Deep Texture Analysis Enhanced MRI Radiomics for Predicting Head and Neck Cancer Treatment Outcomes with Machine Learning Classifiers
Bibliographic record
Abstract
Background: Head and neck cancer treatment does not yield desired outcomes for all patients. This investigation aimed to explore the feasibility of predicting treatment outcomes from routine pre-treatment magnetic resonance images (MRIs). Radiomics features were “mined” and used to train machine learning (ML) classifiers to predict treatment outcomes. Moreover, iterative deep texture analysis (DTA) was explored to boost model performances. Methods: Radiomics features were determined from T1-weighted post-contrast MRIs of pathologically involved lymph node (LN) segmentations for n = 63 patients. SVM, k-NN, and FLD classifier models were trained, selecting for 1–10 features. The model with the top balanced accuracy was chosen for an iteration of DTA. New feature sets were used to retrain and test the ML. Radiomics features were explored for a total of three layers through two iterations of DTA. Results: Models proved useful in predicting treatment outcomes. The best model was a nine-feature multivariable k-NN model with a sensitivity (%Sn) of 93%, specificity (%Sp) of 74%, 86% accuracy (%Acc), and 86% precision (%Per). The best model for two of the three classifiers (k-NN and FLD) was trained using features from three layers. The performance of the average k-NN and FLD models trained with features was boosted significantly with the inclusion of deeper-layer features. Conclusions: Pre-treatment LN MRIs contain quantifiable texture information that can be used to train ML models to predict cancer treatment outcomes. Furthermore, DTA proved useful to boosting predictive models.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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 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".