Radiation-specific Automated Dosimetric dental, Mandible, and maxilla Annotation for Predicting Periodontal Problems (RADMAP): A semi-automated tool for dosimetric oral risk communication and osteoradionecrosis assessment
Bibliographic record
Abstract
Purpose/Objectives: Osteoradionecrosis of the jaw (ORN) is a severe complication of head and neck cancer (HNC) radiotherapy (RT), significantly impacting patient quality of life. Current dose assessment relies on whole-mandible dosimetry, limiting personalized, tooth-specific region risk evaluation. The lack of standardized methods to document and analyze dose distributions to tooth-bearing regions further hampers dental and maxillofacial decision-making. This study develops and evaluates RADMAP, a (semi-)automated tool for segmenting tooth-based jaw regions, enabling patient- and tooth-specific radiation dose mapping to improve dental dose reporting and ORN risk assessment. Methods and Materials: A total of 736 tooth locations from 23 HNC patients treated with definitive RT were analyzed, including 11 who developed ORN. The RADMAP tool applies an angular ray-based algorithm to automatically segment the mandible and maxilla into 32 tooth-specific jaw regions, with optional manual refinement (semi-automated), outputting radiation dose mapping in both tabular and odontogram formats. Mean dose values from manually contoured tooth roots were compared with RADMAP-segmented alveolar regions (fully and semi-automated). Interobserver agreement among six users was evaluated. Whole-mandible, manual tooth, and tooth-based jaw segment (alveolar and basal) doses were compared between regions with and without ORN development. Results: = 0.98, p<0.0001), demonstrating accurate dose estimation. Interobserver agreement showed 95% limits of ±2.9 Gy for mandibular alveolar segments, confirming reproducibility. Tooth-based jaw segments showed a significant differentiation in radiation dose for the ORN-positive versus ORN-negative sites (difference in mean dose (DIM): 12.4 ±3.7 Gy, p=0.0008), which was not seen when considering the whole-mandible dose (DIM=6.2 ±4.3 Gy, p=0.17), demonstrating RADMAP's promise for improved ORN risk assessment. Conclusion: RADMAP enables accurate, tooth-specific dose mapping of the mandible and maxilla, and potential improved ORN risk differentiation beyond whole-mandible dosimetry. Tooth-based dose reporting can personalize treatment, enhance multidisciplinary communication, and support prevention of radiation-related orodental sequelae.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".