ANT Score Nomogram for Predicting Very Early Recurrence of Hepatocellular Carcinoma
Bibliographic record
Abstract
Zichen Yu,1,2 Hanyu Wang,1,3 Qiang Huo,4 Wenli Cao,1,3 Liming Jin,1 Jie Liu,1 Fangqiang Wei1 1Department of General Surgery, Cancer Center, Division of Hepatobiliary and Pancreatic Surgery, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, Zhejiang Province, 310014, People’s Republic of China; 2Department of Postgraduate Training Base Alliance of Wenzhou Medical University, Wenzhou, Zhejiang Province, People’s Republic of China; 3Department of Public Health, Hangzhou Medical College, Hangzhou, Zhejiang Province, 310059, People’s Republic of China; 4Department of General Surgery, Zhoushan Dinghai Central Hospital, Dinghai District of Zhejiang Provincial People’s Hospital, Zhoushan, Zhejiang Province, 316000, People’s Republic of ChinaCorrespondence: Fangqiang Wei, Department of General Surgery, Cancer center, Division of Hepatobiliary and Pancreatic Surgery, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, Zhejiang Province, 310014, People’s Republic of China, Email wdfwfq@126.comBackground: Very early recurrence (VER), defined as recurrence within one year after curative resection of hepatocellular carcinoma (HCC), significantly impacts long-term survival. This study aimed to develop and validate the ANT Score, a novel prognostic model integrating nutrition, inflammation, and tumor burden to refine VER prediction.Methods: A retrospective cohort of HCC patients undergoing curative liver resection was analyzed. Key predictors were identified using least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression, forming the ANT Score. Model performance was evaluated through receiver operating characteristic (ROC) curve analysis, DeLong’s test, calibration curves, and decision curve analysis (DCA). The prognostic value of capsule integrity was also assessed.Results: Among 459 included patients, 118 (25.7%) experienced VER. Patients were randomly assigned to training (70%) and test (30%) cohorts. The ANT Score, comprising albumin-to-alkaline phosphatase ratio (AAPR), neutrophil-to-albumin ratio (NPAR), and tumor burden score (TBS), demonstrated superior predictive performance (area under the curve [AUC] = 0.751, 95% confidence interval: 0.669– 0.832, P < 0.05) compared to conventional markers. Capsule incompleteness was an independent risk factor but did not significantly enhance predictive accuracy (AUC = 0.76 vs 0.751, P > 0.05, DeLong test). The ANT Score-based nomogram exhibited excellent calibration and clinical utility in DCA.Conclusion: The ANT Score is an independent and superior predictor of VER after curative HCC resection. The ANT Score-based nomogram showed promising predictive value, offering a practical tool for individualized risk assessment. External validation and prospective studies are warranted to further assess its clinical applicability.Keywords: hepatocellular carcinoma, very early recurrence, ANT score, curative resection, model
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".