
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)10050-3
10.1016/j.heliyon.2024.e34019
e34019
Research Article
A nomogram incorporating Psoas muscle index for predicting tumor recurrence after liver transplantation: A retrospective study in an Eastern Asian population
Yang Bo abcd1
Huang Guobin abcd1
Chen Dong abcd
Wei Lai abcd
Zhao Yuanyuan abcd
Chen Gen e
Li Junbo abcd
Wang Lu abcd
Xie Bowen abcd
Jiang Wei chiangwei@tjh.tjmu.edu.cn
f⁎
Chen Zhishui zschen@tjh.tjmu.edu.cn
abcd⁎⁎
a Institute of Organ Transplantation, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
b Key Laboratory of Organ Transplantation, Ministry of Education, Wuhan, 430030, China
c NHC Key Laboratory of Organ Transplantation, Wuhan, 430030, China
d Key Laboratory of Organ Transplantation, Chinese Academy of Medical Sciences, Wuhan, 430030, China
e Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
f Department of Gastrointestinal Surgery, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
⁎ Corresponding author. Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No.1095 Jiefang Avenue, Wuhan 430030, China. chiangwei@tjh.tjmu.edu.cn
⁎⁎ Corresponding author. zschen@tjh.tjmu.edu.cn
1 Bo Yang and Guobin Huang contributed equally to this work.

03 7 2024
30 8 2024
03 7 2024
10 16 e340192 2 2024
1 7 2024
2 7 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Background and aims

Tumor recurrence significantly affects the prognostic outcomes for liver cancer patients following liver transplantation. However, existing predictive models often neglect the inclusion of body composition indicators. Hence, this research aimed to investigate the significance of the psoas muscle index (PMI) in evaluating the post-transplant prognosis of liver cancer.

Methods

A retrospective analysis was conducted on liver cancer patients who underwent liver transplantation surgery. Imaging analysis was performed using CT data to calculate PMI based on the left and right psoas muscle areas. Subsequently, the patients were categorized into PMI-Low and PMI-High groups using the established cut-off values. Univariate and multivariate analyses were performed using Cox proportional hazards regression to assess the correlation between PMI and clinical outcomes, and a nomogram was constructed accordingly.

Results

Among the 225 patients included in the analysis, the PMI-High group exhibited significantly improved overall survival (P < 0.001) and disease-free survival (DFS, P < 0.001) rates compared to the PMI-Low group. PMI exhibited a positive correlation with body mass index (R = 0.25, P < 0.001), but no significant correlations were observed. In the multivariate analysis, PMI (HR = 4.596, P < 0.001), MELD score (HR = 1.591, P = 0.038), and Hangzhou criteria (HR = 2.557, P < 0.001) emerged as significant predictors of DFS. The constructed nomogram, incorporating these predictors, demonstrated outstanding predictive performance. Decision curve analysis revealed the superiority of the nomogram over conventional methods.

Conclusions

PMI serves as a valuable prognostic factor for tumor recurrence in liver cancer patients after liver transplantation. The established nomogram is pivotal in delivering personalized predictions of DFS.

Keywords

Liver cancer
Liver transplantation
Psoas muscle index
Tumor recurrence
Prognostic model
==== Body
pmcAbbreviations

AFP alpha-fetoprotein

AUC Area Under the Curve

BMI body mass index

BMP bone morphogenetic protein

cHCC-CC combined hepatocellular carcinoma and cholangiocarcinoma

CI confidence interval

CT computed tomography

DCA decision curve analysis

DFS disease-free survival

HCC hepatocellular carcinoma

HR hazard ratio

INR international normalized ratio

IQR median

KM Kaplan-Meier

LPA left psoas area

LT liver transplantation

MELD model for end-stage liver disease

MRI magnetic resonance imaging

NLR neutrophil-to-lymphocyte ratio

Nox4 Sirt1-NADPH oxidase 4

OS overall survival

PGD primary graft dysfunction

PMI psoas muscle index

ROI region-of-interest

ROC Receiver Operating Characteristic

RPA right psoas area

SD mean

1 Introduction

Liver cancer poses a significant global health challenge [1,2], with liver transplantation emerging as a crucial therapeutic strategy for end-stage cases. However, the scarcity of donor livers necessitates an effective organ allocation strategy [3]. Therefore, developing a robust model to evaluate the postoperative prognosis of liver cancer patients after liver transplantation is of utmost clinical significance. In the landscape of transplantation, the Milan criteria, based on tumor size and number, have been widely adopted in Western countries. Conversely, the Hangzhou criteria, influenced by the genetic and environmental factors prevalent in China, emphasize the importance of population-specific characteristics [4,5]. Pre-transplant prognostication heavily relies on the Child-Pugh classification and the Model for End-Stage Liver Disease (MELD) score. The Child-Pugh classification integrates clinical parameters such as ascites and encephalopathy to assess the severity of liver cirrhosis. In contrast, the MELD score uses biochemical markers such as serum bilirubin, creatinine, and the international normalized ratio (INR) to provide a quantitative measure of liver dysfunction severity [6,7]. Despite these measures, there is a conspicuous absence of a predictive model specifically tailored to postoperative recurrence in liver transplantation for liver cancer, with current evaluation models often neglecting body composition indices.

Sarcopenia, characterized by the progressive loss of muscle mass, is a critical determinant influencing cancer prognosis [8]. In recent years, the psoas muscle index (PMI) has gained recognition as a valuable indicator of the systemic impact of sarcopenia on patient outcomes [9]. PMI, calculated as the cross-sectional area of the psoas muscle normalized to height squared, provides a quantifiable measure of skeletal muscle mass. Beyond merely indicating muscle quantity, PMI also reflects broader aspects of muscle quality, such as strength and functional capacity. Zhang et al. [10] demonstrated an independent association between a low PMI and decreased overall survival (OS) in liver cancer patients undergoing transarterial chemoembolization. These findings underscore the clinical relevance of PMI in liver cancer patients. Additionally, recent studies have reported that PMI is associated with tumor recurrence [11]. Lower PMI values have been linked to higher risks of tumor recurrence [12] and poorer outcomes in various cancers, including colorectal cancer, renal cell carcinoma, pancreatic cancer, and liver cancer [13]. Nakazawa et al. reported that a lower PMI might indicate muscle wasting or loss of muscle mass, which can result from systemic inflammation, malnutrition, or advanced disease [14]. These factors are associated with poor treatment response, impaired immune function, and reduced overall survival. Kawakita et al. further demonstrated that muscle wasting was linked to increased treatment toxicity and decreased tolerance to chemotherapy, potentially leading to treatment interruptions or dose reductions that compromise therapy effectiveness [15]. However, it is important to note that the relationship between PMI and tumor recurrence is still an area of ongoing research, and the specific mechanisms underlying this association are not yet fully understood. The significance of PMI as a prognostic factor may vary depending on the type and stage of cancer, as well as individual patient characteristics.

Furthermore, muscle wasting is commonly observed in end-stage liver cancer patients awaiting transplantation, regardless of the underlying association [16]. As a significant factor influencing the prognosis of many diseases, including cancer, sarcopenia also impacts liver transplantation outcomes. PMI is considered a marker of muscle mass and overall health status. Kaido et al. reported that a lower PMI is associated with increased surgical complications, longer hospital stays, and higher mortality rates after liver transplantation [17]. Xu et al. demonstrated that a lower PMI correlates with higher rates of postoperative complications in liver transplant recipients, including infections, wound healing problems, delayed graft function, and respiratory issues [18]. Additionally, Tan et al. found that a lower PMI is associated with reduced long-term survival rates after liver transplantation. Patients with higher muscle mass, indicated by a higher PMI, tend to have better overall survival and lower rates of graft failure [19]. Liver transplantation candidates often experience malnutrition and muscle wasting due to the underlying liver disease and associated metabolic changes. Factors such as decreased oral intake, impaired nutrient absorption, and altered metabolism contribute to muscle loss [20]. Assessing PMI helps evaluate the nutritional status and muscle mass, guiding interventions such as nutritional support and physical therapy before and after transplantation [21]. Higher muscle mass, as reflected by a higher PMI, is associated with better functional recovery and quality of life following liver transplantation. While the exact mechanisms linking PMI and liver transplantation outcomes are not fully understood, several potential explanations have been proposed.

To begin with, Zhao et al. reported that the PMI reflects the nutritional status and muscle mass of liver transplant recipients. Malnutrition and muscle wasting can compromise immune function, impair wound healing, and increase the risk of postoperative complications [22]. Trigui et al. demonstrated that patients with higher muscle mass, as indicated by a higher PMI, may have better metabolic reserves to withstand the physiological stress associated with transplantation [23]. In contrast, Bolte et al. reported that lower muscle mass usually leads to increased postoperative complications and longer recovery times. Chronic liver disease is often associated with systemic inflammation, which can contribute to muscle wasting and loss of muscle mass [24]. Inflammatory mediators released by the liver and other organs can promote muscle protein breakdown and impair muscle regeneration. Patients with a lower PMI may have a higher degree of systemic inflammation, negatively impacting post-transplant outcomes. Additionally, patients with higher muscle mass generally have better physical strength and endurance, thereby facilitating postoperative recovery, early mobilization, and rehabilitation [25]. A lower PMI may indicate reduced physical function and a poorer response to rehabilitation efforts, leading to longer hospital stays and increased post-transplant complications. According to Ching-Sheng Hsu and his team, metabolic alterations, including insulin resistance, glucose intolerance, and changes in lipid metabolism may contribute to muscle wasting and loss of muscle mass [26]. However, it is important to note that the interplay of these mechanisms and their specific contributions to the association between PMI and liver transplantation outcomes require further investigation. Large-scale, multicenter studies and basic scientific research investigating the effects of sarcopenia on post-transplantation outcomes in liver cancer patients are still lacking.

Therefore, we propose that PMI significantly influences the occurrence of postoperative recurrence in liver cancer patients after liver transplantation. The objective of this research is to assess the predictive capacity of PMI for various postoperative outcomes, particularly tumor recurrence, in liver transplantation. Additionally, we aim to establish a linear regression model to aid in the allocation of donor livers. By analyzing data from liver transplant recipients in a single-center setting, we will evaluate the prognostic value of PMI in predicting postoperative outcomes. Furthermore, we will develop a comprehensive model that integrates PMI with other relevant clinical parameters to improve the accuracy of postoperative recurrence prediction and facilitate the allocation of donor livers.

2 Materials and methods

2.1 Patients

Liver cancer patients who underwent liver transplantation surgery at the Institute of Organ Transplantation, Tongji Hospital, between January 2015 and December 2019, were enrolled in this study. The preoperative diagnosis of liver cancer followed the diagnostic criteria of the European Association for the Study of the Liver, and was confirmed through postoperative histopathologic examination. All patients underwent postoperative follow-up assessments at 1 month after the operation, followed by 3-month intervals during the first 3 years, and subsequently, at 6-month intervals in the following years. Clinical information was extracted from the database of our outpatient service. Tumor recurrence was confirmed by the detection of one or more new lesions on at least two radiological examinations (e.g., magnetic resonance imaging (MRI), contrast-enhanced computed tomography (CT), or contrast-enhanced ultrasound), following the same diagnostic criteria applied for the initial diagnosis of liver cancer. The study excluded 61 samples, including one patient under 18 years old, fifty-eight with insufficient CT data, and two lacking corresponding clinical information. The final analysis comprised 225 patients, each with CT data available within 3 months before or after surgery (Fig. 1).Fig. 1 Flow chart for patient selection. From January 2015 to December 2019, 286 patients underwent liver transplantation surgery. Among them, one patient was excluded for being under 18 years old; two patients were excluded due to missing clinical data; and 58 patients were excluded because of inadequate CT data. Ultimately, a total of 225 samples were included in the analysis. CT, computed tomography.

Fig. 1

2.2 Imaging analysis

Imaging analysis was performed using the Synapse Workstation (v3.2.1, Fujifilm Medical Systems, USA). A radiologist, unaware of clinical information, independently extracted region-of-interest (ROI) data from the CT scans. The psoas muscle in the L3 (the third lumbar) vertebra plane [27,28] was chosen for ROI, and the right psoas area (RPA) and left psoas area (LPA) were calculated (Fig. S1). PMI was calculated as PMI (mm2/m2) = (LPA + RPA)/height [2]. Cutoff values were based on a large sample study of Eastern Asian populations. 490 (mm2/m2) for females and 675 (mm2/m2) for males [29]. Samples with PMI above the cut-off value were included in the PMI-High group; otherwise, they were categorized into the PMI-Low group.

2.3 Statistical analysis

Normality of data distribution was assessed using the Kolmogorov-Smirnov test for continuous variables. Normally distributed variables were presented as mean (SD) and analyzed using the t-test, while non-normally distributed variables were expressed as median (IQR), and non-parametric analysis was conducted using the Mann-Whitney U test. Categorical data were presented as n (%) and analyzed using the κ 2 test or Fisher's exact test. Spearman's correlation coefficient was employed to evaluate the relationship between variables.

The primary endpoint of this study was liver cancer recurrence, defined as any recurrence confirmed by follow-up examination after liver transplantation. Cumulative OS and disease-free survival (DFS) rates were determined using the Kaplan-Meier (KM) approach, and differences between the curves were assessed using the log-rank test. Univariate and multivariate regression analyses were conducted using Cox proportional hazards regression for DFS, and the hazard ratio (HR) and 95 % confidence interval (CI) were calculated. The stepwise forward likelihood ratio method was used to determine the variables included in the final model. The other endpoints included overall survival time, one-year mortality, and other clinical outcomes.

A nomogram was constructed using the "RMS" package, and its predictive performance was assessed using Receiver Operating Characteristic (ROC) curves, Area Under the Curve (AUC), and calibration curve. To compare the net benefit of the constructed nomogram model with clinical decision-making based on conventional methods, a decision curve analysis (DCA) was employed. P < 0.05 was deemed statistically significant. All statistical tests were conducted using the SPSS for Windows (v26.0) and R software (v4.2.1).

3 Results

3.1 Patients’ characteristics

We categorized the study population into two groups based on the PMI cut-off values established by Tee et al. for the East Asian population: the PMI-High group (n = 117) and the PMI-Low group (n = 108). As shown in Table 1, the PMI-High group had an average PMI value of 835.9 (±145.6) mm2/m2, while the PMI-Low group had an average PMI value of 471.6 (±118.5) mm2/m2. The body mass index (BMI) value in PMI-High group was elevated (P = 0.002) compared to PMI-Low group. The proportion of participants who received preoperative therapy was comparable between the two groups. Similar distribution across A, B, and C classifications was observed in both groups. The distribution of MELD scores was comparable between the two groups. Additionally, ascites in the PMI-Low group appeared to be more severe (P = 0.048). However, no significant differences in the levels of AFP, creatinine, albumin, total bilirubin, INR, platelet count, and sodium were observed between the two groups.Table 1 Baseline characteristics of the study population.

Table 1Baseline characteristics	PMI-High (n = 117)	PMI-Low (n = 108)	P	
Gender			0.504	
 Male	109 (93.2)	98 (90.7)		
 Female	8 (6.8)	10 (9.3)		
Age (years)	47.9 (9.4)	48.6 (9.6)	0.576	
Height (cm)	169.5 (6.8)	170.1 (5.7)	0.135	
Weight (kg)	67.3 (10.4)	68.1 (9.0)	0.503	
BMI (kg/m2)	24.3 (3.2)	23.1 (2.7)	0.002	
Etiology			0.421	
 Hepatitis B	101 (86.3)	97 (89.8)		
 Other	16 (13.7)	11 (10.2)		
Preoperative therapy			0.856	
 No	61 (52.1)	55 (50.9)		
 Yes	56 (47.9)	53 (49.1)		
Diabetes			0.540	
 No	105 (89.7)	94 (87.0)		
 Yes	12 (10.3)	14 (13.0)		
Hypoglycemic treatment			0.703	
 No	99 (84.6)	94 (87.0)		
 Yes	18 (15.4)	14 (13.0)		
Child-Pugh classification			0.835	
 A	52 (44.4)	51 (47.2)		
 B	49 (41.9)	41 (38.0)		
 C	16 (13.7)	16 (14.8)		
MELD score			0.253	
 < 20	55 (47.0)	59 (54.6)		
 ≥ 20	62 (53.0)	49 (45.4)		
Encephalopathy			0.258	
 No	108 (92.3)	104 (96.3)		
 Yes	9 (7.7)	4 (3.7)		
Ascites			0.048	
 No	58 (49.6)	60 (55.6)		
 Mild	57 (48.7)	40 (37.0)		
 Severe	2 (1.7)	8 (7.4)		
AFP (ng/ml)			0.500	
 < 400	86 (73.5)	75 (69.4)		
 ≥ 400	31 (26.5)	33 (30.6)		
Creatinine (μmol/L)	104.0 [73.0–189.5]	88.5 [64.0–168.0]	0.194	
Albumin (g/L)	39.0 [33.9–43.6]	38.8 [34.9–42.4]	0.578	
Total bilirubin (μmol/L)	122.0 [36.7–245.0]	108.5 [21.7–211.3]	0.102	
INR	1.7 [1.3–2.6]	1.4 [1.2–2.2]	0.060	
Platelet ( × 109/L)	91.0 [56.5–164.0]	109.5 [71.5–176.0]	0.167	
Sodium (mmol/L)	141.5 [138.6–143.1]	140.4 [137.7–143.1]	0.116	
PMI (mm2/m2)	835.9 (145.6)	471.6 (118.5)	<0.001	
PMI, psoas muscle index; BMI, body mass index; MELD, model for end-stage liver disease; AFP, alpha-fetoprotein; INR, international normalized ratio of prothrombin time.

As presented in Table 2, the majority of participants in both PMI-High and PMI-Low groups had hepatocellular carcinoma (HCC), with no significant difference in the distribution of pathology between the two groups. When comparing pathological characteristics, there were significant differences between the two groups regarding tumor differentiation (P = 0.004), tumor size (P = 0.008), tumor number (P = 0.016), and vascular invasion (P = 0.005). The PMI-High group had a higher proportion of well-differentiated tumors compared to the PMI-Low group. Conversely, the PMI-Low group had a higher proportion of tumors with moderate to poor differentiation. The PMI-High group had a higher proportion of tumors smaller than 3 cm, while the PMI-Low group had a higher proportion of tumors equal to or larger than 3 cm. In the PMI-High group, patients were more inclined to have a single tumor, whereas those in the PMI-Low group tended to have multiple tumors. A greater percentage of patients in PMI-High group exhibited no vascular invasion, while those in PMI-Low group demonstrated a higher incidence of vascular invasion. Furthermore, in the PMI-High group, seventy-nine patients (67.5 %) met the Milan criteria, while in the PMI-Low group, only forty patients (37 %) met these criteria (P < 0.001). Similarly, for the Hangzhou criteria, 94 patients (8.3 %) in the PMI-High group met the criteria, whereas only 60 patients (55.6 %) in the PMI-Low group met them (P < 0.001).Table 2 Pathology characteristics of study population.

Table 2Pathology characteristics	PMI-High (n = 117)	PMI-Low (n = 108)	P	
Pathology			0.669	
 HCC	110 (94.0)	100 (92.6)		
 Other	7 (6.0)	8 (7.4)		
Differentiation			0.004	
 Well	10 (8.5)	5 (4.6)		
 Well-Moderate	10 (8.5)	7 (6.5)		
 Moderate	39 (33.3)	38 (35.2)		
 Moderate-Poor	16 (13.7)	31 (28.7)		
 Poor	5 (4.3)	11 (10.2)		
 Other	37 (31.6)	16 (14.8)		
Tumor size			0.008	
 < 3 cm	72 (61.5)	47 (43.9)		
 ≥ 3 cm	45 (38.5)	60 (56.1)		
Tumor number			0.016	
 Single	85 (72.6)	62 (57.4)		
 Multiple	32 (27.4)	46 (42.6)		
Vascular invasion			0.005	
 No	98 (83.8)	73 (67.6)		
 Yes	19 (16.2)	35 (32.4)		
Milan Criteria			<0.001	
 Yes	79 (67.5)	40 (37.0)		
 No	38 (32.5)	68 (63.0)		
Hangzhou Criteria			<0.001	
 Yes	94 (80.3)	60 (55.6)		
 No	23 (19.7)	48 (44.4)		
PMI, psoas muscle index; HCC, hepatocellular carcinoma.

3.2 Correlation between PMI and clinical outcomes

We compared the clinical outcomes between the two groups of patients (Table 3). It was observed that the one-year mortality (11.1 % vs. 3.6 %, P < 0.001) and one-year recurrence rate (5.1 % vs. 15.7 %, P = 0.009) were remarkably higher in PMI-Low group than in PMI-High group. However, no obvious differences were found between the two groups regarding intraoperative bleeding volume, surgical duration, primary graft dysfunction rate, vascular complication rate, biliary complication rate, infection-related complication rate, and rejection reaction rate. In addition, the Kaplan-Meier method was employed to assess long-term outcomes. The results indicated that both cumulative OS (P < 0.001) (Fig. 2A)and DFS (P < 0.001)(Fig. 2B) were markedly improved in PMI-High group. We further stratified the data by confounding parameters such as BMI, tumor size, tumor number, adherence to Milan criteria, meeting Hangzhou criteria, and AFP value. These analyses consistently demonstrated robust survival advantages for the PMI-High group (Fig. S2). For DSF and OS, we constructed models using COX regression (Table 4). In the crude model, both recurrence (HR = 5.174, 95%CI: 3.139–8.527, P < 0.001) and mortality (HR = 3.722, 95%CI: 2.396–5.784, P < 0.001) rates were higher in PMI-Low group than in PMI-High group. Similarly, in the fully adjusted model, which accounted for age, gender, BMI, tumor differentiation, AFP, MELD score, Milan criteria and Hangzhou criteria, the observed patterns remained consistently significant (all P < 0.05).Table 3 Clinical outcomes.

Table 3Outcomes	PMI-High (n = 117)	PMI-Low (n = 108)	P	
Blood loss			0.712	
 < 1000 ml	47 (40.2)	46 (42.6)		
 ≥ 1000 ml	70 (59.8)	62 (57.4)		
Operation time			0.378	
 < 400 min	76 (65.0)	64 (59.3)		
 ≥ 400 min	41 (35.0)	44 (40.7)		
PGD			0.253	
 No	114 (97.4)	102 (94.4)		
 Yes	3 (2.6)	6 (5.6)		
Vascular complication			0.185	
 No	111 (94.9)	106 (98.1)		
 Yes	6 (5.1)	2 (1.9)		
Bile duct complication			0.119	
 No	98 (83.8)	98 (90.7)		
 Yes	19 (16.2)	10 (9.3)		
Infection complication			0.265	
 No	98 (83.8)	96 (88.9)		
 Yes	19 (16.2)	12 (11.1)		
Rejection			0.891	
 No	110 (94.0)	102 (94.4)		
 Yes	7 (6.0)	6 (5.6)		
Mortality within 1 year			<0.001	
 No	104 (88.9)	75 (69.4)		
 Yes	13 (11.1)	33 (30.6)		
Recurrence within 1 year			0.009	
 No	111 (94.9)	91 (84.3)		
 Yes	6 (5.1)	17 (15.7)		
PMI, psoas muscle index; PGD, primary graft dysfunction.

Fig. 2 Kaplan-Meier analysis revealing significant differences in both overall survival and disease-free survival between groups based on PMI cut-off values. Overall survival (a) was significantly better in the PMI-High group compared to the PMI-Low group (P < 0.001). Disease-free survival (b) was also significantly better in the PMI-High group compared to the PMI-Low group (P < 0.001). PMI, psoas muscle index.

Fig. 2

Table 4 COX regression model on disease-free survival and overall survival.

Table 4Models	Reference	PMI-Low	
HR (95 % CI)	P	
Disease-free survival	
Model1	PMI-High	5.174 (3.139–8.527)	<0.001	
Model2	PMI-High	5.274 (3.163–8.796)	<0.001	
Model3	PMI-High	4.354 (2.555–7.420)	<0.001	
Model4	PMI-High	4.391 (2.569–7.504)	<0.001	
Model5	PMI-High	4.771 (2.763–8.237)	<0.001	
Model6	PMI-High	4.072 (2.325–7.131)	<0.001	
Model7	PMI-High	4.479 (2.581–7.771)	<0.001	
Overall survival	
Model1	PMI-High	3.722 (2.396–5.784)	<0.001	
Model2	PMI-High	4.050 (2.567–6.388)	<0.001	
Model3	PMI-High	3.910 (2.413–6.334)	<0.001	
Model4	PMI-High	3.968 (2.446–6.438)	<0.001	
Model5	PMI-High	4.376 (2.666–7.181)	<0.001	
Model6	PMI-High	3.763 (2.256–6.275)	<0.001	
Model7	PMI-High	4.154 (2.514–6.864)	<0.001	
Model 1: No adjustment.

Model 2: Adjusted for age, gender, BMI.

Model 3: Model 2 + differentiation (categorical variables).

Model 4: Model 3 + AFP (categorical variables).

Model 5: Model4 + MELD score (categorical variables).

Model 6: Model 5 + Milan Criteria (categorical variables).

Model 7: Model 5 + Hangzhou Criteria (categorical variables).

PMI, psoas muscle index; HR, hazard ratio; CI, confidence interval.

3.3 Correlation between PMI and other parameters

In our study, we explored the relationship between PMI values and four key clinical parameters that could signify the vulnerability of liver cancer patients: age, BMI, MELD score, and Child-Pugh score. We employed the Spearman correlation coefficient for this analysis. Although we did not observe any significant associations between PMI and age(Fig. 3A), MELD score(Fig. 3C), or Child-Pugh score(Fig. 3D), notably, a significant positive correlation was identified between PMI and BMI (R = 0.25, P < 0.001) (Fig. 3B). This suggests that although there is a positive correlation with BMI, PMI appears to be independent of age, liver disease severity (MELD score), and liver function (Child-Pugh score) in this cohort of liver cancer patients.Fig. 3 Correlations among PMI and other parameters. PMI showed a significant correlation with BMI (b, R = 0.25, P < 0.001). However, no significant correlations were found between PMI and age (a), MELD score (model for end-stage liver disease) (c), or Child-Pugh score (d). PMI, psoas muscle index; BMI, body mass index; MELD, model for end-stage liver disease.

Fig. 3

3.4 Nomogram establishment

Furthermore, we conducted univariate and multivariate COX analyses to evaluate factors associated with tumor recurrence (Table 5). In the univariate analysis, variables with P-values <0.2 were selected for inclusion in the multivariate analysis, and the forward likelihood ratio method was used for variable selection. Ultimately, PMI (HR = 4.596, P < 00.001), MELD scores (HR = 1.591, P = 00.038), and Hangzhou standard (HR = 2.557, P < 00.001) were identified as independent factors related to DFS in the COX model. Next, we established a nomogram (Fig. 4A) according to the COX regression findings. Using the calculated values from the nomogram, we categorized the study population into Risk-High and Risk-Low groups, and KM curves were plotted for both groups (Fig. S3). Notably, the high-risk group exhibited significantly worse long-term outcomes. For further validation, we conducted Receiver Operating Characteristic (ROC) analysis (Fig. 4B). The Area Under the Curve (AUC) values of the nomogram predictions for 1-, 3- and 5-year DFS were 0.797, 0.839 and 0.864, respectively. Calibration curves (Fig. 4C) demonstrated that the nomogram exhibited excellent predictive performance. Additionally, we performed Decision Curve Analysis (DCA) for 1-, 3-, and 5-year DFS (Fig. 4D–F). The results indicated that the nomogram developed in this study yielded the highest net benefit compared to conventional factors such as MELD scores, Hangzhou criteria, Milan criteria, alpha-fetoprotein (AFP), and PMI. Taken together, our comprehensive analyses highlight PMI, MELD scores, and adherence to the Hangzhou standard as independent factors associated with DFS. The nomogram derived from these factors demonstrated robust predictive accuracy, surpassing other conventional measures in assessing the risk of tumor recurrence at 1, 3, and 5 years.Table 5 COX regression for disease-free survival. Parameters with P value < 0.2 in univariate analysis were input into the multivariate analysis. Stepwise forward likelihood ratio method was used to select the final parameters.

Table 5Variables	Univariate analysis	Multivariate analysis	
HR (95 % C.I.)	P	HR (95 % C.I.)	P	
Male	0.933 (0.406–2.142)	0.870			
Age	1.002 (0.980–1.025)	0.849			
BMI	0.955 (0.888–1.027)	0.211			
Diabetes	1.312 (0.695–2.475)	0.402			
Child-Pugh classification	
 A	Ref	0.690			
 B	1.036 (0.645–1.663)	0.884			
 C	1.304 (0.705–2.412)	0.398			
MELD score	
 < 20	Ref		Ref		
 ≥ 20	1.386 (0.900–2.135)	0.138	1.591 (1.026–2.466)	0.038	
AFP>400	
 < 400	Ref				
 ≥ 400	2.366 (1.531–3.655)	<0.001			
PMI	
 High	Ref		Ref		
 Low	5.174 (3.139–8.527)	<0.001	4.596 (2.742–7.704)	<0.001	
Tumor size	
 < 3 cm	Ref				
 ≥ 3 cm	2.182 (1.395–3.413)	0.001			
Tumor number	
 Single	Ref				
 Multiple	1.600 (1.033–2.479)	0.035			
Vascular invasion	
 No	Ref				
 Yes	2.982 (1.914–4.644)	<0.001			
Milan Criteria	
 Yes	Ref				
 No	4.199 (2.588–6.813)	<0.001			
Hangzhou Criteria	
 Yes	Ref		Ref		
 No	3.617 (2.341–5.586)	<0.001	2.557 (1.634–4.002)	<0.001	

Fig. 4 A nomogram was constructed to predict 1-year, 3-year, and 5-year disease-free survival. The model included three parameters: Hangzhou criteria (Yes/No), MELD score ≥20 (Yes/No), and PMI (High/Low). b ROC analysis of the nomogram for disease-free survival showed AUC values of 0.797, 0.839 and 0.864 for 1-year, 3-year and 5-year predictions, respectively. c The calibration curve demonstrated the nomogram's accuracy in predicting the probability of 1-year, 3-year and 5-year disease-free survival. DCA analysis for 1-year (d), 3-year (e) and 5-year (f) disease-free survival indicated that the nomogram achieved the largest net benefit compared to other strategies. MELD, model for end-stage liver disease; PMI, psoas muscle index; ROC, receiver operating characteristic; AUC, area under the curve; DCA, decision curve analysis.

Fig. 4

4 Discussion

PMI was first introduced by Hamaguchi et al. in a Japanese cohort of adult living donors for liver transplantation [29]. This index is calculated by measuring the cross-sectional area of the psoas muscle at the L3 vertebral level and normalizing it to the square of the patient's height. Generally, it serves as a quantitative measure of muscle mass, particularly focusing on the psoas muscle, which is an important skeletal muscle involved in various functional activities. Previous studies have investigated the influence of PMI on the prognosis of individuals with chronic liver disease and those undergoing nonsurgical treatments for HCC, revealing a correlation between lower PMI and a worse prognosis [30,31]. Additionally, research has established PMI as an independent predictor of OS in patients undergoing nonsurgical treatments for HCC [32]. Our study provides compelling evidence supporting the role of PMI as a robust prognostic indicator for tumor recurrence in liver cancer patients after liver transplantation. Our findings indicate that higher PMI values are associated with significantly improved OS and DFS rates compared to lower PMI values, even after adjusting for confounding factors. Notably, the significant correlation between PMI and various clinical parameters, such as BMI, tumor characteristics, and outcomes, underscores its potential as a valuable prognostic indicator for predicting long-term outcomes in this patient cohort. Furthermore, we successfully developed a novel prognostic model centered on PMI, which shows promise for predicting tumor recurrence and aiding in post-transplantation management decisions.

Liver cancer patients awaiting transplantation often experience malnutrition and muscle wasting, factors linked to increased early post-transplant mortality and morbidity [33]. Several studies have recognized BMI as an important prognostic factor for various cancer types, including liver cancer [34,35]. Our study highlights the clinical significance of PMI by revealing a positive correlation with BMI [36]. This emphasizes the importance of considering both overall nutritional status and muscle mass when assessing the prognosis of liver cancer patients. Additionally, our study revealed a significant relationship between PMI and favorable tumor characteristics such as better tumor differentiation, smaller tumor size, lower tumor number, and a lower incidence of vascular invasion. These findings are consistent with recent literature suggesting that PMI can serve as a predictor of tumor aggressiveness and progression [37]. The presence or absence of microvascular invasion is a crucial factor in the prognosis and management of HCC. Microvascular invasion refers to the invasion of tumor cells into the microvessels, which is associated with a higher risk of recurrence and poorer outcomes. Mao et al. [38] identified six preoperative factors, including visceral adipose tissue density, intramuscular adipose tissue index, skeletal muscle area, age, tumor size, and cirrhosis, and integrated them into a nomogram, which demonstrated strong performance for predicting microvasular invasion risk in HCC patients.

Sarcopenia, characterized by the loss of muscle mass, has been linked to poor clinical outcomes in liver cancer patients, including an increased risk of tumor recurrence [39]. Our findings align with previous research, showing that lower PMI values are markedly related to worse OS and DFS in liver cancer patients undergoing transplantation. Even in the fully adjusted model, where age, gender, BMI, tumor differentiation, AFP, MELD score, Milan criteria and Hangzhou criteria were considered, the observed patterns remained significant. Therefore, our study revealed muscle wasting was independently associated with tumor recurrence after transplantation. Interventions focused on preserving or improving muscle mass, such as nutritional support and exercise interventions, could be explored as adjuvant therapies to enhance postoperative outcomes and reduce the risk of tumor recurrence [40]. Hou et al. [41] investigated the prognostic value of sarcopenia in patients with combined hepatocellular carcinoma and cholangiocarcinoma (cHCC-CC) after surgery. The sarcopenia group exhibited significantly worse OS and DFS compared to the non-sarcopenia group. Multivariate Cox regression analyses identified sarcopenia as an independent and stable risk factor for both OS and DFS. They also developed a nomogram, which outperformed primary liver cancer stages in prognostic prediction. Nonetheless, understanding the molecular pathways underlying muscle wasting and tumor biology is essential for the development of targeted therapeutic strategies. Alterations in AKT, bone morphogenetic protein (BMP), and Sirt1-NADPH oxidase 4 (Nox4) signaling pathways have been implicated in muscle wasting and tumor progression [[42], [43], [44]]. Exploring these pathways may offer potential avenues for preventing tumor recurrence and improving outcomes in liver cancer patients after transplantation.

The clinical implications of this study are particularly noteworthy. The development of prognostic models that incorporate PMI has gained considerable attention in recent years. Combining PMI with other factors, such as the neutrophil-to-lymphocyte ratio (NLR), has demonstrated excellent prognostic value in liver cancer patients [45]. However, current liver transplant allocation criteria do not consider nutritional status, potentially affecting the prognosis of individuals with compromised nutrition. Takuma et al. [46] developed nomograms based on tumor-related factors, liver function markers, and patient demographics to predict DFS and OS in HCC patients treated with radiofrequency ablation. These nomograms outperformed traditional staging systems, highlighting their potential for enhanced prognostic accuracy in early-stage HCC patients. Our nomogram, which integrates PMI, MELD score, and Hangzhou criteria, demonstrated good predictive performance for DFS and offers a more personalized approach for predicting outcomes and guiding treatment decisions. Incorporating PMI into prognostic models holds promise for individualized risk assessment and treatment decision-making in clinical practice. The MELD score, a widely acknowledged tool for assessing liver disease severity, emerged as a key player in our prognostic models. Intriguingly, our findings highlight that alongside PMI, the MELD score is significantly related to DFS, underscoring the importance of combining liver function assessments with muscle mass considerations for a comprehensive prognostic evaluation. The integration of Hangzhou criteria, a set of guidelines for liver transplantation in HCC patients, further strengthens our prognostic model. By combining PMI, MELD score, and Hangzhou criteria, our nomogram emerges as a potent tool for predicting DFS. Nevertheless, further investigation in larger, multicenter cohorts is needed to validate the predictive accuracy and clinical applicability of this model.

Earlier studies have explored the relationship between PMI and outcomes of different liver cancer treatment modalities. This study contributes to existing knowledge by specifically focusing on liver transplantation for liver cancer, addressing a gap in previous research that primarily explored the relationship between PMI and other treatment modalities. However, this study has several limitations. Firstly, its retrospective nature may have introduced selection bias and potential confounding factors. Secondly, this study was performed at a single center, which could limit the generalizability of the results to a broader population. Thirdly, the study focused on a specific population of East Asian individuals. Although this specificity provides detailed insights into a particular demographic, caution is necessary when extrapolating these findings to other ethnicities or populations. Despite these limitations, the study has some strengths, particularly the inclusion of comprehensive clinical information. This depth of data contributes to a better understanding of the relationship between PMI and liver cancer outcomes. The use of rigorous statistical analysis methods is also a notable strength, ensuring the accuracy and reliability of the study's findings. Additionally, the construction of a nomogram adds practical value, offering a visual tool for clinicians to predict outcomes based on various factors. This not only facilitates individualized predictions of DFS but also supports clinical decision-making in a tangible and applicable manner.

5 Conclusions

In summary, this study reveals that PMI is an essential prognostic factor for tumor recurrence in liver cancer patients after liver transplantation. These findings underscore the importance of body composition and its association with cancer outcomes. The established nomogram, incorporating the PMI, MELD score, and Hangzhou criteria, provides a comprehensive model for predicting DFS and can guide clinical decision-making for post-transplantation management. Further research in larger, multicenter cohorts is warranted to validate these findings and explore the underlying mechanisms linking PMI to tumor recurrence in liver cancer patients undergoing liver transplantation.

Ethics statement

Study approval was obtained from the Institutional Review Board at Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology (TJ-IRB20230607). The approval was obtained at June 6th, 2023. Content was obtained from the minor in addition to parental consent. This study was conducted using medical records obtained from previous clinical diagnosis and treatment, which met the requirements for the waiver of informed consent application. The waiver of informed consent has been applied for and the waiver of informed consent certificate has been uploaded.

Data availability statement

Data will be made available on request.

Funding

This work was supported by the 10.13039/501100001809 National Natural Science Foundation of China (82000602 ), Knowledge Innovation Program of Wuhan-Shuguang Project (2022020801020448 ), and Jiangeng Pharmaceutical-Qimingxing Project (KYXZ012021120005 ).

CRediT authorship contribution statement

Bo Yang: Writing – original draft, Supervision, Conceptualization. Guobin Huang: Writing – original draft, Formal analysis, Data curation. Dong Chen: Writing – review & editing. Lai Wei: Writing – review & editing. Yuanyuan Zhao: Writing – review & editing, Formal analysis. Gen Chen: Data curation. Junbo Li: Data curation. Lu Wang: Data curation. Bowen Xie: Data curation. Wei Jiang: Supervision, Formal analysis, Conceptualization. Zhishui Chen: Conceptualization, Supervision.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A Supplementary data

The following are the Supplementary data to this article:figs1 figs1

figs2 figs2

figs3 figs3

Acknowledgements

The authors would like to express their gratitude to EditSprings (https://www.editsprings.cn) for the expert linguistic services provided.

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.heliyon.2024.e34019.
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