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Predictive Value of Platelet-Related Measures in Patients with Hepatocellular Carcinoma.

Zhijia FanMengmeng WuZihui TangAnfang HeFuchen LiuWei LiangZhicheng WangDongqin Yang
Published in: Technology in cancer research & treatment (2022)
Background: Increasing numbers of studies reported platelet (PLT)- related measures could play a creative role in many malignancies, while the prognostic impact of these measures in hepatocellular carcinoma (HCC) remains limited and controversial. It is worth exploring the predictive value of PLT-related measures in HCC. Methods: A total of 279 HCC patients with hepatectomy were analyzed in the retrospective cohort study. The optimal cut-off points of these PLT-related indices were obtained by the receiver operating characteristic (ROC) curve. The associations of these indices with clinical characteristics and overall survival (OS) were evaluated by Kaplan-Meier curves and Cox proportional hazards models. Results: High PLT count and low prognostic nutritional index (low-PNI) were significantly associated with larger tumor size. The low gamma-glutamyl transpeptidase-to-platelet ratio (low-GPR) group was inclined to more hepatitis infections. Survival curves indicated that preoperative high-PLT, low-GPR, and low-PNI had a worse prognosis after surgery in the cohort. In addition, PLT≥220 × 10 9 /L (HR, 2.274; 95% CI, 1.061-4.876; P  = .035), PNI≥51.9 (HR, 0.503; 95% CI, 0.265-0.954; P  = .035), and GPR≥0.2 (HR, 0.432; 95% CI, 0.204-0.912; P  = .028) were identified as independent prognostic factors for survival outcomes in the multivariable analysis. Conclusion: High-PLT, low-GPR, and low-PNI as the preoperative predictors were associated with poor OS in HCC patients with hepatectomy. Our data reveal that they could be simple, easily obtained, and effective predictors for evaluation of survival outcomes in patients.
Keyphrases
  • prognostic factors
  • fatty acid
  • patients undergoing
  • end stage renal disease
  • ejection fraction
  • chronic kidney disease
  • newly diagnosed
  • machine learning
  • dna methylation
  • deep learning
  • peripheral blood
  • patient reported