The Efficacy and Safety of Hepatic Artery Infusion Chemotherapy Combined with Lenvatinib and Programmed Death (PD)-1 Inhibitors for Unresectable Intrahepatic Cholangiocarcinoma: A Retrospective Study
Bibliographic record
Abstract
Objectives: Although systemic chemotherapy (SC) is the mainstay for treating unresectable intrahepatic cholangiocarcinoma (ICC), its efficacy is limited and it causes severe systemic side effects. This study focuses on evaluating the effectiveness and safety of hepatic arterial infusion chemotherapy (HAIC) in combination with lenvatinib plus programmed death-1 (PD-1) inhibitors (HLP), compared to SC in combination with lenvatinib plus PD-1 inhibitors (SCLP) for unresectable ICC. Methods: We analyzed patients initially diagnosed with unresectable ICC at our center between March 2021 and December 2023, classifying them into HLP and SCLP groups according to treatment regimen. This study assessed and compared overall survival (OS), progression-free survival (PFS), tumor response, and safety outcomes across the two treatment groups. Results: This study enrolled 53 subjects in total; 25 were treated with HLP and 28 with SCLP. The two groups showed well-matched baseline characteristics. The HLP group reported an extended median OS (12.8 vs. 11.0 months, p = 0.310) and a prolonged median PFS (8.8 vs. 6.4 months, p = 0.043), compared to the SCLP group. The HLP group had a better objective response rate (ORR) (52% vs. 25%, p = 0.043) and disease control rate (DCR) (96% vs. 78.6%, p = 0.104). Based on OS (p = 0.019) and PFS (p = 0.032) results, those without extrahepatic metastasis seemed to benefit more significantly from the HLP regimen than from the SCLP regimen. The HLP group experienced fewer grade 3–4 adverse events (AEs) than the SCLP group. Conclusions: The HLP regimen for unresectable ICC is an effective and safe strategy and is potentially better suited for patients without extrahepatic metastases.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".