Prognostic Value of the PET/CT-Derived Maximum Standardized Uptake Value Combined with the Neutrophil–Lymphocyte Ratio in Patients with Hepatocellular Carcinoma Undergoing Hepatectomy
Bibliographic record
Abstract
Background: We aimed to evaluate ability of a novel scoring system that combines fluorodeoxyglucose-uptake parameters and systemic inflammatory response indicators to predict hepatocellular carcinoma (HCC) prognosis. Methods: Clinical data were collected from patients with HCC who underwent hepatectomy at our hospital in 2014–2022. The tumor-to-liver ratio (TLR) was adopted as a positron emission tomography/computed tomography (PET/CT) standardized uptake value (SUV)-related indicator and calculated as the ratio of the SUVmax of tumor tissue to the SUVmean of normal liver tissue. The patients’ immune microenvironment reflected the NLR. Postoperative overall survival (OS)- and disease-free survival (DFS)-related independent prognostic factors were analyzed using Cox proportional hazards regression modeling. Results: Eighty-nine patients were included. TLR, NLR, and alpha-fetoprotein levels were independently associated with OS and DFS. The OS and DFS in the zero-point group were significantly longer than those in the one- and two-point groups. Time-dependent ROC curve analyses revealed area under the curve values of 0.830 and 0.752 for 5-year OS and DFS, respectively, for the scoring system, outperforming single evaluation indices. Conclusions: The proposed scoring system, which incorporates both TLR and NLR, simultaneously reflects metabolic tumor characteristics and the host’s immune microenvironment, enabling more accurate patients with early to intermediate-stage HCC undergoing hepatectomy classification and better prognostic evaluation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".