Metabolic Syndrome–Related Hepatocellular Carcinoma Treated by Volumetric Modulated Arc Therapy
Bibliographic record
Abstract
Hepatocellular carcinoma (hcc) is a leading cause of cancer mortality, and its incidence is increasing in developed countries. Risk factors include cirrhosis from viral hepatitis or alcohol abuse. Metabolic syndrome is a newly recognized, but important, risk factor that is likely contributing to the increased incidence of hcc. Surgery is the therapy of choice for hcc, but local therapies are often contraindicated, usually because of advanced disease or comorbid conditions such as cardiac disease (which is associated with metabolic syndrome). Current radiation therapy techniques such as stereotactic body radiotherapy allow for treatment plans that highly conform to the target and provide excellent sparing of normal structures. Radiation therapy is emerging as a viable option in patients not eligible for surgery or other locoregional therapies. Here, we report a case of a large hcc presenting in a patient with metabolic syndrome without significant alcohol history or biochemical liver dysfunction. The patient was not a candidate for locoregional therapies because of cardiac and renal comorbidities typical of patients experiencing the long-term sequelae of metabolic syndrome. Treatment using an arc-based volumetric-modulated arc therapy technique allowed for the highest dose of radiation to be delivered to the tumour while the peripheral radiation dose was minimized. A complete local response was confirmed by computed tomography imaging 21 months after treatment completion.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".