Table 1.
Study characteristics and demographics.
| Author | Year | Study Type (LOE) | Radiomics | Country | US Device Brand | N Patients | Male | Female | Mean Age |
|---|---|---|---|---|---|---|---|---|---|
| Zhou | 2020 | RC (III) | CNN | China | Esaote/Phillips | 105 | 25 | 80 | 47.9 |
| Nguyen | 2019 | RC (III) | CNN | Korea | NS | 61 | NS | NS | NS |
| Wei | 2020 | RC (III) | CNN | China | NS | 2489 | 614 | 1875 | 45.3 |
| Park | 2019 | PC (II) | CNN | Korea | Samsung | 265 | 52 | 213 | 47.1 |
| Thomas | 2020 | RC (III) | CNN | USA | 4 brands | 103 | NS | NS | NS |
| Wei (2) | 2020 | RC (III) | Non-CNN | China | 5 brands | NS | NS | NS | 47 |
| Liu | 2019 | RC (III) | CNN | China | Vinno | 131 | 54 | 77 | 46.7 |
| Stib | 2020 | RC (III) | CNN | USA | Siemens/GE/Phillips | 571 | 234 | 337 | 52.9 |
| Ye | 2020 | RC (III) | CNN | China | 5 brands | 166 | 46 | 100 | 44.6 |
| Ma | 2020 | RC (III) | CNN | China | NS | 211 | 34 | 177 | NS |
| Koh | 2020 | RC (III) | CNN | Korea | 11 brands | 200 | 49 | 151 | 49.6 |
| Kwon | 2020 | RC (III) | CNN | Korea | Phillips/Hitachi | 762 | NS | NS | NS |
| Kim | 2019 | RC (III) | Non-CNN | Korea | Samsung | 106 | 29 | 77 | 48 |
| Zhao | 2020 | RC (III) | Non-CNN | China | Phillips/Hitachi | 174 | 44 | 130 | 45 |
| Qin | 2019 | RC (III) | CNN | China | NS | 233 | NS | NS | NS |
| Zhu | 2021 | RC (III) | CNN | China | 4 brands | 102 | 0 | 102 | 54.8 |
| Liu (2) | 2019 | RC (III) | CNN | China | GE | 376 | NS | NS | NS |
| Xia | 2019 | PC (II) | CNN | China | Samsung | 171 | 32 | 139 | 47.2 |
| Zhao | 2021 | RC (III) | Non-CNN | China | SuperSonic | 102 | 25 | 77 | 50.6 |
| Lee | 2019 | RC (III) | CNN | Korea | Phillips/Hitachi | 519 | 93 | 426 | 47.5 |
| Ataide | 2020 | RC (III) | Non-CNN | Germany | NS | 99 | NS | NS | NS |
| Chen | 2020 | RC (III) | Non-CNN | China | GE/Hitachi | 1480 | 302 | 1178 | 45.6 |
| Zhu (2) | 2021 | RC (III) | CNN | China | Phillips/GE/Toshiba | 261 | 64 | 197 | 52 |
| Shi | 2020 | RC (III) | CNN | China | Esaote/Hitachi/Toshiba | NS | NS | NS | NS |
| Barczyński | 2020 | PC (II) | CNN | Poland | Samsung | 50 | 9 | 41 | 47.5 |
| Zhang | 2020 | RC (III) | Non-CNN | Korea | Siemens | 303 | 59 | 244 | 46.4 |
| Wei (3) | 2020 | RC (III) | Non-CNN | China | Samsung | 181 | 35 | 146 | 46 |
| Colakoglu | 2019 | RC (III) | Non-CNN | Turkey | GE | 198 | 48 | 150 | 44.5 |
| Park | 2021 | RC (III) | Non-CNN | Korea | Phillips | 325 | 61 | 264 | 50.1 |
| Nguyen | 2020 | RC (III) | CNN | Korea | NS | 61 | NS | NS | NS |
| Sun | 2020 | RC (III) | CNN | China | GE | 338 | 134 | 416 | 43.8 |
| Peng | 2021 | PC (II) | CNN | China | 13 brands | 2775 | 726 | 2049 | 42.2 |
| Liu | 2021 | RC (III) | CNN | China | Siemens | 163 | 48 | 115 | 44.3 |
| Han | 2021 | RC (III) | CNN | Korea | Samsung | 372 | NS | NS | NS |
| Shin | 2020 | RC (III) | CNN | Korea | Samsung | 340 | 79 | 261 | 47.2 |
| Wang | 2019 | RC (III) | CNN | China | GE/Phillips | 276 | 53 | 223 | 46.3 |
| Zhu | 2019 | RC (III) | CNN | China | 4 brands | 467 | 97 | 370 | 45.3 |
| Zhang (2) | 2019 | RC (III) | Non-CNN | China | Hitachi | 2032 | 695 | 1337 | 42.3 |
| Ko | 2019 | RC (III) | CNN | Korea | Phillips/Hitachi | 150 | 23 | 127 | 49.7 |
| Song | 2019 | RC (III) | CNN | Korea | Toshiba | 100 | NS | NS | NS |
| Li | 2019 | RC (III) | CNN | China | Phillips/Toshiba/GE | 154 | 34 | 120 | 51 |
| Wildman-Tobriner | 2019 | RC (III) | Non-CNN | UK | Siemens/GE/Phillips | 94 | 21 | 73 | 52.6 |
| Yu | 2017 | PC (II) | CNN | China | Phillips/Siemens | 50 | 9 | 41 | 48.4 |
| Buda | 2019 | RC (III) | CNN | USA | Siemens/GE/Phillips | 91 | NS | NS | 52.3 |
| Raghavendra | 2018 | RC (III) | Non-CNN | India | 4 brands | 344 | NS | NS | 44.1 |
| Li | 2018 | RC (III) | CNN | China | NS | 300 | 53 | 247 | NS |
| Ma | 2017 | RC (III) | CNN | china | 7 brands | 4782 | NS | NS | 52 |
| Raghavendra | 2017 | RC (III) | Non-CNN | India | GE | 242 | 63 | 179 | 44.1 |
| Zhu | 2013 | RC (III) | CNN | China | Siemens | 618 | 161 | 528 | 47.7 |
| Choi | 2017 | PC (II) | Non-CNN | Korea | Samsung | 89 | 18 | 71 | 43.5 |
| Gao | 2017 | RC (III) | CNN | China | Phillips/GE | 342 | 70 | 272 | 44.8 |
| Choi | 2015 | RC (III) | CNN | Korea | Phillips | 85 | 24 | 61 | 52 |
| Chi | 2017 | RC (III) | CNN | Canada | Toshiba | 61 | NS | NS | NS |
| Jeong | 2019 | PC (II) | CNN | Korea | Samsung | 76 | NS | NS | NS |
| Acharya | 2012 | RC (III) | Non-CNN | Singapore | NS | 20 | 10 | 10 | NS |
| Liang | 2018 | RC (III) | Non-CNN | China | Phillips | 95 | 20 | 75 | 43.2 |
| Prochazka (2) | 2019 | RC (III) | Non-CNN | Czechia | Phillips/GE | 60 | 11 | 49 | 55.7 |
| Song | 2015 | RC (III) | Non-CNN | China | GE | 147 | 32 | 115 | NS |
| Ardakani | 2015 | RC (III) | Non-CNN | Iran | Medison | 60 | NS | NS | NS |
| Guan | 2019 | RC (III) | CNN | China | NS | 399 | NS | NS | NS |
| Xia | 2017 | RC (III) | Non-CNN | China | Siemens | 187 | 36 | 151 | 50.8 |
| Yoo | 2018 | PC (II) | CNN | Korea | Samsung | 50 | 10 | 40 | 43.2 |
| Tsantis | 2009 | RC (III) | Non-CNN | Greece | Phillips | 85 | NS | NS | NS |
| Liu | 2008 | RC (III) | Non-CNN | USA | NS | 37 | NS | NS | NS |
| Acharya | 2013 | RC (III) | Non-CNN | Italy | Esaote | 20 | 10 | 10 | 52.8 |
| Acharya (2) | 2012 | RC (III) | Non-CNN | Italy | Esaote | 20 | 10 | 10 | 52.8 |
| Acharya | 2011 | RC (III) | Non-CNN | Italy | Esaote | 20 | 10 | 10 | 52.8 |
| Kweon Seo | 2017 | RC (III) | CNN | Korea | NS | 230 | 51 | 179 | 48.7 |
| Ardakani (2) | 2015 | RC (III) | CNN | Iran | Medison | 60 | NS | NS | NS |
| Wu | 2016 | RC (III) | CNN | China | Phillips | 970 | 214 | 756 | 46.7 |
| Cao | 2019 | RC (III) | Non-CNN | China | NS | 120 | NS | NS | NS |
| Wang (2) | 2020 | RC (III) | CNN | China | NS | 1040 | NS | NS | NS |
| Sun (2) | 2020 | RC (III) | CNN | China | NS | 245 | NS | NS | NS |
| Reverter | 2019 | RC (III) | Non-CNN | Spain | GE | 300 | 45 | 255 | 55.5 |
| Gitto | 2019 | RC (III) | Non-CNN | Italy | Samsung | 62 | 12 | 50 | 60 |
NS: not specified, LOE: level of evidence, RC: retrospective cohort, PC: prospective cohort, CNN: convolutional neural network, non-CNN: analysis performed using a method other than a convolutional neural network, GE: General Electric.