Predicting Head and Neck Cancer Treatment Outcomes using Textural Feature Level Fusion of Quantitative Ultrasound Spectroscopic and Computed Tomography: A Machine Learning Approach
Bibliographic record
Abstract
Predicting therapy response of Head & Neck (H&N) cancers prior to therapy initiation can be effective to increase the probability of pathologic complete response (pCR) and clinical response. Quantitative ultrasound spectroscopy (QUS) and treatment planning computed tomography (CT) are utilized to evaluate therapy response. Although analysis of cancerous cells on ultrasound (US) and CT images is not feasible due to their sub-resolution sizes, radiomics features can be extracted from these images to measure changes in biological conditions and cells’ micro-structures. Combination of CT and QUS data at feature level can generate features that are more informative. To this end, we proposed a technique to fuse radiomics feature of CT and QUS in order to generate more discriminative features. A fusion of radiomics features from CT and QUS was achieved using an autoencoder, followed by the application of an SVM classifier to distinguish between complete responders (CR) and partial responders (PR) among patients with H&N cancer. The proposed method could achieve to accuracy=71%, F1-score=69% and AUC=70% to classify H&N patient with responses CR and PR.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".