Zhu Hengli, Cai Chiyu, Li Bingyao, Tang Changqian, Ren Yongnian, Li Deyu
Objective This study aims to develop and validate a novel platelet index score （PIS）-based nomogram to predict the prognosis of hepatocellular carcinoma （HCC）. Methods A retrospective analysis was conducted on the medical records of 692 patients with HCC who underwent surgical resection at the Department of Hepatopancreatobiliary Surgery, Zhengzhou University People’s Hospital, between January 2017 and June 2022. Preoperative laboratory testing, clinicopathological characteristics, and surgery-related data were collected. Postoperative follow-up was performed according to standard protocols, with recurrence-free survival （RFS） as the primary outcome to assess early recurrence and metastasis after HCC resection. Platelet parameters were analyzed using Kaplan-Meier curves in the training cohort to establish the platelet index score. Independent risk factors for postoperative recurrence were identified using univariate and multivariate Cox proportional hazards regression models, and a nomogram was constructed. The predictive performance of the model was evaluated using receiver operating characteristic （ROC） curves and calibration curves in both the training and validation cohorts, aiming to assess the consistency with actual RFS outcomes. Results Kaplan-Meier analysis revealed that lower platelet counts （PLT≤ 157.5× 109/L, P=0.001）, higher mean platelet volume （MPV≥ 11.35 fL, P< 0.001）, and higher platelet distribution width （PDW≥ 13.85 fL, P< 0.001） were associated with shorter RFS in HCC patients. These three platelet indices were integrated into a novel scoring system, namely PIS, which demonstrated a good predictive performance. Based on the PIS, HCC patients were stratified into high- and low-risk groups. The 1-, 2-, and 3-year RFS in the low-risk group was 13.32% （51/383）, 28.20% （108/383）, and 38.90% （149/383）, respectively, which was 23.53% （24/102）, 49.02% （50/102）, and 67.65% （69/102） in the high-risk group, respectively. Multivariate Cox regression analysis identified the American Joint Committee on Cancer （AJCC） staging （HR=2.921, 95%CI： 1.83-4.67, P< 0.001）, microvascular invasion （HR=1.906, 95%CI： 1.28-2.83, P=0.001）, portal vein tumor thrombus （HR=1.408, 95%CI： 1.03-1.92, P=0.031）, tumor satellite lesions （HR=1.388, 95%CI： 1.03-1.88, P=0.033）, Ki-67 expression （HR=1.997, 95%CI： 1.45-2.75, P< 0.001）, alpha-fetoprotein （HR=1.723, 95%CI： 1.29-2.30, P< 0.001）, and PIS （HR=1.442, 95%CI： 1.08-1.92, P=0.013） were independent risk factors for postoperative recurrence. The nomogram was plotted based on independent risk factors. The results showed that the AUC of the nomogram model in the training queue for 1-,2-, and 3-years was 0.826 （95%CI：0.773-0.876）, 0.850 （95% CI： 0.800-0.876）,and 0.909 （95% CI： 0.882-0.934） respectively,and the AUC in the validation queue was 0.826（95% CI： 0.752-0.890）,0.807（95%CI： 0.741-0.863）, and 0.804 （95%CI： 0.737-0.862） respectively.The higher the total score of the nomogram, the better the efficacy of the model in predicting the RFS of patients. Calibration curves showed a good consistency between the predicted and actual RFS in both cohorts. Conclusion The PIS-based nomogram model, incorporating multiple platelet indices, accurately predicts 1-, 2-, and 3-year recurrence status after HCC resection and provides effective postoperative risk stratification for patients with HCC.
