Arterial enhancement on triphasic computed tomography scan predicts response of colorectal liver metastases to chemoembolization: A case–control study
Bibliographic record
Abstract
Objectives: The arterial enhancement fraction (AEF), a simple calculation based on a standard triple-phase computed tomography (CT) scan, has been shown to predict treatment response in radioembolization of colorectal liver metastases (CRLM). The current study aims to determine if arterial enhancement also predicts treatment response in transarterial chemoembolization (TACE) of CRLM, which uses a larger particle size and exerts an ischemic effect. Materials and Methods: A retrospective analysis of our experience with TACE for CRLM between 2013 and 2022 yielded 97 TACE treatments for CRLM. The study included the first TACE treatment patients having a triple-phase CT scan before and after TACE, yielding 62 tumors treated with TACE of irinotecan drug-eluting beads in 36 patients. Tumors with complete response or partial response based on CT-based modified RECIST criteria were considered to be “responders,” whereas tumors that had progressive disease or stable disease were considered to be “non-responders.” Results: The responders differed from the non-responders in terms of arterial phase enhancement (APhE) (9.5 [interquartile range, IQR 6, 17] vs. 2 [IQR 1, 5] Hounsfield units [HUs], P < 0.001) and AEF (0.7 [IQR 0.5, 1] vs. 0.3 [IQR 0.1, 1], P = 0.01), both validated measures of hepatic arterial perfusion. Receiver operating characteristic curve analysis yielded a 5.5 HU cutoff for APhE. Those tumors with APhE >5.5 HU had a response rate of 72%, whereas those <5.5 HU had a response rate of 21%. Median overall survival for patients with tumors having APhE >5.5 HU was 22.4 months (IQR 13, 32) versus 14.5 months (IQR 10, 19) for those with APhE ≤5.5 HU, but this did not achieve statistical significance (P = 0.14). Conclusion: CRLM with greater hepatic arterial blood supply as measured by the APhE and AEF have a higher probability of TACE treatment response.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".