An Empirical Approach for Avoiding False Discoveries When Applying High-Dimensional Radiomics to Small Datasets
Bibliographic record
Abstract
Purpose: Radiomic studies, where correlations are drawn between patients' medical image features and patient outcomes, often deal with small datasets. Consequently, results can suffer from lack of replicability and stability. This paper establishes a methodology to assess and reduce the impact of statistical fluctuations that may occur in small datasets. Such fluctuations can lead to false discoveries, particularly when applying feature selection or machine learning (ML) methods commonly used in the radiomics literature. Methods: Two feature selection methods were created, one for choosing single predictive features, and another for obtaining features sets that could be combined in a predictive model. The features were combined using ML tools less affected by overfitting (Naïve Bayes, logistic regression, and linear support vector machines). Only three features were allowed to be combined at a time, further limiting overfitting. This methodology was applied to MR images from small datasets in metastatic liver disease (69 samples) and primary uterine adenocarcinoma (93 samples), and the outcomes studied were: desmoplasia (for liver metastases), lymphovascular space invasion (LVSI), cancer staging (FIGO), and tumor grade (for uterine tumors). For outcomes in uterine cancer, the predictive models were tested on independent subsets. Results: With respect to the combined predictive feature approach: for LVSI, a prognostic factor that a human reader cannot detect, the predictive model yielded AUC = 0.87 ± 0.07 and accuracy = 0.84 ± 0.09 in the testing set. For FIGO staging, AUC = 0.81 ± 0.03 and accuracy = 0.79 ± 0.08. For tumor grade, AUC = 0.76 ± 0.05 and accuracy = 0.70 ± 0.08. Conclusion: Despite considering a large set (~104) of texture features, the false discovery avoidance methodology allowed only robust predictive models to be retained. Thus, the stringent false discovery avoidance methods introduced here do not preclude the discovery of promising correlations.
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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 | 0.164 | 0.460 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".