CT-based deep learning prediction of complete response in intermediate-stage hepatocellular carcinoma treated with drug-eluting beads transarterial chemoembolization
Bibliographic record
Abstract
Objectives: To develop a CT-based deep learning (DL) model to predict complete response (CR) to drug-eluting beads-transarterial chemoembolization (TACE) in patients with naive Barcelona Clinic Liver Cancer (BCLC) B hepatocellular carcinoma (HCC). Methods: This dual-centre retrospective study included 93 patients with BCLC B HCC treated with drug-eluting beads-TACE (median size of 40 mm and 37 mm at Institutions 1 and 2). Complete response was defined as per modified Response Evaluation Criteria in Solid Tumours on liver contrast-enhanced CT within 2 months of treatment. A twin-network DL model with spatio-temporal Video Vision Transformer (ViViT) architecture was developed to predict CR using baseline dedicated liver CT. The model was extensively trained/tested based on an 8-fold cross-validation approach with an ensemble technique, a model vote system where the outcome is the average of multiple model predictions. Results: The CR rate was 36% (18/50) and 22% (11/49) at Institutions 1 and 2. The model showed high specificity and AUC, as well as moderate sensitivity and balanced accuracy when using either the late arterial phase (0.91 ± 0.12, 0.86 ± 0.16, 0.43 ± 0.23, and 0.67 ± 0.13, respectively) or the portal venous phase (0.90 ± 0.15, 0.85 ± 0.17, 0.57 ± 0.30, and 0.74 ± 0.16, respectively). Conclusion: The developed CT-based DL model predicted CR to drug-eluting beads-TACE in patients with naive BCLC B HCC with high specificity. It should be refined to improve sensitivity. Advances in knowledge: The study underscores the potential of artificial intelligence in precision medicine in patients with HCC. While the model shows promise, further research with larger datasets and prospective studies is needed to enhance its predictive power and clinical applicability.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".