Devising novel imaging biomarkers for Human Papillomavirus (HPV) status in oropharyngeal squamous cell carcinoma (OPSCC): applying radiomics and machine learning algorithms
Bibliographic record
Abstract
Purpose HPV-positive and HPV-negative OPSCC are biologically distinct entities, with different prognosis and divergent AJCC/UICC staging schemes. Radiomics refers to automated extraction of shape, intensity and texture features from target lesions on medical images – inaccessible to visual interpretation. We applied machine learning classifiers to devise radiomics signatures to determine the HPV-status in OPSCC. Methods Imaging data was retrieved form The Cancer Imaging Archive and our institutional archives. Patients with OPSCC, known HPV/p16-status, and pre-treatment FDG‑PET / non‑contrast CT were included. The primary tumors were delineated on PET and CT scans. 1040 radiomics features describing texture, shape and signal intensity characteristics were extracted from each tumor and per imaging modality. For HPV-status prediction, LASSO regression feature selection (LASSO) and random forest (RF) machine learning classifiers were applied in 10-fold cross validation, repeated 10x. The area under the receiver operating characteristic curve (AUC-ROC) averaged across validation folds is reported. Conclusions A total of 244 HPV-positive and 82 HPV-negative OPSCC patients were included: 46 T1, 119 T2, 107 T3, and 54 T4 UICC/AJCC stage primary tumors were analyzed. The LASSO / RF machine learning algorithm achieved an averaged AUC-ROC of 0.79 (PET/CT), 0.74 (CT) and 0.70 (PET). Conclusion Radiomics feature extraction from PET and CT scans, combined with machine learning classifiers can generate imaging biomarkers for HPV in OPSCC primary tumors, which may aid in HPV-classification if standard immunohistochemical staining is equivocal, or supplement the immunohistochemical tests in subjects requiring second-line testing. Poster-PDF A-1164.PDF Publication History Article published online: 10 June 2020 © 2020. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial-License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/). © Georg Thieme Verlag KG Stuttgart · New York
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".