Table 3. The characteristics of the included studies regarding DL models for identifying tumor patterns or variety of cells on digital slides.
| Authors | Publication year | Number of datasets | Number of cases | Number of images | Subtypes (images) | Training set (images/cells) | Validation set (images/cells) | Test set (images/cells) | Independent test datasets (images/cells) |
Classifier | Results | Conclusion | ||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ACC | SN | SP | AUC | Precision | ||||||||||||
| Gertych et al. (38) | 2019 | 3 | 110 | 206 | CSMC: ADC (n=91); MIMW: ADC (n=88); TCGA: ADC (n=27) |
78 | 19 | 109 | 0 | GoogLeNet, ResNet-50 and AlexNet | FT-AlexNet:75.3%; DN-AlexNet-1: 89.90%; GoogLeNet-2: 85.84%; Resnet-50-3: 87.64% |
NR | NR | NR | NR | One of the DN-AlexNets obtained the best performance than other CNNs, with the accuracies of 89.90% for classification involving the five tissue classes in test set |
| Wei et al. (12) | 2019 | 1 | NR | 422 | DHMC: ADC (n=422) | 245 | 34 | 143 | 0 | ResNet | NR | NR | NR | Lepidic: 0.988; acinar: 0.970; papillary: 0.993; micropapillary: 0.981; solid: 0.997; benign: 0.988 |
NR | CNN could improve classification accuracy of ADC patterns by automatically pre-screening, superior to pathologists |
| Wang et al. (7) | 2020 | 2 | 507 | 639 | TCGA: ADC (n=208); NLST: ADC (n=431) |
12,000 cell nuclei | 1,227 cell nuclei | 1,086 cell nuclei | 0 | Mask R-CNN, Cox proportional hazard prognostic model | 88% in the validation set; 90% in the testing set. | NR | NR | NR | NR | Mask R-CNN extracted and identified 48 cell spatial features, which could predict high-risk group, significantly worse survival than the low-risk group |
| AbdulJabbar et al. (41) | 2020 | 2 | 1,070 | 4,599 | TRACERx: NSCLC (n=275); LATTICe-A: ADC (n=4,324) |
16790 H&E cells and 9333 IHC cells | 4219 H&E cells | 5951 H&E cells and 5028 IHC cells | 5082 H&E cells | SCCNN | Lymphocyte: 0.942; tumor: 0.933; other: 0.917; stromal: 0.936 |
Lymphocyte: 0.902; tumor: 0.936; other: 0.853; stromal: 0.898 |
Lymphocyte: 0.982; tumor: 0.930; other: 0.981; stromal: 0.973 |
NR | NR | SCCNN for NSCLCs exhibited high accuracy of single-cell classification in H&E digital slides and T-cell identification in the IHC image slides, respectively |
| Wang et al. (40) | 2019 | 3 | NR | 159 | TCGA and NLST: ADC (n=29); SPORE: ADC (n=130) |
29 | 130 | 0 | 0 | DL-based ConvPath software | Lymphocytes: 99.3%; stromal cells: 87.9%; tumor cells: 91.6%; overall: 92.9% |
NR | NR | NR | NR | The overall classification accuracies of the CNN in both datasets were 99.3% for lymphocytes, 87.9% for stromal cells, and 91.6% for tumor cells, respectively |
| Teramoto et al. (42) |
2020 | 1 | 60 | 793 | Normal (n=25); malignant (n=35) |
NR | 173 | NR | 0 | PGGAN, DCGAN, ImageNet | ImageNet: 0.810; DCGAN: 0.795; PGGAN: 0.853 |
ImageNet: 0.850; DCGAN: 0.793; PGGAN: 0.854 |
ImageNet: 0.768; DCGAN: 0.797; PGGAN: 0.853 |
NR | NR | PGGAN for cytological specimens improved the classification specificity by 8.5% and the total classification accuracy by approximately 4.3% compared to a CNN model |
| Saha et al. (43) | 2021 | 1 | 712 | 712 | TCGA: ADC (n=356); SCC (n=356) |
356 | 160 | 160 | 0 | TilGAN | 0.98 | 0.96 | NR | NR | 0.98 | TilGAN generated the high quality of synthetic pathology images could efficiently classify real TIL and non-TIL patches with improved accuracy |
NR, not reported; CNN, convolutional neural networks; DL, deep learning; SCCNN, sensitive convolutional neural networks; ADC, adenocarcinoma; NSCLC, non-small cell lung cancer; TCGA, The Cancer Genome Atlas; CSMC, Cedars-Sinai Medical Center; MIMW, the Military Institute of Medicine in Warsaw; DHMC, the Dartmouth-Hitchcock Medical Center; NLST, the National Lung Screening Trial project; SPORE, the University of Texas Special Program of Research Excellence; H&E, hematoxylin and eosin; IHC, immunohistochemistry; ACC, accuracy; SN, sensitivity; SP, specificity; GAN, generative adversarial network; PGGAN, progressive growing of GAN; DCGAN, deep convolutional generative adversarial network; TIL, tumor-infiltrating lymphocyte.