Worse is better? Performance and bias implications of feature selection in radiomics-based survival analysis
Bibliographic record
Abstract
We present an extension of the minimum redundancy, maximum relevance (mRMR) feature selection algorithm to accommodate right-censored survival outcomes. We use a set of 197 preoperative CT scans from patients that underwent hepatic resection to treat colorectal liver metastases, with the intention of predicting overall survival based on radiomic analysis of the largest metastasis. Using selection of the K-best features as a baseline, mRMR is introduced as a correction of the ranking, balancing relevance with the correlation of each candidate feature with those previously selected. Harrell’s C-index is introduced as a replacement for the F-statistic to accommodate right-censoring. Two pre-processing operations are considered: first, features of low univariate significance are removed; and second, features with low reproducibility in an independent data set are removed. Each algorithm was tested in a repeated 10-fold cross-validation of a Cox proportional hazards model of overall survival. We found that use of mRMR was associated with a decrease in performance C-index of -0.016. While mRMR models had lower bias, univariate significance thresholding increased the bias of mRMR models by 0.02, producing the models with highest bias. More stable feature selection methods were associated with reduced bias and better overall performance. The baseline K-best method, with no accounting for redundancy or censoring, and no pre-processing, obtained the best performance (C-index=0.600 (0.591–0.607)) and a bias of 0.000 (-0.001– 0.004).
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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 | 0.030 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".