Adjusted Tumor Enhancement on Dual-Phase Cone-Beam CT: Predictor of Response and Overall Survival in Patients with Liver Malignancies Treated with Hepatic Artery Embolization
Bibliographic record
Abstract
The aim of this study was to examine the value of tumor enhancement parameters on dual-phase cone-beam CT (CBCT) in predicting initial response, local progression-free survival (L-PFS) and overall survival (OS) following hepatic artery embolization (HAE). Between Feb 2016 and Feb 2023, 13 patients with 29 hepatic tumors treated with HAE were analyzed. Pre- and post-embolization, subtracted CBCTs were performed, and tumor enhancement parameters were measured, resulting in three parameters: pre-embolization Adjusted Tumor Enhancement (pre-ATE), post-embolization ATE and the difference between pre- and post-ATE (∆ATE). Treatment response was evaluated using the mRECIST criteria at 1 month. Tumors were grouped into complete response (CR) and non-complete response (non-CR) groups. To account for the effect of multiple lesions per patient, a cluster data analytic method was employed. The Kaplan–Meier method was utilized for survival analysis using the lesion with the lowest ∆ATE value in each patient. Seventeen (59%) tumors showed CR and twelve (41%) showed non-CR. Pre-ATE was 38.5 ± 10.6% in the CR group and 30.4 ± 11.0% in the non-CR group (p = 0.023). ∆ATE in the CR group was 39 ± 12 percentage points following embolization, compared with 29 ± 11 in the non-CR group (p = 0.009). Patients with ∆ATE > 33 had a median L-PFS of 13.1 months compared to 5.7 in patients with ∆ATE ≤ 33 (95% CI = 0.038–0.21) (HR, 95% CI = 0.45, 0.20–0.9, p = 0.04). Patients with ∆ATE ≤ 33 had a median OS of 19.7 months (95% CI = 3.77–19.8), while in the ∆ATE > 33 group, median OS was not reached (95% CI = 20.3-NA) (HR, 95% CI = 0.15, 0.018–1.38, p = 0.04). CBCT-derived ATE parameters can predict treatment response, L-PFS and OS following HAE.
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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 | 0.000 | 0.001 |
| 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".