Table 1.
Main characteristics of studies included in the meta-analysis
| Study | Year | Country | Design | Study period | Group | Cases | Age | BMI (kg/m2) | Surgical extension |
|---|---|---|---|---|---|---|---|---|---|
| Zhang [21] | 2012 | China | R | 2009-2011 | RG | 97 | 56.1 ± 5.8 | 22.5 ± 3.6 | D, P, T |
| LG | 70 | 54.8 ± 4.9 | 21.7 ± 2.1 | ||||||
| Liu [22] | 2014 | China | R | 2012-2013 | RG | 100 | 66.4 ± 5.7 | 22.7 ± 1.8 | D, P, T |
| LG | 100 | 67.8 ± 4.8 | 23.1 ± 1.2 | ||||||
| Huang [23] | 2014 | China | R | 2008-2014 | RG | 72 | 67.7 ± 15.1 | 24.1 ± 3.3 | D, T |
| LG | 73 | 66 ± 13.5 | 24.2 ± 3.3 | ||||||
| Zhou [24] | 2014 | China | R | 2010-2013 | RG | 120 | 54.7 ± 10.1 | 21.6 ± 2.8 | D,P,T |
| LG | 394 | 55.6 ± 11.8 | 21.7 ± 2.6 | ||||||
| Son T [25] | 2014 | Korea | R | 2003-2010 | RG | 51 | 55.3 ± 12.2 | 22.7 ± 2.9 | T |
| LG | 58 | 58.8 ± 12.2 | 23.2 ± 3.3 | ||||||
| Han [26] | 2015 | Korea | R | 2008-2013 | RG | 68 | 50.6 ± 8.3 | 22.7 ± 2.4 | PPG |
| LG | 68 | 49.8 ± 11.5 | 22.8 ± 3 | ||||||
| Lee [27] | 2015 | Korea | R | 2003-2010 | RG | 133 | 53.6 ± 13.2 | 23.2 ± 2.7 | D |
| LG | 267 | 59.2 ± 11.7 | 23.7 ± 2.8 | ||||||
| Suda [28] | 2016 | Japan | R | 2009-2012 | RG | 88 | 63.5 ± 15.0 | 22.6 ± 4.6 | D, T |
| LG | 438 | 64.0 ± 15.8 | 23.1 ± 6.4 | ||||||
| Shen [29] | 2016 | China | R | 2011-2014 | RG | 93 | 56.8 ± 10.5 | 24.3 ± 3.3 | D, T |
| LG | 330 | 57.9 ± 11.5 | 23.8 ± 3.6 | ||||||
| Hong [30] | 2016 | Korea | R | 2008-2015 | RG | 232 | 53.7 ± 11.5 | 23.8 ± 3.3 | D, P |
| LG | 232 | 55.0 ± 13.0 | 23.8 ± 3.0 | ||||||
| La n[31] | 2017 | China | R | 2014-2016 | RG | 196 | 59.0 ± 11.6 | 23.6 ± 4.6 | D, P, T |
| LG | 673 | 59.0 ± 11.6 | 23.5 ± 4.5 | ||||||
| Zhang [32] | 2018 | China | R | 2011-2013 | RG | 70 | 58.0 ± 9.8 | 24.2 ± 3.4 | D, P, T |
| LG | 70 | 56.9 ± 12.1 | 23.2 ± 2.9 | ||||||
| Li [33] | 2018 | China | R | 2013-2017 | RG | 112 | 55.6 ± 11.3 | 23.6 ± 2.9 | D, T |
| LG | 112 | 56.1 ± 11.1 | 23.6 ± 3.0 | ||||||
| Obama [34] | 2018 | Japan | R | 2005-2009 | RG | 311 | 54.5 ± 12.6 | 23.6 ± 3.1 | D, T |
| LG | 311 | 54.8 ± 12.0 | 23.2 ± 2.8 | ||||||
| Gao [35] | 2019 | China | R | 2011-2014 | RG | 163 | 60.27 ± 10.50 | 23.77 ± 3.11 | D, T |
| LG | 163 | 59.88 ± 11.72 | 23.25 ± 3.26 | ||||||
| Sun [36] | 2019 | China | R | 2016-2018 | RG | 33 | 55.6 ± 10.3 | 22.38 ± 3.03 | D, T |
| LG | 88 | 54.7 ± 10.9 | 22.59 ± 2.95 | ||||||
| Ye [37] | 2020 | China | R | 2014-2019 | RG | 285 | 57.1 ± 8.3 | 24.4 ± 2.3 | D |
| LG | 285 | 57.0 ± 8.6 | 24.5 ± 2.2 | ||||||
| Kong [38] | 2020 | China | R | 2014-2017 | RG | 266 | 58.68 ± 10.54 | 24.23 ± 3.06 | D, P, T |
| LG | 532 | 58.92 ± 9.82 | 24.25 ± 3.34 | ||||||
| Cui [39] | 2020 | China | R | 2016-2019 | RG | 187 | 59.0 ± 10.5 | 24.1 ± 3.0 | D |
| LG | 334 | 57.2 ± 11.9 | 23.8 ± 3.4 |
R retrospectively collected data D distal gastrectomy, P proximal gastrectomy, T total gastrectomy, PPG pylorus-preserving gastrectomy, BMI body mass index, LG laparoscopic gastrectomy, RG robotic gastrectomy