Abstract PO-033: The impact of the variation of CT scanner on the prediction of HPV status in head & neck cancer patients
Bibliographic record
Abstract
Abstract Radiomics involves the use of high-dimensional quantitative imaging features for predictive purposes. However, studies showed that these radiomic features are sensitive to the variability of imaging parameters (e.g., scanner model). One of the major challenges in radiomics lies in improving the robustness of quantitative features against the variation in the imaging dataset in multi-center studies. Here, we assess the impact of scanner choice on the computed tomography (CT)-derived radiomic features to predict association of oropharyngeal squamous cell carcinoma with human papillomavirus (HPV), which has a well-established impact on CT-derived radiomic features. This experiment was performed on CT image datasets acquired with two different scanner types. We demonstrate strong scanner dependency by developing a machine learning model to classify HPV status from radiological images. These experiments revealed the effect of scanner type on the robustness of the radiomic features, and the extent of this dependency is reflected on the performance of HPV prediction models. The result of this study highlighted the importance of implementing an appropriate approach to reduce the impact of the imaging domain radiomic features and consequently on the machine learning models. Citation Format: Reza Reiazi, Collin Arrowsmith, Farnoosh Abbas-Aghababazadeh, Christopher Eles, Aria Rezaie, Scott V. Bratman, Andrew J. Hope, Benjamin Haibe-Kains. The impact of the variation of CT scanner on the prediction of HPV status in head & neck cancer patients [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-033.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".