Abstract A35: Diagnostic performance and timing of post-treatment 18F-FDG PET/CT for head and neck cancer surveillance: A meta-analysis of reported studies
Bibliographic record
Abstract
Abstract Purpose: The aims of this meta-analysis are to evaluate the diagnostic performance and to explore the optimal timing of post-treatment 18F-FDG PET/CT for HNSCC. Methods: January 2010 to August 2016 was the range for study selection. Subgroup analyses were performed for local/regional failure stratified by treatment-to-scan time interval of ≤3 vs. >3 months. Results: Twenty-four studies (2,256 patients) were included. Compared to ≤3 months, 18F-FDG PET/CT performed >3 months showed significantly improved sensitivity (87% vs. 60%, p=0.020) and specificity (93% vs. 84%, p<0.001) for identifying local failure and marginally improved sensitivity for regional failure (79% vs. 56%, p=0.100). The specificity for regional failure was equally high for >3 months vs. ≤3 months (95% vs. 97%, p=0.35). Conclusions: This meta-analysis showed high NPV but modest PPV for post-treatment 18F-FDG PET/CT for local and regional failure. Sensitivity is improved if performed >3 months for local failure and marginally improved for regional failure. Citation Format: Erin T. Wong, Adam A. Dmytriw, Eugene Yu, John Waldron, Rouhi Fazelzad, John de Almeida, Patrick Veit-Haibach, Brian O’Sullivan, Wei Xu, Shao Hui Huang. Diagnostic performance and timing of post-treatment 18F-FDG PET/CT for head and neck cancer surveillance: A meta-analysis of reported studies [abstract]. In: Proceedings of the AACR-AHNS Head and Neck Cancer Conference: Optimizing Survival and Quality of Life through Basic, Clinical, and Translational Research; 2019 Apr 29-30; Austin, TX. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(12_Suppl_2):Abstract nr A35.
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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 | 0.018 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.054 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".