Non-invasive liver fibrosis screening on CT images using radiomics
Bibliographic record
Abstract
PURPOSE: To develop a radiomics machine learning model for detecting liver fibrosis on CT images of the liver. METHODS: With Ethics Board approval, 169 patients (68 women, 101 men; mean age, 51.2 years ± 14.7 [SD]) underwent an ultrasound-guided liver biopsy with simultaneous CT acquisitions without and following intravenous contrast material administration. Radiomic features were extracted from two regions of interest (ROIs) on the CT images, one placed at the biopsy site and another distant from the biopsy site. A development cohort, which was split further into training and validation cohorts across 100 trials, was used to determine the optimal combinations of contrast, normalization, machine learning model, and radiomic features for liver fibrosis detection based on their Area Under the Receiver Operating Characteristic curve (AUC) on the validation cohort. The optimal combinations were then used to develop one final liver fibrosis model which was evaluated on a test cohort. RESULTS: When averaging the AUC across all combinations, non-contrast enhanced (NC) CT (AUC, 0.6100; 95% CI: 0.5897, 0.6303) outperformed contrast-enhanced CT (AUC, 0.5680; 95% CI: 0.5471, 0.5890). The most effective model was found to be a logistic regression model with input features of maximum, energy, kurtosis, skewness, and small area high gray level emphasis extracted from non-contrast enhanced NC CT normalized using Gamma correction with γ = 1.5 (AUC, 0.7833; 95% CI: 0.7821, 0.7845). CONCLUSIONS: The presented radiomics-based logistic regression model holds promise as a non-invasive detection tool for subclinical, asymptomatic liver fibrosis. The model may serve as an opportunistic liver fibrosis screening tool when operated in the background during routine CT examinations covering liver parenchyma. The final liver fibrosis detection model is made publicly available at: https://github.com/IMICSLab/RadiomicsLiverFibrosisDetection .
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".