Planned Liver Stereotactic Body Radiotherapy for Residual Colorectal Cancer Liver Metastases After Surgery: A Single-Arm Retrospective Study
Bibliographic record
Abstract
Given the promising outcomes of stereotactic body radiation therapy (SBRT) in treating colorectal cancer liver metastases (CRLMs), we proposed an innovative strategy combining surgery with planned liver SBRT for CRLMs. This retrospective study included patients who underwent curative-intent surgery combined with planned liver SBRT from July 2019 to October 2023. Planned liver SBRT was delivered to residual unresectable and unablatable lesions with maximum diameters of ≤5 cm. Outcomes included local failure (LF), intrahepatic recurrence-free survival (IHRFS), extrahepatic recurrence-free survival (EHRFS), progression-free survival (PFS), overall survival (OS), and radiation-related adverse events. A total of 69 patients were included. The 1-, and 2-year cumulative incidence rates of LF after SBRT were 7.7%, and 9.6%, respectively. The median PFS was 6.2 months, and the median OS was 45.8 months. Multivariate analysis identified RAS/BRAF mutations, extrahepatic metastases excluding lung involvement, and higher CEA as independent predictors of poorer OS. Intrahepatic recurrence was the predominant pattern of first disease progression after combination treatment. Acute grade 1-2 radiation-related adverse events occurred in 56.5% of patients, while grade 3 toxicities were reported in 4.3%. This approach offers favorable long-term outcomes, suggesting its potential to broaden the indications for curative-intent local treatments in CRLMs.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".