Vascular Normalization Caused by Short-Term Lenvatinib Could Enhance Transarterial Chemoembolization in Hepatocellular Carcinoma
Bibliographic record
Abstract
We describe the clinical effects of short-term lenvatinib administration prior to conventional transarterial chemoembolization (cTACE) on tumor vasculature. Two patients with unresectable hepatocellular carcinoma underwent high-resolution digital subtraction angiography (DSA) and perfusion four-dimensional computed tomography during hepatic arteriography (4D-CTHA) before and after administration of lenvatinib treatment. The doses and periods of lenvatinib administration were, respectively, 12 mg/day for 7 days and 8 mg/day for 4 days. In both cases, high-resolution DSA revealed a decrease in dilatation and tortuosity of the tumor vessels. Furthermore, the tumor staining became more refined, and newly formed tiny tumor vessels were observed. Perfusion 4D-CTHA revealed a decrease in arterial blood flow to the tumor by 28.6% (from 487.9 to 139.5 mL/min/100 mg) and 42.5% (from 288.2 to 122.6 mL/min/100 mg) in the two cases, respectively. The cTACE procedure resulted in good lipiodol accumulation and complete response. Patients have remained recurrence-free for 12 and 11 months after the cTACE procedure, respectively. The administration of short-term lenvatinib in these two cases resulted in the normalization of tumor vessels, which likely led to improved lipiodol accumulation and a favorable antitumor effect.
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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 | 0.000 |
| 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".