Abstract PO-051: Development of web-based quality-assurance tool for radiotherapy target delineation for head and neck cancer: Quality evaluation of nasopharyngeal carcinoma
Bibliographic record
Abstract
Abstract Purpose: We developed Label-It, a new web-application for rapid review of radiotherapy (RT) target volumes, and used it to evaluate the relationship between target delineation compliance to the international guidelines and treatment outcomes of nasopharyngeal carcinoma (NPC) patients undergoing definitive RT. Methods and Materials: Our radiographic image database consists of anonymized simulation CT scans, RT structures, and treatment data of 3,211 head and neck cancer patients treated between July 2005 and August 2017. We used 332 patients treated with intensity-modulated RT for pathologically confirmed NPC as the study cohort and imported intermediate risk clinical target volumes of the primary tumor (IR-CTVp) receiving 56 Gy into Label-It. We determined inclusion of anatomic sites within IR-CTVp in accordance with 2018 International guideline for CTV delineation for NPC and correlated the results with time to local failure (TTLF) using Cox-regression. Results: At a median follow-up of 5.6 years, 5-year TTLF and overall survival rates were 93.1% and 85.9% respectively. The most frequently non-compliant anatomic sites were sphenoid sinus (n = 69, 20.8%), followed by cavernous sinus (n = 38, 11.4%), left and right petrous apices (n = 37 and 32, 11.1% and 9.6%), clivus (n = 14, 4.2%), and right and left foramen rotundum (n = 14 and 12, 4.2% and 3.6%). Among 23 patients with a local failure (6.9%), the number of non-compliant cases were 8 for sphenoid sinus, 7 cavernous sinus, 4 left and 3 right petrous apices, and 2 clivus. In Cox regression analysis, T4 disease (p = 0.003), RT alone (p = 0.007), cavernous sinus non-conformity (p = 0.020) were independent prognostic factors for TTLF. Conclusions: Label-It was an effective tool for rapid review of target volumes in a large patient cohort. Despite a high compliance to the international guidelines, inadequate coverage of cavernous sinus was correlated with decreased TTLF. Citation Format: Jun Won Kim, Joseph Marsilla, Michal Kazmierski, Denis Tkachuk, Benjamin Haibe-Kains, Andrew Hope. Development of web-based quality-assurance tool for radiotherapy target delineation for head and neck cancer: Quality evaluation of nasopharyngeal carcinoma [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-051.
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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 | 0.011 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".