Comparison of Radiomics Models Built Through Machine Learning in a Multicentric Context With Independent Testing: Identical Data, Similar Algorithms, Different Methodologies
Bibliographic record
Abstract
Machine learning techniques are becoming increasingly popular in radiomics studies. They can handle high dimensional sets of radiomics features with higher robustness than usual statistical analyses, by capturing complex interactions between features themselves and between feature combinations and clinical endpoints under investigation in order to build efficient prognostic/predictive models. However, there is no “one fits all” solution and deciding which algorithm is the most accurate for a given application is not always straightforward. In this paper, to keep a realistic perspective on various emerging clinical applications based on radiomics, we performed an evaluation of the popular random forest classifier for predicting local failure in cervix cancer exploiting identical data, but relying on different methodologies to select and combine features of interest. The main objective was to demonstrate various challenges of model building and tuning for radiomics applications. The results obtained in the present work could provide general guidelines to assist in the practical development of radiomics-based 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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".