Predicting Tumour Response With Radiomics and Machine Learning in MR-Guided Cervix Brachytherapy
Bibliographic record
Abstract
This study seeks to determine if radiomic features extracted from whole or part of the gross tumour volume of locally advanced cervical cancer (LACC) patients can be used to predict tumour response prior to brachytherapy treatment. 12 machine learning algorithms were tested with 5-fold cross validation using 1183 radiomic features extracted from 20 patients from T1, T2 and diffusion-weighted MR images. Recursive Feature Elimination was used to indicate the most predictive radiomic features of the most accurate models. Several models, particularly Ensemble Methods, performed with accuracies of up to 85%. After combining the 11 most predictive features into a single dataset, a random forest model achieved an accuracy of 93%. Overall, this study showed that machine learning models coupled with radiomic features are capable of accurately predicting LACC tumour response prior to administering the first fraction of brachytherapy treatment.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| 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 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".