Trends in Selective Internal Radiation Therapy (SIRT) for Treating Hepatocellular Carcinoma, Cholangiocarcinoma, and Liver Metastasis: A Total Population Analysis from 2006 to 2021 in Germany
Bibliographic record
Abstract
The aim of this study was to investigate trends in selective internal radiation therapy (SIRT) for hepatocellular carcinoma (HCC), cholangiocarcinoma (CCC), and liver metastasis in Germany. We analyzed the nationwide German hospital billing database from 2006 to 2019 for the diagnosis of HCC, CCC or liver metastasis in combination with SIRT. For analyses of SIRT on the hospital level, we used the reimbursement.INFO tool based on German hospitals’ quality reports from 2008 to 2021. Linear regression analysis was performed to detect changes over time. We included a total of 14,165 SIRT procedures. The annual numbers increased from 99 in 2006 to 1605 in 2015 (p < 0.001; increase by 1521%), decreasing to 1175 cases in 2019 (p < 0.001). In 2008, 6 of 21 hospitals (28.6%) performed more than 20 SIRTs per year, which increased to 19 of 53 (35.8%) in 2021. The share of SIRT for HCC increased from 29.8% in 2006 to 44.7% in 2019 (p < 0.001) and for CCC from 0% in 2006 to 9.5% in 2019 (p < 0.001), while the share of SIRT for liver metastasis decreased from 70.2% in 2006 to 45.7% in 2019 (p < 0.001). In-hospital mortality was 0.2% after the SIRT procedure. Gastritis (2.7%), liver failure (0.4%), and sepsis (0.3%) were the most common in-hospital complications reported. We observed an increase in SIRT procedures in Germany, with the number of hospitals offering the procedure going up from 21 in 2008 to 53 in 2021. While the treatment of liver metastasis remains the most common indication, SIRT for HCC and CCC increased significantly over the last few years. The mortality and complication rates show that SIRT is a relatively safe procedure.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".