Vocal Cord Dysfunction in Nonlaryngeal Head and Neck Cancer After Chemoradiation Therapy: Predictive Modeling Using CT Radiomics and Machine Learning
Bibliographic record
Abstract
Introduction: This study aims to investigate computed tomography (CT) radiomic features and dosimetric-clinical biomarkers to predict vocal cord dysfunction (VCD) in nonlaryngeal head and neck cancer (HNC) patients treated with chemoradiation therapy (CRT), using machine learning (ML) models. Methods: Sixty-five HNC patients who underwent CRT were recruited to assess radiation-induced VCD 6 months posttreatment. For each patient, CT radiomic features of the laryngeal region, clinical, and dose-volume histogram (DVH) metrics were collected to develop ML models. Nine classifiers were trained using selected features obtained from three feature selection algorithms: least absolute shrinkage and selection operator (LASSO), extra trees, and elastic net. The models were built using imaging features alone (radiomics model) and in combination with clinical and dosimetric features (combined model). Model performance was evaluated using the area under the receiver operating characteristic curve (AUC-ROC). Results: Of the 65 patients, 31 developed VCD. Among radiomics models, the AdaBoost and random forest (RF) classifiers performed best, with AUCs of 0.74 and 0.84, respectively. For the combined models, nine classifiers achieved an AUC greater than 0.95 using LASSO and elastic net algorithms. In contrast, only one classifier surpassed an AUC of 0.95 when using the extra trees algorithm. Conclusion: Our findings demonstrate that pretreatment CT radiomic features are predictive biomarkers for radiation-induced toxicities, including VCD. Furthermore, combining radiomic features with clinical and dosimetric data can improve the predictive modeling of radiotherapy outcomes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".