Limited utility of routine bone scintigraphy in the staging of patients with hepatocellular carcinoma: A cross-sectional study
Bibliographic record
Abstract
INTRODUCTION AND OBJECTIVES: The most widely used staging system for hepatocellular carcinoma (HCC) is the Barcelona Liver Clinic Cancer (BCLC) system, which considers tumor burden, performance status, and liver function. Tumor burden is assessed with cross sectional imaging of the abdomen and chest, controversy surrounds the routine use of bone scintigraphy (BS) for detecting extrahepatic metastases. This study evaluated the role of BS in staging HCC in Mexican patients. PATIENTS AND METHODS: Retrospective cross-sectional study of all adults with HCC at a Mexican referral center from 2000 to 2018. Staging included abdominal computed tomography (CT) or magnetic resonance imaging, chest CT, and BS. The main outcome was the impact of BS on staging and/or therapy plans. RESULTS: Among 238 patients, 2 with fibrolamellar variant and 44 with incomplete data were excluded. Median age was 66 years, 84 % had cirrhosis, and the predominant etiology was hepatitis C virus (43 %). BCLC stages were distributed as follows: A (14 %), B (7 %), C (68 %), and D (11 %). Extrahepatic disease was present in 18 %; only 8 % patients had a positive BS. Among the positive cases, 4 were true positives, but they did not alter staging or therapy plans. CONCLUSIONS: Routine BS in HCC staging demonstrated low yield, with a notable rate of false positives. Considering the implications of extrahepatic disease, BS may be justified for liver transplant candidates outside conventional criteria. Our study highlights the limited role of BS in early-stage HCC and advocates for a more selective utilization.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".