Artificial Intelligence Model for Imaging-Based Extranodal Extension Detection and Outcome Prediction in Human Papillomavirus−Positive Oropharyngeal Cancer
Bibliographic record
Abstract
Importance: Although not included in the eighth edition of the American Joint Committee on Cancer Staging System, there is growing evidence suggesting that imaging-based extranodal extension (iENE) is associated with worse outcomes in HPV-associated oropharyngeal carcinoma (OPC). Key challenges with iENE include the lack of standardized criteria, reliance on radiological expertise, and interreader variability. Objective: To develop an artificial intelligence (AI)-driven pipeline for lymph node segmentation and iENE classification using pretreatment computed tomography (CT) scans, and to evaluate its association with oncologic outcomes in HPV-positive OPC. Design, Setting, and Participants: This was a single-center cohort study conducted at a tertiary oncology center in Montreal, Canada, of adult patients with HPV-positive cN+ OPC treated with up-front (chemo)radiotherapy from January 2009 to January 2020. Participants were followed up until January 2024. Data analysis was performed from March 2024 to April 2025. Exposures: Pretreatment planning CT scans along with lymph node gross tumor volume segmentations performed by expert radiation oncologists were extracted. For lymph node segmentation, an nnU-Net model was developed. For iENE classification, radiomic and deep learning feature extraction methods were compared. Main Outcomes and Measures: iENE classification accuracy was assessed against 2 expert neuroradiologist evaluations using area under the receiver operating characteristic curve (AUC). Subsequently, the association of AI-predicted iENE with oncologic outcomes-ie, overall survival (OS), recurrence-free survival (RFS), distant control (DC), and locoregional control (LRC)-was assessed. Results: Among 397 patients (mean [SD] age, 62.3 [9.1] years; 80 females [20.2%] and 317 males [79.8%]), AI-iENE classification using radiomics achieved an AUC of 0.81. Patients with AI-predicted iENE had worse 3-year OS (83.8% vs 96.8%), RFS (80.7% vs 93.7%), and DC (84.3% vs 97.1%), but similar LRC. AI-iENE had significantly higher Concordance indices than radiologist-assessed iENE for OS (0.64 vs 0.55), RFS (0.67 vs 0.60), and DC (0.79 vs 0.68). In multivariable analysis, AI-iENE remained independently associated with OS (adjusted hazard ratio [aHR], 2.82; 95% CI, 1.21-6.57), RFS (aHR, 4.20; 95% CI, 1.93-9.11), and DC (aHR, 12.33; 95% CI, 4.15-36.67), adjusting for age, tumor category, node category, and number of lymph nodes. Conclusions and Relevance: This single-center cohort study found that an AI-driven pipeline can successfully automate lymph node segmentation and iENE classification from pretreatment CT scans in HPV-associated OPC. Predicted iENE was independently associated with worse oncologic outcomes. External validation is required to assess generalizability and the potential for implementation in institutions without specialized imaging expertise.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".