SYSTEMATIC REVIEW article

Front. Oncol., 29 November 2021

Sec. Gastrointestinal Cancers

Volume 11 - 2021 | https://doi.org/10.3389/fonc.2021.675870

Identifying Optimal Surgical Intervention-Based Chemotherapy for Gastric Cancer Patients With Liver Metastases

  • 1. Department of General Surgery, Taihe Hospital, Hubei University of Medicine, Shiyan, China

  • 2. Department of Anesthesiology, Institute of Anesthesiology, Taihe Hospital, Hubei University of Medicine, Shiyan, China

  • 3. Hubei Key Laboratory of Embryonic Stem Cell Research, Taihe Hospital, Hubei University of Medicine, Shiyan, China

  • 4. Department of General Surgery, Xinchang Hospital Affiliated to Wenzhou Medical University, Wenzhou, China

  • 5. Department of Pediatrics, Zhongnan Hospital of Wuhan University, Wuhan, China

  • 6. College of Basic Medical Sciences, Hubei University of Medicine, Shiyan, China

  • 7. Department of Stomatology, Taihe Hospital, Hubei University of Medicine, Shiyan, China

  • 8. School of Basic Medicine, Fourth Military Medical University, Xi’an, China

Abstract

Background:

This study aimed at evaluating the effects of surgical treatments-based chemotherapy in the treatment of gastric cancer with liver metastases (GCLM). It has not been established whether Liver-directed treatment (LDT) options such as hepatectomy and gastrectomy plus chemotherapy (HGCT), radiofrequency ablation and gastrectomy plus chemotherapy (RFAG), transarterial chemoembolization and gastrectomy plus chemotherapy (TACEG), gastrectomy plus chemotherapy (GCT) enhance the survival of GCLM patients.

Methods:

We performed systematic literature searches in PubMed, EMBASE, and Cochrane library from inception to September 2021. We created a network plot to comprehensively analyze the direct and indirect evidence, based on a frequentist method. A contribution plot was used to determine inconsistencies, a forest plot was used to evaluate therapeutic effects, the publication bias was controlled by funnel plot, while the value of surface under the cumulative ranking curves (SUCRA) was calculated to estimate rank probability.

Results:

A total of 23 retrospective studies were identified, involving 5472 GCLM patients. For OS and 1-, 2-, 3-year survival rate of all trials, meta-analysis of the direct comparisons showed significant better for HGCT treatments compared with GCT or PCT. In the comparison of the 5 treatments for 1-, 2-, 3-year survival rate, HGCT and RFAG were found to be more effective than GCT and PCT, respectively. By OS and 2-, 3-year survival rate analysis, RFAG was identified as the best option, followed by HGCT, TACEG, GCT and PCT. By 1-year survival rate analysis, HGCT and RFAG were identified as the most effective options.

Conclusion:

HGCT and RFAG has remarkable survival benefits for GCLM patients when compared to TACEG, GCT and PCT. HGCT was found to exhibit superior therapeutic effects for GCLM patients for 1-year survival rate while RFAG was found to be a prospective therapeutic alternative for OS and 2-, 3-year survival rate.

Systematic Review Registration:

identifier [10.37766/inplasy2020.12.0009].

Introduction

Globally, gastric cancer is the fourth most common malignant tumor and the second highest cause of cancer-related mortalities (13). Therapeutic options for advanced gastric cancers have been enormously improved. In the last two decades, the 5-year survival rate is up to 40%. However, gastric cancer with liver metastases is considered a late-stage disease. Systemic chemotherapy was recommended as standard cure, with a 5-year survival rate of less than 10% (4, 5). The current standard management of GCLM is systemic chemotherapy with supportive care. Liver metastasis is a common phenomenon for many types of cancer (68). Liver-directed treatment (LDT) options such as hepatectomy and gastrectomy plus chemotherapy (HGCT), radiofrequency ablation and gastrectomy plus chemotherapy (RFAG), transarterial chemoembolization and gastrectomy plus chemotherapy (TACEG), gastrectomy plus chemotherapy (GCT) for GCLM is controversial (5, 9, 10). Compared to systemic chemotherapy, surgical treatment such as HGCT and RFAG of hepatic metastases presents favorable prognosis (1113). According to the guidelines of The Committee of the Japan Gastric Cancer Association (JGCA) and National Comprehensive Cancer Network (NCCN), palliative management is recommended for stage IV gastric cancer, e.g. GCLM. In contrast, colorectal liver metastases are considered as suitable targets for radical surgery because they often present as liver-only metastatic disease, and R0 resection shows good prognostic outcomes, with a 5-year survival rate > 50% (14, 15). Retrospective studies have presented that the combination of hepatectomy and gastrectomy has visible survival outcome superiority (1621). In the last two decades, along with the results of reported studies which demonstrated that radical surgery of primary gastric cancer and metastatic liver lesions had survival benefits, the Guidelines Committee of JGCA reconsidered the effect of surgical treatment in GCLM patients (22). Therefore, the role of LDT for GCLM is gradually being considered.

Previous therapeutic options for GCLM were HGCT, RFAG, TACEG, GCT and palliative chemotherapy (PCT). There are no randomized controlled clinical trials for GCLM therapies. In the present literature, majority of the studies are retrospective studies, which were performed at a single center, with a limited number of patients. Although some studies have confirmed the superior therapeutic outcomes of LDT, the clinical pathological characteristics of the involved patients reveal some selection bias, therefore, their results are difficult to accept. We performed a network meta-analysis to evaluate the survival benefits of LDT and systemic chemotherapy in the treatment of GCLM.

Methods

Study Protocol

This work was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) of the Cochrane Handbook for Systematic Reviews of Intervention (23). The full protocol was registered and available on INPLASY (INPLASY2020120009).

Search Strategy

We retrieved literature published in between 1966 and September 1st, 2021 by searching PubMed, EMBASE, and Cochrane Library with the keywords (1) “stomach neoplasm” OR “gastric neoplasms” OR “cancer of stomach” OR “stomach cancers” OR “gastric cancer” AND (2) “liver metastases OR liver metastasis OR hepatic metastasis” AND (3) “operative surgical procedure” OR ablation OR liver resection OR hepatectomy OR gastrectomy OR chemotherapy OR “interventional therapy” and using the search strategies as illustrated in Supplementary Table 1. We selected and evaluated all relevant studies and review articles about GCLM and inquired the authors for unpublished raw data. Searches were limited to English-language publications. In addition, the reference lists of the retrieved articles were examined for potential eligible studies.

Study Selection

The inclusion criteria for the studies were: i. Systemic chemotherapy and surgical treatment; ii. Series of case control or cohort studies; iii. The number of patients were to be > 20; iv. Consists of available endpoints, such as overall survival, 1-, 2-, 3-, and 5-year survival rates, median survival time, and postoperative complications. The exclusion criteria for the studies were: i. studies with insufficient data or no related endpoints; ii. Missing control group.

Data Extraction

Two researchers (MS and ZZ) independently extracted results from the enrolled articles in a standardized form. In addition, a third researcher (TL) was consulted in case there were disagreements. The information extracted from each study included the first author, country, year of publication, number of cases, treatment, sex, median or mean age of patients, study design, follow-up, median survival time. If a study did not report the Hazard Ratio (HR) of overall survival, we estimated HR and their corresponding 95% confidence intervals (CIs) using the method described by Parmar et al. (24) and Tierney et al. (25). We recovered the data of Kaplan-Meier curves as recently described by us (26, 27).

Quality Assessment

We used the Newcastle-Ottawa Scale (NOS) to assess the quality of each included study. Scores ≥ 7 were considered high quality. We used a “star system” for case-control studies (Supplementary Table 2).

Publication Bias

The funnel plots were used to establish publication bias. The funnel plot that was symmetrical near zero represented no publication bias.

Statistical Analysis

The primary endpoint of this network meta-analysis (NMA) was overall survival (OS), defined as the time from random assignment to date of death from any cause or date of last follow-up. Secondary endpoints were 1-, 2-, 3-year survival rates. A pair-wise meta-analysis was performed by STATA 13.0 (Stata Corp, College Station, TX). R-3.6.3 and R packages gemtc were applied to conduct the Bayesian NMA, and 95% confidence intervals (CIs) were computed for HR in overall survival analysis. 1-, 2-, 3-year survival rates were analyzed while Odds Ratios (OR) with 95% confidence intervals (CIs) was calculated by fixed-effects or random-effects model (28, 29). Z test was performed to evaluate the significance of overall effect size.

A network plot was then used to directly demonstrate the whole information of included studies (30). Depending on direct comparison and indirect comparison outcomes, we estimated the contribution of each direct treatment comparison in the whole network structure, which was presented in a contribution plot. The inconsistency factor (IF) was calculated to determine the possible inconsistency in network comparison. The 95% CIs of IF values close to zero or the p value of Z test higher than 0.05 demonstrated there being no statistically significant inconsistency (31). Summary effects and corresponding predictive intervals were used to conclude relative mean effects and impact of heterogeneity in the network forest plot.

Finally, we calculated the surface under the cumulative ranking curve (SUCRA) of each treatment, which transformed the relative effects to the probability (2). SUCRA values range from 0 to 100%. The treatment was more valuable if the SUCRA value was higher. According to the estimated probability value, the treatments were ranked, which showed the percentage of effectiveness a treatment achieves with reference to an imaginary ideal treatment. Small-study effects was adjusted by a model of network meta-regression, the variance of the log-odds ratios as covariation (32).

Results

Study Characteristics

A total 6362 relevant articles were downloaded. The flow diagram documenting the search and inclusion of relevant studies is displayed in Figure 1. After considering the inclusion and exclusion criteria, a total of 23 retrospective studies involving 5472 GCLM patients were identified (12, 3357). At least one of the following treatments were assessed by the study: HGCT, RFAG, GCT, PCT, and TACEG. Eight studies were three-arm trials while fifteen studies were two-arm trials. Further characteristics and Newcastle-Ottawa scale results regarding the included studies are presented in Table 1 (Supplementary Table 2).

Figure 1

Table 1

StudyAuthorCountryYearNumber of PatientsMedian age (years)Follow up (months)Hepatectomy armSynchronousLVIG3UnilobarSolitaryT3-4R0N+Median survival time (months)Tumor size of hepatic metastasis (cm)Tumor size of gastric cancer (cm)NOS score
1Markar (37)Japan2016217658.3Minor liver resections7863NA217NANANANANANANA9
2Guner (12)Japan201698NANANA3949NA64NA72NA78HGCT 24; RFAG 23NANA7
3Guan (33)China201613663NAPartial hepatectomy71NANA62136NANANAPCT 8.7; RFAG 10.1; GCT 13.3NANA8
4Yao (38)China201549NANAIrregular hepatectomyNANANA34NANANANAHGCT 24; GCT 12NANA8
5Shinohara (39)Japan20154766.7NALiver resection28NA16251825NA41HGCT 22; GCT 7NANA8
6Ohkura (40)Japan20153467.222.4NA3429NANANA24NANANANANA8
7Liu (41)China201510759 ± 1.7NANA107NA7618NANANANARFAG 5; PCT 3NANA8
8Li (42)China20154961.4 ± 9.519.6NA49NA18NANA39NANAGCT 20.5; PCT 9.1NANA9
9Wang (58)China2014666114Radical surgeries66NA634NA31NA33NANANA7
10Tiberio (43)Italy201419568NAHepatectomyNANANANANANA53NAHGCT 13; GCT 6.6; PCT 3NANA8
11Chen (45)China201311454NAMajor hepatectomyNANA23635178NA17HGCT 22.3; PCT 5.5NANA7
12Miki (46)Japan2012507033.4Hepatectomy41NANA252040NANAHGCT 33.4; GCT 10.5; PCT 8.7NANA8
13Makino (47)Japan20106365.816Hepatectomy31NANA3024NANA54NANANA9
14Lu (36)China201060NANAHepatectomyNANANA3434NANANAHGCT 20; RFAG 18; GCT 16NANA7
15Kim (36)Korea20102957.914.4NA12NA1123NA21NANARFAG 30.7; GCT 6.8RFAG 2.8 ± 1.4; GCT 4.5 ± 1.5RFAG 5.1± 2.3; GCT 6.1 ± 2.27
16Cheon (48)Korea2008586115.5Hepatectomy42NA234229NANA8HGCT 21.7; RFAG 17; GCT 8.1HGCT 2.4 ± 1.7HGCT 5.7 ± 2.48
RFAG 2.1 ± 1.4RFAG 6.6 ± 3.1
GCT 6.1 ± 2.3
17Li (35)China200644NANAHepatectomyNANA31NANANANANAHGCT 19.5; GCT 11; PCT 6.2NANA7
18Li (49)China, Taiwan201765368.28 ± 12.8733NANANANANANANANANAGCT 3.13; HGCT 26.16NANA9
19Shirasu (51)Japan20182464.647.9Partial hepatectomy16NA5102NA91HGCT 24.8; PCT 38.1NANA9
20Jagric (54)Slovenia20204265.2 ± 8.49NAMetastasectomy42NA1923234018NAHGCT 9.3; GCT 4.2NANA9
21Picado (57)USA201831756421(10–32)NA42NA2168NANA2601371496GCT 16; PCT 9.7NANA9
22Tang (55)China2020306260NA351833363146NA37HGCT 21; RFAG 32; GCT 17HGCT, 2.9 ± 1.6NA9
RFAG, 2.8 ± 1.7
GCT, 2.1 ± 2.0
23Yu (56)China202013262.5(32-75)37.1(1-96)NA132NA4636NA12139111HGCT 33.6(26.6-40.6); PCT 12.4(10.0-14.8)NANA9

The major clinical and survival information of included eighteen studies.

NA, Not available; Synchronous, Number of patients with synchronous liver metastases; N+, Number of patients with lymph-node involvement of the primary cancer; T3-4, Number of patients with stage pT3 or pT4; LVI, Number of patients with lymphovascular involvement; G3, Number of patients with G3 primary cancer; Unilobar, Number of patients with unilobar liver involvement; R0, Number of patients who achieved an R0 surgical removal on both primary cancer and liver metastases; Solitary, Number of patients with solitary liver metastases; HGCT, hepatectomy and gastrectomy plus chemotherapy; GCT, gastrectomy plus chemotherapy; PCT, palliative chemotherapy; RFAG, radiofrequency ablation and gastrectomy plus chemotherapy; TACEG, transarterial chemoembolization and gastrectomy plus chemotherapy.

Direct Comparisons and Subgroup Analysis

For OS and 1-, 2-, 3-year survival rate of all trials, meta-analysis of the direct comparisons showed significant better for HGCT treatments compared with GCT or PCT, with the exception of RFAG (Table 2). As to OS and 1-, 2-, 3-year survival rate of all trials, PCT predicted a significantly worse OS than GCT (Table 2). For 1-, 2-, 3-year survival rate, the results showed that RFAG indicated a better survival rate than GCT (Table 2). Analysis of Asian subgroups showed that HGCT were better than GCT in OS, and 1-, 2-, 3-year survival rate, RFAG were better than GCT in 1-, 2-, 3-year survival rate (Table 2). Overall, statistical heterogeneity was moderate, although for most comparisons 95% CIs were wide and included values indicating very high or no heterogeneity, which portrayed the small number of studies available for every pair-wise comparison. In the meta-analyses of direct comparisons for OS and 1-, 2-, 3-year survival rate, I² values higher than 40% were recorded for the comparisons HGCT versus GCT and HGCT versus PCT (Table 2).

Table 2

OutcomeSubgroupsNo. of trialsOR, FEM95%CI, FEMP value of FEMOR, REM95%CI, REMP value of REMI2Heterogeneity P
All trials
OSGCT vs HGCT122.147[1.819; 2.534]<0.00012.209[1.744; 2.797]<0.000141.30%0.066
PCT vs HGCT82.797[2.3; 3.402]<0.00012.664[1.991; 3.563]<0.000147.30%0.065
RFAG vs HGCT31.07[0.725; 1.580]0.7341.07[0.725; 1.580]0.7340.00%0.81
GCT vs PCT20.551[0.467; 0.650]<0.00010.551[0.467; 0.650]<0.00010.00%0.418
1-year survival ratesHGCT vs GCT134.173[3.090; 5.635]<0.00014.438[2.852; 6.905]<0.000143.79%0.0455
HGCT vs PCT85.831[3.957; 8.591]<0.00015.765[3.286; 10.113]<0.000144.09%0.0847
HGCT vs RFAG31.084[0.538; 2.186]0.82071.091[0.540; 2.203]0.80750.00%0.6299
HGCT vs TACEG10.816[0.211; 3.159]0.76880.816[0.211; 3.159]0.7688NA<0.0001
GCT vs PCT72.957[2.308; 3.788]<0.00012.944[2.297; 3.775]<0.00010.00%0.7182
GCT vs RFAG30.248[0.100; 0.617]0.00270.25[0.099; 0.633]0.00350.00%0.5505
GCT vs TACEG21.103[0.559; 2.174]0.77790.688[0.055; 8.540]0.771189.22%0.0023
PCT vs TACEG20.34[0.186; 0.622]0.00050.322[0.086; 1.206]0.092578.12%0.0325
2-year survival ratesHGCT vs GCT85.311[3.353; 8.410]<0.00015.076[2.303; 11.185]0.000155.67%0.0271
HGCT vs PCT64.707[2.673; 8.289]<0.00014.824[1.270; 18.330]0.020970.83%0.0042
HGCT vs RFAG20.725[0.296; 1.774]0.48090.725[0.296; 1.775]0.4810.00%0.7939
HGCT vs TACEG10.468[0.136; 1.611]0.22840.468[0.136; 1.611]0.2284NA1
GCT vs PCT53.106[2.292; 4.209]<0.00013.059[2.260; 4.140]<0.00010.00%0.9282
GCT vs RFAG30.161[0.050; 0.513]0.0020.158[0.026; 0.958]0.044839.87%0.1895
GCT vs TACEG20.702[0.291; 1.691]0.43030.488[0.001; 238.526]0.820291.40%0.0006
PCT vs TACEG20.849[0.238; 3.028]0.8010.732[0.025; 21.063]0.855577.22%0.0361
3-year survival ratesHGCT vs GCT114.742[2.699; 8.333]<0.00014.556[2.574; 8.061]<0.00010.00%0.9186
HGCT vs PCT75.157[2.628; 10.120]<0.00015.565[1.811; 17.103]0.002748.73%0.0689
HGCT vs RFAG30.877[0.454; 1.695]0.69670.877[0.453; 1.698]0.6960.00%0.7758
HGCT vs TACEG10.988[0.248; 3.935]0.98660.988[0.248; 3.935]0.9866NA1
GCT vs PCT54.227[2.822; 6.332]<0.00014.295[2.908; 6.345]<0.00010.00%0.9903
GCT vs RFAG30.153[0.034; 0.687]0.01440.176[0.037; 0.842]0.02960.00%0.5427
GCT vs TACEG20.332[0.067; 1.645]0.17680.454[0.011; 19.365]0.679965.83%0.0871
PCT vs TACEG10.139[0.006; 3.507]0.23090.139[0.006; 3.507]0.2309NA<0.0001
Trials of Asian population
OSGCT vs HGCT102.283[1.861; 2.800]<0.00012.308[1.695; 3.143]<0.000147.40%0.047
PCT vs HGCT72.67[2.142; 3.328]<0.00012.505[1.770; 3.546]<0.000151.90%0.052
RFAG vs HGCT31.07[0.725; 1.580]0.7341.07[0.725; 1.580]0.7340.00%0.81
GCT vs PCT20.503[0.346; 0.732]<0.00010.503[0.346; 0.732]<0.00010.00%0.526
1-year survival ratesHGCT vs GCT105.565[3.716; 8.333]<0.00015.266[3.192; 8.688]<0.000126.94%0.196
HGCT vs PCT64.316[2.685; 6.937]<0.00014.465[2.240; 8.901]<0.000143.24%0.1169
GCT vs PCT43.027[1.675; 5.472]0.00023.034[1.673; 5.500]0.00030.00%0.7838
HGCT vs RFAG31.084[0.538; 2.186]0.82071.091[0.540; 2.203]0.80750.00%0.6299
GCT vs RFAG30.248[0.100; 0.617]0.00270.25[0.099; 0.633]0.00350.00%0.5505
HGCT vs TACEG10.816[0.211; 3.159]0.76880.816[0.211; 3.159]0.7688NA<0.0001
GCT vs TACEG21.103[0.559; 2.174]0.77790.688[0.055; 8.540]0.771189.22%0.0023
PCT vs TACEG20.34[0.186; 0.622]0.00050.322[0.086; 1.206]0.092578.12%0.0325
2-year survival ratesHGCT vs GCT66.362[3.692; 10.963]<0.00015.681[2.138; 15.100]0.000558.25%0.0351
HGCT vs PCT54.396[2.442; 7.912]<0.00014.417[0.914; 21.340]0.064576.07%0.0022
GCT vs PCT33.708[1.480; 9.289]0.00523.621[1.428; 9.178]0.00670.00%0.7139
HGCT vs RFAG20.725[0.296; 1.774]0.48090.725[0.296; 1.775]0.4810.00%0.7939
GCT vs RFAG30.161[0.050; 0.513]0.0020.158[0.026; 0.958]0.044839.87%0.1895
HGCT vs TACEG10.468[0.136; 1.611]0.22840.468[0.136; 1.611]0.2284NA1
GCT vs TACEG20.702[0.291; 1.691]0.43030.488[0.001; 238.526]0.820291.40%0.0006
PCT vs TACEG20.849[0.238; 3.028]0.8010.732[0.025; 21.063]0.855577.22%0.0361
3-year survival ratesHGCT vs GCT94.354[2.355; 8.049]<0.00014.2[2.255; 7.824]<0.00010.00%0.8549
HGCT vs PCT64.765[2.379; 9.542]<0.00015.158[1.474; 18.055]0.010355.95%0.0449
GCT vs PCT33.764[0.849; 16.686]0.08113.751[0.843; 16.685]0.08260.00%0.9585
HGCT vs RFAG30.877[0.454; 1.695]0.69670.877[0.453; 1.698]0.6960.00%0.7758
GCT vs RFAG30.153[0.034; 0.687]0.01440.176[0.037; 0.842]0.02960.00%0.5427
HGCT vs TACEG10.988[0.248; 3.935]0.98660.988[0.248; 3.935]0.9866NA1
GCT vs TACEG20.332[0.067; 1.645]0.17680.454[0.011; 19.365]0.679965.83%0.0871
PCT vs TACEG10.139[0.006; 3.507]0.23090.139[0.006; 3.507]0.2309NA<0.0001

Summary estimates for 1-, 2-, 3-year survival rates in meta-analyses of direct comparisons between pairs of Liver-directed treatment and subgroup analysis of Asian for GCLM.

OR, Odds ratios; CI, confidence interval; FEM, Fixed-effect Model; REM, Random-effect Model; NA, not available..

I2: index for assessing heterogeneity; value ≥40% indicates a moderate to high heterogeneity.

Bold indicate statistically significant values (P < 0.05).

Network Meta-Analysis

The network evidence plot is shown in Figure 2. Five treatments were included for analysis; HGCT, GCT, PCT, RFAG and TACEG, respectively. Comparing the studies with regards to their OS, 1-, 2-, 3-year survival rates, HGCT had the highest number of related studies and number of patients, while RFAG had the least number of patients and TACEG had the least number of related studies.

Figure 2

The contribution plot is presented in Figure 3. Ten comparisons were made in the network analysis. All of them are mixed comparisons. In the overall contribution of network analysis, the remarkable influence evidence in the comparisons of 1-, 2-, 3-year survival rate is PCT vs. TACEG (19.9%), HGCT vs. RFAG (25.8%), GCT vs. PCT (22.2%), respectively.

Figure 3

There was no inconsistency between direct and indirect point estimates. In our network, there were 5 closed loops (Supplementary Figure 1). All confidence intervals for inconsistency factors (IFs) were compatible with zero inconsistency (IF=0) for all study outcomes (Supplementary Figure 1).

Network Comparison

The summary effects with 95% CI are shown in Figure 4. In the comparison of the 5 treatments for 1-year survival rate, HGCT and RFAG were found to be more effective than GCT and PCT, respectively. GCT and TACEG was found to be more effective than PCT while there was no difference between HGCT and RFAG (Figure 4A). In the comparison of 2-year survival rates, HGCT and RFAG were found to be more effective than GCT and PCT, respectively. Other comparisons did not exhibit any significant differences (Figure 4B). In the comparison of 3-year survival rate, HGCT and RFAG were found to be more effective than GCT and PCT, respectively. GCT and TACEG was found to be more effective than PCT while there was no difference between HGCT and RFAG (Figure 4C).

Figure 4

Ranking of Treatment

Figures 4D–F shows the relative ranking distribution of estimated cumulative probabilities for each treatment. The surface under the cumulative ranking curve (SUCRA) was adjusted by small-study effects. The SUCRA value rankings of 1-year survival rate were HGCT (83.8%), RFAG (81.5%), TACEG (51.7%), GCT (29.4%), and PCT (3.7%). The SUCRA value rankings of 2-year survival rate were RFAG (83%), HGCT (68.7%), TACEG (54%), GCT (30.9%), and PCT (13.4%). The SUCRA value rankings of 3-year survival rate were RFAG (77%), HGCT (72.9%), TACEG (72.4%), GCT (20.2%), and PCT (7.5%).

Network Comparison, Ranking of Treatment and Subgroup Analysis of OS

On OS analysis, four treatments (HGCT, GCT, RFAG, TACEG) showed an HR in favor of OS (HR range, 0.146-0.979) (Figure 5A) in Asian and Caucasian. RFAG was identified as the best option, based again on HR and the SUCRA (Figure 5B), followed by HGCT, TACEG, GCT and PCT. In Asian population, HGCT and RFAG had the most favorable HR (HGCT HR, 0.331 [95% CI, 0.230-0.490; RFAG HR, 0.265 [95% CI, 0.138-0.510]) (Figure 5C); they ranked, on median, first and second in all the simulations (Figure 5D).

Figure 5

Subgroup Analysis of Network Comparison in Asian Population

The summary effects in Asian population with 95% CI are shown in Figure 6. In the comparison of the 5 treatments for 1-, 3- year survival rate, HGCT and RFAG were found to be more effective than GCT and PCT, respectively. TACEG was found to be more effective than PCT while there was no difference between GCT and PCT (Figures 6A, C). In the comparison of 2-year survival rates, HGCT and RFAG were found to be more effective than GCT and PCT, respectively. Other comparisons did not exhibit any significant differences (Figure 6B). The SUCRA value rankings of 1-year survival rate were HGCT, RFAG, TACEG, GCT, and PCT (Figure 6D). The SUCRA value rankings of 2-, 3-year survival rate were RFAG, HGCT, TACEG, GCT, and PCT (Figures 6E, F).

Figure 6

Publication Bias

The funnel plot for network meta-analysis is presented in Figure 7. In general, all the selected studies were symmetrically distributed between the vertical line (x = 0). Therefore, there was no noteworthy publication bias in our network meta-analysis.

Figure 7

Discussion

In this network meta-analysis, we revealed that HGCT and RFAG and has remarkable survival benefits for GCLM patients when compared to TACEG, GCT and PCT. By OS and 2-, 3-year survival rate analysis, RFAG was identified as the best option, followed by HGCT, TACEG, GCT and PCT. By 1-year survival rate analysis, HGCT and RFAG were identified as the most effective options. Due to non-specific symptoms, majority of gastric cancer patients were initially diagnosed with distant metastases. GCLM was considered as IV stage. The NCCN guideline recommends systemic chemotherapy as the standard cure for this group of patients. However, some controversies have been reported in the past two decades. Kim et al. (59) reported that gastrectomy or gastrectomy plus hepatectomy in GCLM patients has survival benefits when compared to chemotherapy. Tsujimoto et al. (60) showed that the 5-year survival rate of GCLM patients after hepatic resection was 31.5%, median survival time was 34 months. They also found that gastric tumor less than 6 cm and D2 lymphadenectomy were important factors for prognosis. Song’s study (61) suggested that surgical hepatic resection is beneficial for long-term survival in selected patients, with a 3-year survival rate of 47.6%. Groundbreaking by survival benefits of combined conversion therapy with surgery in patients with colorectal cancer liver metastases, numerous general surgeons navigated HGCT or RFAG in GCLM, which was thought over as a crucial strategy to alleviation disease and to prolong patient life (6279). Liver-directed treatment (LDT) options for GCLM patients and surgical treatments were gradually attempted (12). If complete resection of liver metastases is possible, considering adequate hepatic reserve and surgical security, radical operations for primary gastric cancer and liver metastases lesions should be attempted (22, 79).

Considering the retrospective nature of the included studies and different selection biases for choosing patients on whom to perform radical surgery, their outcomes can hardly be regarded as a rationale in the treatment of GCLM, but it broadens the horizon of radical surgery in the selected GCLM patients. Furthermore, its prognostic value is considerable. Hepatic resection for liver metastases from colorectal cancer has been recommended as a standard treatment, 5-year survival was almost 40% (80). When the number of liver metastasis tumor ≤ 3, the diameter of single metastasis lesion ≤ 3 cm, the resection of primary gastric cancer and liver metastasis can also offer survival benefits in the GCLM patient (40). The security of surgical treatments for GCLM patients has also been confirmed. It does not enhance postoperative mortality (37). Studies also reported that GCLM patients with hepatectomy and gastrectomy exhibited favorable prognosis (2, 8185). Therefore, we have confidence in the survival benefits of surgical hepatic resection. The value of this surgical treatment option is worth considering. According to the SUCRA values of the NMA, HGCT exhibited remarkable 1-, 2-, or 3-year survival and OS outcomes. Hepatic resection has survival superiority for selected GCLM patients.

RFAG was superior to the other therapies in 2-, or 3-year survival rate and OS analysis in this NMA. RFA has been thought over a less invasive therapeutic choice for GCLM (86). RFA can be used combined with systemic treatments (chemotherapy, targeted treatment, and immunotherapy), surgeries, and radiotherapy. RFAG which was radiofrequency ablation and gastrectomy plus chemotherapy showed comparable outcomes to curative resection (12, 48, 87). Cheon’s study suggested that a survival benefit of RFAG with curative intent was observed as compared with GCT, as evidenced by an improvement of 20.8% in the 5-year survival rate, corresponding to a 64.0% reduction in the risk of death (48). Kim et al. reported that The RFAG group showed a 76% decreased death rate compared to the GCT, was received well, and was found to be minor complications (34). Guner et al. suggested that in select patients with GCLM, HGCT and RFA showed satisfactory and comparable short- and long-term results, possible liver-directed treatment options for GCLM patients should be considered on an individual basis (12). Tang et al. suggested that OS were satisfactory and comparable between RFA and HGCT but better than those of chemotherapy, RFA is an appropriate option for patients with gastric cancer who have a solitary liver metastasis measuring ≤3.0 cm (55).

In our network analysis, we adopted several methods to control potential bias. First, the quality of all included studies was assessed by the Newcastle-Ottawa scale. The contribution plot was then performed to seek for significant bias in the network analysis. HGCT and RFAG exhibited the most impact on the 1-, 2- and 3-year survival rates, with 19.6%, 25.8% and 22.2% respectively, which was attributed to the small number of included patients. We also applied the small-study effects to adjust the value of SUCRA to control for potential bias. There was a low risk of publication bias.

Our study had some limitations. The retrospective nature of the included studies enhances the possibility of selection bias between different centers. Patient characteristics such as the number and size of hepatic metastasis, the location of metastasis lesions, the postoperative supportive treatment and adjuvant chemotherapy, which are vital prognostic factors to influence the survival benefits in GCLM patients could hardly ensure balance. However, it is difficult to perform prospective cohort studies for this group of patients die to the small number of GCLM patients in single centers and dismal prognosis with systemic chemotherapy. Our results recommend the HGCT or RFAG treatment option for GCLM patients when resection of gastric cancer and liver metastases lesions is feasible. This recommendation is in tandem with those of the EORTC and JCOG studies. Liver resection or RFA is a favorable option for GCLM patients without extrahepatic metastases, peritoneal dissemination and multiple hepatic metastases (22). Meanwhile, the maximum liver metastatic tumor size for which RFA is safe and effective remains highly controversial (55, 88).

To sum up, HGCT was found to exhibit superior therapeutic effects for GCLM patients while RFAG was found to be a prospective therapeutic alternative. Although we obtained data from retrospective studies, we confirmed the role of RFAG and HGCT as a therapeutic option for GCLM. Large-scale prospective studies in multiple centers are needed to further evaluate the survival benefits of potential radical surgery or RFAG in selected patients.

Funding

This research was supported by the National Natural Science Foundation of China (81902498), Hubei Provincial Natural Science Foundation (2019CFB450, 2019CFB177, 2016CFB530), Natural Science Foundation of Hubei Provincial Department of Education (Q20182105), Chen Xiao-ping Foundation for the Development of Science and Technology of Hubei Provincial (CXPJJH11800001-2018333), The Scientific and Technological Project of Shiyan City of Hubei Province (18Y35), The Foundation of Health and Family Planning Commission of Hubei Province (WJ2021Q007), and Innovation and Entrepreneurship Training Program (201810929005, 201810929009, 201810929068, 201813249010, S201910929009, S201910929045, S202013249005, S202013249008 and 202010929009).

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Statements

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding authors.

Author contributions

Conceptualization: TL and MS. Data curation: MS, LX, and ZZ. Formal analysis: SW and HD. Funding acquisition: XG, LX, and MS. Methodology: TL and MS. Project administration: TL and HD. Resources: MS and HD. Software: MS, ZZ, and SW. Supervision: TL and HD. Validation: TL. Visualization: MS and LX. Writing - original draft: MS, ZZ, and TL. Writing - review & editing: HD and TL. All authors contributed to the article and approved the submitted version.

Acknowledgments

We thanked Home for Researchers editorial team (www.home-for-researchers.com) for language editing service.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fonc.2021.675870/full#supplementary-material

Supplementary Figure 1

Inconsistency plot for the network meta-analysis. (A) inconsistency plot of 1- year survival rate. (B) inconsistency plot of 2- year survival rate. (C) inconsistency plot of 3- year survival rate.

Abbreviations

GCLM, gastric cancer with liver metastasis; LDT, Liver-directed treatment; SUCRA, surface under the cumulative ranking curves; JGCA, Japan Gastric Cancer Association; NCCN, National Comprehensive Cancer Network; SCT, systemic chemotherapy; RoR, ratio of two odds ratios; IF, inconsistency factor; HGCT, hepatectomy and gastrectomy plus chemotherapy; GCT, gastrectomy plus chemotherapy; PCT, palliative chemotherapy; RFAG, radiofrequency ablation and gastrectomy plus chemotherapy; TACEG, transarterial chemoembolization and gastrectomy plus chemotherapy; HR, Hazard Ratio; NMA, network meta-analysis; OR, Odds ratio.

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Summary

Keywords

gastric cancer, liver metastasis, hepatectomy, interventional therapy, network analysis

Citation

Sun M, Ding H, Zhu Z, Wang S, Gu X, Xia L and Li T (2021) Identifying Optimal Surgical Intervention-Based Chemotherapy for Gastric Cancer Patients With Liver Metastases. Front. Oncol. 11:675870. doi: 10.3389/fonc.2021.675870

Received

04 March 2021

Accepted

08 November 2021

Published

29 November 2021

Volume

11 - 2021

Edited by

Dominik Wolf, Innsbruck Medical University, Austria

Reviewed by

Yongfeng He, Weill Cornell Medicine, United States; Chao Wang, Peking University People’s Hospital, China

Updates

Copyright

*Correspondence: Tian Li, ; ; Lingyun Xia,

†These authors have contributed equally to this work

This article was submitted to Gastrointestinal Cancers, a section of the journal Frontiers in Oncology

Disclaimer

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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