Diagnostic Accuracy of Single-Phase Computed Tomography Texture Analysis for Prediction of LI-RADS v2018 Category
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to determine if texture analysis can classify liver observations likely to be hepatocellular carcinoma based on the Liver Imaging Reporting and Data System (LI-RADS) using single portal venous phase computed tomography. METHODS: This research ethics board-approved retrospective cohort study included 64 consecutive LI-RADS observations. Individual observation texture analysis features were compared using Kruskal-Wallis and 2 sample t tests. Logistic regression was used for prediction of LI-RADS group. Diagnostic accuracy was assessed using receiver operating characteristic curves and Youden method. RESULTS: Multiple texture features were associated with LI-RADS including the mean HU (P = 0.003), median (P = 0.002), minimum (P = 0.010), maximum (P = 0.013), standard deviation (P = 0.009), skewness (P = 0.007), and entropy (P < 0.001). On logistic regression, LI-RADS group could be predicted with area under the curve, sensitivity, and specificity of 0.98, 96%, and 100%, respectively. CONCLUSIONS: Texture analysis features on portal venous phase computed tomography can identify liver observations likely to be hepatocellular carcinoma, which may preclude the need to recall some patients for additional multiphase imaging.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".