Use of radiomic data to improve imputation of HPV (p16) status in oropharyngeal cancer
Bibliographic record
Abstract
The incidence of oropharyngeal cancer has been steadily increasing during the past decades. This increase is linked with human papillomavirus, one of the most common sexually transmitted diseases in Canada and worldwide. Recent studies have shown the importance of using p16 testing to assess the HPV status of all oropharyngeal cancer patients on diagnostic. However, that practice was not common during early 2000, making historical data flawed.\nMany imputation models have been built to retroactively predict the HPV status of oropharyngeal cancer patients that were not tested. This models are based on clinical data, which is easy to store and analyze. However, recent advancements in the field of radiomics have enabled the use of CT scans obtained from patients to build models for cancer behavior. In this study, we take a novel approach to HPV status imputation by building machine learning models that utilize not only clinical data but also imaging features, aiming to show a significant improvement over classical models. The increase of performance between state of the art clinical models and our models will be assessed through the use of the RADCURE dataset from the Princess Margaret
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".