Table 4 Performance data on studies for early detection of EC using a ML
From: Machine learning applications for early detection of esophageal cancer: a systematic review
Author | Cancer Type | Modality | algorithm | image | patient | AUROC | accuracy | sensitivity | specificity |
|---|---|---|---|---|---|---|---|---|---|
Lou et al. | EAC & ESCC | CT | U-Net | 80 | - | - | - | - | - |
Ghatwary et al. | EAC & ESCC | WLI | Faster R-CNN SSD | 100 | 39 | - | - | 96% | 92% |
Tang et al. | EAC & ESCC | WLI | MTCS | 805 | 255 | - | 93.43% | 92.82% | 96.20% |
Ghatwary et al. | EAC | - | Faster R-CNN | 1000 | - | - | - | - | - |
Yu et al. | EAC & ESCC | endoscopy images | MTL | 1003 | - | - | 96.96% | 95.64% | 97.70% |
Wu et al. | EAC & ESCC | WLI | Faster-RCNN DSN | 1051 | - | - | 96.28% | 90,34% | 97,18% |
Liu et al. | EAC & ESCC | WLI | CNN | 1272 | 748 | - | 85.83% | 94.23% | 94.67% |
Groof et al. | EAC | WLI | hybrid ResNet-Unet | 1704 | 669 | - | 89% | 90% | 88% |
Tang et al. | ESCC | - | DCNN | 4002 | 1078 | 95,4% | 91.30% | 97.9% | 88.6% |
Meng et al. | ESCC | WLI | YOLO v5 | 4447 | 837 | 98,2% | 92.9% | 91.90% | 94.7% |
Gong et al. | EAC & ESCC | WLI | No-code deep-learning tool “Neuro-T” version 2.3.2 | 5162 | - | 95% | 95.6% | - | - |
Shiroma et al. | ESCC | WLI & NBI | SSD | 8428 | - | - | 98% | 100% | 100% |
Du et al. | EAC & ESCC | - | RWS ECA-DDCNN | 20,965 | 4,077 | 98.77% | 90.63% | - | - |
Putten et al. | EAC | endoscopy images | U-Net Transfer Learning | 494,356 | - | - | 87.50% | 92.50% | 82.50% |
Gan et al. | EAC & ESCC | OCT image | D-UCN | - | - | - | 98% | - | - |
Wang et al. | EAC & ESCC | endoscopy & ultrasound | Cascade RCNN | - | 80 | - | 83% | - | - |
Sui et al. | EAC & ESCC | CT | V-Net | - | 414 | - | 65% | 88.80% | 90.90% |
Takeuchi et al. | EAC & ESCC | CT | CNN- VGG16 | - | 457 | - | 84.20% | 71.70% | 90.00% |
Ghatwary et al. | EAC & ESCC | video | 3DCNN | - | - | - | 91.10% | - | - |
Alharbe et al. | EAC & ESCC | image | Deep transfer learning | - | - | - | 99.7% | 99.49% | 99.78% |
Zhao et al. | EAC & ESCC | digestive endoscopy | Google Net V3 TensorFlow 1.6 | - | 300 | 91% | 91.00% | 90.00% | 92.0% |
Collins et al. | EAC & ESCC | - | SVM, MLP, 3DCNN | - | 10 | 93% | - | - | - |
Zhao et al. | EAC & ESCC | - | CNN | - | 500 | - | - | 98% | 99,6% |
Chen et al. | EAC & ESCC | - | Faster RCNN | 1520 | 421 | - | 92.15% | - | - |
Tsai et al. | EAC & ESCC | WLI & NBI | SSD VGG-16 | 155 153 | - | - | 86% | 92% | - |
Tsai et al. | EAC & ESCC | WLI | SSD | 1780 | - | - | 96.1% | 81.6% | - |
Sali et al. | EAC | whole-slide tissue histopathology images (WSIs) | ResNet34 | 387 | 130 | - | - | - | - |
Wang et al. | EAC & ESCC | WLI & NBI | SSD | 498 438 | - | - | 90.90% | 96.20% | 70.40% |
Zhang et al. | EAC & ESCC | - | Faster R-CNN VGG16 | 6445 | 200 | - | 90.3% | 92.5% | 88.70% |
Guo et al. | ESCC | NBI | SegNet | 6473 | - | - | - | 98.04% | 95.03% |
Fang et al. | EAC & ESCC | WLI & NBI | U-Net | 75 91 | - | - | 84.72% | - | - |