Table 2.
Characteristics of the artificial intelligence models in endometrial, gastric, and ovarian cancers.
| Organ /Cancers |
Author | Year | Country | AI-Based Model | Data Set/WSIs/No. of Patients (n) | Pixel Level | Additional Methodology for Validating MSI | Performance Metrics | External Validation Dataset/WSIs/No. of Patients (n) | External Validation Result | Ref. |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Endometrial cancer | Zhang | 2018 | USA | Inception-V3 | TCGA-UCEC and CRC/1141/NC | 1000 × 1000 | NC | ACC: 84.2% | NS | NS | [51] |
| Kather | 2019 | Germany | ResNet18 | TCGA-FFPE/NC/492 | NC | PCR | AUC: 0.75 | NS | NS | [29] | |
| Wang | 2020 | China | ResNet18 | TCGA/NC/516 | 512 × 512 | NC | AUC: 0.73 | NS | NS | [59] | |
| Hong | 2021 | USA | InceptionResNetVI | TCGA, CPTAC/496/456 | 299 × 299 | PCR/NGS | AUC: 0.82 | NYU-H/137/41 | AUC: 0.66 | [57] | |
| Gastric cancer | Kather | 2019 | Germany | ResNet18 | TCGA-FFPE/NC/315 | NC | PCR | AUC: 0.81 | KCCH-FFPE-Japan/NC/185 | AUC: 0.69 | [29] |
| Zhu | 2020 | China | ResNet18 | TCGA-FFPE/285/NC | NC | NC | AUC: 0.80 | NS | NS | [55] | |
| Schmauch | 2020 | USA | ResNet50 | TCGA/323/NC | 224 × 224 | PCR | AUC: 0.76 | NS | NS | [54] | |
| Ovarian cancer | Zeng | 2021 | China | Random forest | TCGA/NC/229 | 1000 × 1000 | NC | AUC: 0.91 | NS | NS | [58] |
Abbreviations: AI, artificial intelligence; DL, Deep learning; WSIs, whole slide images; TCGA, The Cancer Genome Atlas; CPTAC, Clinical Proteomic Tumor Analysis Consortium; CRC, Colorectal Cancer; UCEC, Uterine Corpus Endometrial Carcinoma; NYU-H, New York University-Hospital; KCCH-Japan, Kanagawa Cancer Centre Hospital-Japan; ACC, accuracy; AUC, area under the ROC curve; NC, not clear; NS, not specified.