Dual Modality Radiation With External Beam Radiation Therapy and Transarterial Radioembolization for Hepatocellular Carcinoma With Gross Vascular Invasion
Bibliographic record
Abstract
OBJECTIVES: Patients with hepatocellular carcinoma (HCC) and gross vascular invasion (GVI) have poor outcomes with systemic therapy such as sorafenib. Both external beam radiation therapy (EBRT) and transarterial radioembolization (TARE) have been utilized for this patient population. We sought to compare outcomes using dual modality radiation (EBRT+TARE) versus EBRT alone in patients with HCC and GVI. MATERIALS AND METHODS: Between 2011 and 2017, 45 patients with HCC and GVI were treated with EBRT±TARE at our institution. Progression-free survival (PFS) and overall survival (OS) were assessed and compared using Kaplan-Meier method and log-rank test. Univariable and multivariable Cox proportional hazards regression was used to assess the impact of the variables stage, etiology of cirrhosis, Child-Pugh (CP) score, and Karnofsky Performance Score (KPS) on PFS and OS. RESULTS: Patient characteristics were well-balanced except for KPS (80 vs. 90) and CP score. Median OS for patients receiving EBRT+TARE was 263 days (95% confidence interval [CI]: 167, -) versus 193 days (95% CI: 51, 262) for EBRT alone (P=0.049). However, this did not hold up on MVA. When EBRT and TARE were delivered within 2 months as planned (n=12), median PFS was 218 days (95% CI: 44, -) for dual modality radiation versus 63 days (95% CI: 38, 137) for EBRT alone (P=0.048). When EBRT and TARE were delivered within 6 months, the difference in PFS was no longer seen (P=NS), because some patients received TARE as a salvage therapy. CONCLUSIONS: Dual modality radiation with EBRT and TARE may be associated with improved OS in patients with HCC and GVI. Dual modality radiation may be associated with improved PFS in patients with HCC and GVI compared with EBRT alone when EBRT and TARE are delivered within 2 months of each other as part of a planned dual modality treatment strategy. However, since this is a retrospective study with inherent selection bias, these findings need further validation in a prospective clinical trial for patients with HCC and GVI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".