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ΔSII-based nomogram for prognosis prediction after radical resection for hepatocellular carcinoma.

Journal of gastrointestinal oncology 2026 Vol.17(1) p. 22

Zou Y, Jin M, Luo M, He K

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[BACKGROUND] Primary liver cancer (PLC) ranks sixth in global incidence and third in cancer mortality.

🔬 핵심 임상 통계 (초록에서 자동 추출 — 원문 검증 권장)
  • 표본수 (n) 171
  • p-value P<0.001
  • p-value P=0.005
  • HR 2.81

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BibTeX ↓ RIS ↓
APA Zou Y, Jin M, et al. (2026). ΔSII-based nomogram for prognosis prediction after radical resection for hepatocellular carcinoma.. Journal of gastrointestinal oncology, 17(1), 22. https://doi.org/10.21037/jgo-2025-772
MLA Zou Y, et al.. "ΔSII-based nomogram for prognosis prediction after radical resection for hepatocellular carcinoma.." Journal of gastrointestinal oncology, vol. 17, no. 1, 2026, pp. 22.
PMID 41816602

Abstract

[BACKGROUND] Primary liver cancer (PLC) ranks sixth in global incidence and third in cancer mortality. Chronic inflammation drives hepatocarcinogenesis via fibrosis. Despite curative resection, hepatocellular carcinoma (HCC) patients have high recurrence rates (50% at 3 years and 70% at 5 years). In addition to pathological factors [e.g., an alpha-fetoprotein (AFP) concentration >400 ng/L and vascular invasion], systemic immune inflammation influences the prognosis. The systemic immune-inflammation index (SII) has prognostic value, but the preoperative SII does not account for surgical impact. We propose a novel postoperative/preoperative SII ratio (ΔSII) to predict survival and recurrence, integrated into a ΔSII-based prognostic nomogram.

[METHODS] We retrospectively analyzed 244 HCC patients undergoing radical resection, randomly divided into training (n=171) and validation (n=73) cohorts at a 7:3 ratio. The optimal ΔSII cutoff for overall survival (OS) was determined by receiver operating characteristic (ROC) curve analysis. Clinicopathologic associations were assessed with Chi-squared/Fisher tests. Variable selection involved univariable Cox, least absolute shrinkage and selection operator (LASSO), and Boruta algorithms; multivariable Cox models were used to construct OS/recurrence-free survival (RFS) nomograms. Performance was evaluated via time-dependent area under the curve (AUC), calibration curves, and decision curve analysis (DCA).

[RESULTS] The ΔSII outperformed preoperative SII in predicting OS (AUC: 0.770 0.593, P<0.001). High-ΔSII patients (n=59) had higher rates of multifocal tumors (20.3% 6.3%, P=0.005), major resections (42.4% 17.0%, P<0.001), and transfusions (18.6% 9.8%, P=0.04) compared to low-ΔSII patients (n=112). Survival was superior in the low-ΔSII group, with median OS 58 23 months [hazard ratio (HR) =3.71, P<0.001] and median RFS 39 16 months (HR =2.81, P<0.001). Multivariable analysis confirmed high ΔSII (HR =3.71), AFP ≥400 ng/mL (HR =1.79), and major resection (HR =2.45) as independent risk factors for OS; hepatitis B virus (HBV) positivity (HR =2.03) was an additional risk factor for RFS. The nomogram showed AUCs of 0.776/0.894 (1-year OS) and 0.848/0.653 (1-/3-year RFS), with good calibration (Brier score 0.11-0.19) and clinical utility.

[CONCLUSIONS] The ΔSII-based nomogram effectively predicts OS/RFS after resection, enabling individualized management for high-risk HCC patients.

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