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Nomograms including the controlling nutritional status score in patients with hepatocellular carcinoma undergoing transarterial chemoembolisation for prediction survival: a retrospective analysis

Published online by Cambridge University Press:  09 December 2021

Yi Chen
Affiliation:
Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, People’s Republic of China Department of Interventional Radiology, First Hospital of Shanxi Medical University, Shanxi Province, Taiyuan 030001, People’s Republic of China
Wen-ji Xu
Affiliation:
Department of CT and MRI, The Hospital of ShanXi University of Chinese Medicine, Taiyuan 030001, People’s Republic of China
Yi Yang
Affiliation:
Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, People’s Republic of China
Yu-Jing Xin
Affiliation:
Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, People’s Republic of China
Xin-Yuan Zhang
Affiliation:
Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, People’s Republic of China
Xiao Li
Affiliation:
Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, People’s Republic of China
Xiang Zhou*
Affiliation:
Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, People’s Republic of China
*
*Corresponding author: Xiang Zhou, email zhou.xiang@yeat.net
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Abstract

This retrospective study investigated the predictive value of the Controlling Nutritional Status (CONUT) score in patients with intermediate-stage hepatocellular carcinoma (HCC) who received transarterial chemoembolization (TACE). Nomograms were developed to predict progression-free and overall survival (PFS, OS). The medical data of 228 patients with HCC and treated with TACE were collected. The patients were apportioned to 2 groups according to CONUT score: low or high (<4, ≥4). Univariate and multivariate analyses were performed using Cox regression for OS and PFS. OS and PFS were estimated by the Kaplan-Meier curve and compared with the log-rank test. Nomograms were constructed to predict patient OS and PFS. The nomograms were evaluated for accuracy, discrimination, and efficiency. The cut-off value of CONUT score was 4. The higher the CONUT score, the worse the survival; Kaplan-Meier curves showed significant differences in OS and PFS between the low and high CONUT score groups (P = 0·033, 0·047). The nomograms including CONUT, based on the prognostic factors determined by the univariate and multivariate analyses, to predict survival in HCC after TACE were generated. The CONUT score is an important prognostic factor for both OS and PFS for patients with intermediate HCC who underwent TACE. The cut-off value of the CONUT score was 4. A high CONUT score suggests poor survival outcomes. Nomograms generated based on the CONUT score were good models to predict patient OS and PFS.

Information

Type
Research Article
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of The Nutrition Society
Figure 0

Table 1. Baseline characteristics of the 228 patients with hepatocellular carcinoma (HCC)*(Numbers and percentages)

Figure 1

Fig. 1. Kaplan–Meier curve showing the OS rates of the two groups.

Figure 2

Table 2. Univariate and multivariate analyses of prognostic factors for OS(Hazard ratio and 95 % confidence intervals)

Figure 3

Fig. 2. Kaplan–Meier curve showing the progression free survival (PFS) rates of the two groups.

Figure 4

Table 3. Univariate and multivariate analyses of prognostic factors for progression-free survival (PFS)(Hazard ratio and 95 % confidence intervals)

Figure 5

Fig. 3. Nomogram of OS to predict 5-year OS.

Figure 6

Fig. 4. Nomogram of progression free survival (PFS) to predict 1-year PFS.

Figure 7

Fig. 5. Nomogram calibration curves for survival. (a) OS. (b) PFS.

Figure 8

Fig. 6. Nomogram area under curve (AUC) for survival. (a) OS. (b) progression free survival (PFS).