Background: Hepatocellular carcinoma (HCC) is a common and biologically aggressive malignancy linked to cirrhotic and pre-cirrhotic changes in the liver. We analyzed degrees of fibrosis in affected patients as indices of survival, to establish an effective prognostic nomogram.
Methods: Eligible patients with HCC and hepatic fibrosis, of varying degrees, were selected from the Surveillance, Epidemiology, and End Results (SEER) database for propensity score matching (PSM). The prognostic value of data was determined using Kaplan-Meier and Cox proportional hazards model. A nomogram based on variables derived from multivariate analyses was established and subjected to internal validation. Its predictive accuracy was tested by concordance index (C-index) and calibration plots. Results: In this propensity score-matched cohort, advanced fibrosis/cirrhosis (vs. none-to-moderate fibrosis) correlated with poorer survival [hazard ratio (HR): 1.131, 95% confidence interval (CI): 1.032-1.240; P=0.009].Multivariate analysis identified the following as independent risk factors for HCC: age >63 years, higher fibrosis score, American Joint Cancer Committee (AJCC) stages T3-4, distant metastasis (M1), tumor size >1 cm, major vascular invasion, and elevated alpha-fetoprotein (AFP) level. A nomogram that integrated these factors offered a superior prognostic prediction for HCC patients (C-index: 0.749, 95% CI: 0.7485-0.7495) relative to conventional tumor staging the AJCC tumor-node-metastasis (TNM) staging system (0.730). In calibration plots, optimal agreement between nomogram-predicted and observed survival was evident.Conclusions: Increased fibrosis was an independent risk factor for survival of HCC patients. A prognostic nomogram integrating fibrosis score and other independent risk factors offered more accurate depictions in this regard.
A “single site but two local environments” phenomenon in Sr2.99−xEu0.01La1+xP3−xSixO12 phosphors yields an applicable Sr of ≥1% K−1 in 303–530 K and a repeatability capability of over 99% for ratiometric temperature sensing applications.
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