibliographic record
Abstract
Introduction: Previous attempts to segment tumours for radiation therapy targeting based on FDG‐PET image thresholds have had little success. However, if the texture information available in PET and CT images is used, more accurate and reliable differentiation of abnormal and normal tissues may be possible. Objective: To develop an automated image segmentation method for head and neck cancer (HNC) using texture analysis of co‐registered FDG‐PET/CT images. Methods: CO‐registered Multi‐modality Pattern Analysis Segmentation System (COMPASS) was developed using a region‐of‐interest‐based Decision Tree K‐Nearest‐Neighbors (DTKNN) classifier. 14 PET and 13 CT texture features such as coarseness, busyness and Left/right symmetrical ratio were calculated for each voxel from corresponding PET and CT images within a window centered on the voxel. Then the voxel was classified as “tumor” or “non‐tumor” using the DTKNN classifier. PET/CT images of 10 patients with HNC who had their primary tumors and positive nodes manually segmented by three radiation oncologists were used for evaluation. Results: The sensitivity per patient was 83%±19% when “true positive voxels” were defined as those voxels identified by at least two physicians as tumor. The specificity was 95%±2% when “true negative” voxels were all soft tissue voxels not identified by any of three physicians as tumor. Results of COMPASS were significantly better than those of three previously published PET threshold‐based methods. Conclusions: This work suggests that an automated segmentation method based on texture classification of FDG‐PET/CT images has the potential to provide accurate delineation of HNC.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.070 | 0.030 |
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".