Prognostic Factors for Patients with Proliferative Hepatocellular Carcinoma After Liver Resection

Abstract

Purpose: There is a scarcity of predictive models currently accessible for prognosticating proliferative hepatocellular carcinoma (HCC), an integrated class of subtype, characterized by a dismal prognosis. Consequently, this study aimed to develop and validate a novel prognostic model capable of accurately predicting the prognosis of proliferative HCC after curative resection.

Patients and methods: This retrospective multicenter study included patients with solitary HCC who underwent curative liver resection from August 2014 to December 2020 (n = 816). Patients were stratified into either the proliferative HCC cohort (n = 259) or the nonproliferative HCC cohort (n = 557) based on histological criteria. Disease-free survival (DFS) was compared between the two groups before and after one-to-one propensity score matching (PSM). Of all the proliferative HCC patients, 203 patients were assigned to training cohort, and 56 patients were assigned to validation cohort. Univariate and multivariate analyses were performed in training cohort to identify risk factors associated with worse DFS. Thereafter, a predictive model was constructed, subsequently validated in the validation cohort.

Results: The DFS of proliferative HCC was significantly worse than nonproliferative HCC before and after PSM. Meanwhile, multivariate regression analysis revealed that liver cirrhosis (P = 0.032) and larger tumor size (P = 0.000) were independent risk factors of worse DFS. Lastly, the discriminative abilities of the predictive model for 1, 3, 5-year DFS rates, as determined by receiver operating characteristic (ROC) curves, were 0.702, 0.720, and 0.809 in the training cohort and 0.752, 0.776, and 0.851 in the validation cohort, respectively.

Conclusion: This study developed a predictive model with satisfactory accuracy to predict the worse DFS in proliferative HCCs after liver resection. Moreover, this predictive model may serve as a valuable tool for clinicians to predict postoperative HCC recurrence, thereby enabling them to implement early preventative strategies.

Keywords: curative resection; hepatocellular carcinoma; nonproliferative; predictive model; proliferative.

Figure 1

Study flowchart. Patients with proliferative hepatocellular carcinoma who underwent liver resection in institution (A) (n=203) were included in the training cohort for establishing the predictive model. Patients from institutions (B and C) (n=56) were included in the external validation cohort.

Figure 2

(A) Images showing a case of proliferative hepatocellular carcinoma in the 7th segment of the liver of a 50-year-old man with cirrhosis. The lesion was 41 mm in size, with an oval shape, and without satellite nodules on the CT images. CT image during the arterial phase displayed heterogenous enhancement. A washout appearance was observed in the delayed phase of the CT scan. These observations qualified as Liver Imaging Reporting and Data System category 5. Microscopic examination with hematoxylin and eosin staining confirmed the presence of the macrotrabecular-massive HCC subtype. (B) Images illustrating a case of nonproliferative hepatocellular carcinoma at the junction of the 7th and 8th segments of the liver of a 48-year-old woman without cirrhosis. The lesion was 22 mm in size, with a round shape, and without satellite nodules on the CT images. CT image during the arterial phase CT image showed moderate enhancement, while a washout appearance was noted during the delayed phase. These observations qualified as Liver Imaging Reporting and Data System category 5. Microscopic examination with hematoxylin and eosin staining confirmed the presence of the clear cell HCC subtype.

Figure 3

Disease-free survival curves of proliferative and nonproliferative HCC before (A) and after (B) propensity score matching in the entire cohort.

Figure 4

(A) Nomogram for predicting the probability of disease-free survival at 1, 3, and 5 years. Each predictor corresponds to a specific point by drawing a line straight upward to the points axis. The sum of the points is located on the total points axis, and this sum represents the probability of disease-free survival. (B) ROC curves in the training cohort. (C) ROC curves in the validation cohort.

Figure 5

Disease-free survival curves for the total cohort (A), training cohort (B), and validation cohort (C).

Figure 6

Survival curves of the nonproliferative, high-risk proliferative, and low-risk proliferative hepatocellular carcinoma in the total cohort (A), institution A (B), and institution B+C (C).