Table 3.
Summary of key studies on artificial intelligence-assisted pathology in the gastroenterology and hepatology fields
| Ref. | Country | Disease studied | Design of study | Application | Number of cases | Type of machine learning algorithm |
Outcomes (%)
|
|
|
Accuracy
|
Sensitivity/Specificity
|
|||||||
| Basic AI-based pathology: diagnosis | ||||||||
| Tomita et al[118], 2019 | United States | BE and EAC | Retrospective | Detection and classification of cancerous and precancerous esophagus tissue | Training: 379 images with 4 classes: normal, BE-no-dysplasia, BE-with-dysplasia, and adenocarcinoma; Testing: 123 images with 4 classes: normal, BE-no-dysplasia, BE-with-dysplasia, and adenocarcinoma | CNNs | Mean: 83; BE-no-dysplasia: 85; BE-with-dysplasia: 89; Adenocarcinoma: 88 | Normal: 69/71 BE-no-dysplasia: 77/88; BE-with-dysplasia: 21/97; Adenocarcinoma: 71/91 |
| Sharma et al[119], 2017 | Germany | GC | Retrospective | Classification and necrosis detection of GC | 454 patients (6810 WSIs: 4994 for cancer classification and 1816 for necrosis detection) (HER2 immunohistochemical stain and HE stained) | CNNs | Cancer classification: 69.90; Necrosis detection: 81.44 | NA/NA |
| Li et al[120], 2018 | China | GC | Retrospective | Detection of GC | 700 images: 560 GC and 140 normal (HE stained) | CNNs | 100 | NA/NA |
| Leon et al[121], 2019 | Colombia | GC | Retrospective | Detection of GC | 40 images: 20 benign and 20 malignant | CNNs | 89.72 | NA/NA |
| Sun et al[122], 2019 | China | GC | Retrospective | Diagnosis of GC | 500 WSIs of gastric areas with typical cancerous regions | DNNs | 91.6 | NA/NA |
| Ma et al[123], 2020 | China | GC | Retrospective | Classification of lesions in the gastric mucosa | Training: 534 WSIs (1616713 images: 544925 normal, 544624 chronic gastritis, and 527164 cancer) (HE stained) Testing: 153 WSIs (399240 images: 135446 normal, 125783 chronic gastritis, and 138011 cancer) (HE stained) | CNNs, RF | Benign and cancer: 98.4; Normal, chronic gastritis, and GC: 94.5 | Benign and cancer: 98.0/98.9; Normal, chronic gastritis, and GC: NA/NA |
| Yoshida et al[124], 2018 | Japan | Gastric lesions | Retrospective | Classification of gastric biopsy specimens | 3062 gastric biopsy specimens (HE stained) | CNNs | 55.6 | 89.5/50.7 |
| Qu et al[125], 2018 | Japan | Gastric lesions | Retrospective | Classification of gastric pathology images | Training: 1080 patches: 540 benign and 540 malignant; Testing: 5400 patches: 2700 benign and 2700 malignant | CNNs | 96.5 | NA/NA |
| Iizuka et al[126], 2020 | Japan | Gastric and colonic epithelial tumors | Retrospective | Classification of gastric and colonic epithelial tumors | 4128 cases of human gastric epithelial lesions and 4036 of colonic epithelial lesions (HE stained) | CNNs, RNNs | Gastric adenocarcinoma: 97; Gastric adenoma: 99; Colonic adenocarcinoma: 96; Colonic adenoma: 99 | NA/NA |
| Korbar et al[127], 2017 | United States | Colorectal polyps | Retrospective | Classification of different types of colorectal polyps on WSIs | Training: 458 WSIs; Testing: 239 WSIs | A modified version of a residual network | 93 | 88.3/NA |
| Wei et al[128], 2020 | United States | Colorectal polyps | Retrospective | Classification of colorectal polyps on WSIs | Training: 326 slides with colorectal polyps: 37 tubular, 30 tubulovillous or villous, 111 hyperplastic, 140 sessile serrated, and 8 normal; Testing: 238 slides with colorectal polyps: 95 tubular, 78 tubulovillous or villous, 41 hyperplastic, and 24 sessile serrated | CNNs | Tubular: 84.5; Tubulovillous or villous: 89.5; Hyperplastic: 85.3; Sessile serrated: 88.7 | Tubular: 73.7/91.6; Tubulovillous or villous: 97.6/87.8; Hyperplastic: 60.3/97.5; Sessile serrated: 79.2/89.7 |
| Shapcott et al[129], 2018 | UnitedKingdom | CRC | Retrospective | Diagnosis of CRC | 853 hand-marked images | CNNs | 84 | NA/NA |
| Geessink et al[130], 2019 | Netherlands | CRC | Retrospective | Quantification of intratumoral stroma in CRC | 129 patients with CRC | CNNs | 94.6 | 91.1/99.4 |
| Song et al[131], 2020 | China | CRC | Retrospective | Diagnosis of CRC | Training: 177 slides: 156 adenoma and 21 non-neoplasm; Testing: 362 slides: 167 adenoma and 195 non-neoplasm | CNNs | 90.4 | 89.3/79.0 |
| Wang et al[132], 2015 | China | Hepatic fibrosis | Retrospective | Assessment of HBV-related liver fibrosis and detection of liver cirrhosis | Training: 105 HBV patients; Testing: 70 HBV patients | SVM | 82 | NA/NA |
| Forlano et al[133], 2020 | UnitedKingdom | MAFLD | Retrospective | Detection and quantification of histological features of MAFLD | Training: 100 MAFLD patients; Testing: 146 MAFLD patients | K-means | Steatosis: 97; Inflammation: 96; Ballooning: 94; Fibrosis: 92 | NA/NA |
| Li et al[134], 2017 | China | HCC | Retrospective | Nuclei grading of HCC | 4017 HCC nuclei patches | CNNs | 96.7 | G1: 94.3/97.5; G2: 96.0/97.0;G3: 97.1/96.6; G4: 99.5/95.8 |
| Kiani et al[135], 2020 | United States | Liver cancer (HCC and CC) | Retrospective | Histopathologic classification of liver cancer | Training: 70 WSIs: 35 HCC and 35 CC Testing: 80 WSIs: 40 HCC and 40 CC | SVM | 84.2 | 72/95 |
| Advanced AI-based pathology: prediction of gene mutations and prognosis | ||||||||
| Steinbuss et al[136], 2020 | Germany | Gastritis | Retrospective | Identification of gastritis subtypes | Training: 92 patients (825 images: 398 low inflammation, 305 severe inflammation, and 122 A gastritis) (HE stained) Testing: 22 patients (209 images: 122 low inflammation, 38 severe inflammation, and 49 A gastritis) (HE stained) | CNNs | 84 | A gastritis: 88/89; B gastritis: 100/93; C gastritis: 83/100 |
| Liu et al[137], 2020 | China | Gastrointestinal neuroendocrine tumor | Retrospective | Prediction of Ki-67 positive cells | 12 patients (18762 images: 5900 positive cells, 6086 positive cells, and 6776 background from ROIs) (HE and IHC stained) | CNNs | 97.8 | 97.8/NA |
| Kather et al[138], 2019 | Germany | GC and CRC | Retrospective | Prediction of MSI in GC and CRC | Training: 360 patients (93408 tiles); Testing: 378 patients (896530 tiles) | CNNs | 84 | NA/NA |
| Bychkov et al[139], 2018 | Finland | CRC | Retrospective | Prediction of CRC outcome | 420 CRC tumor tissue microarray samples | CNNs, RNNs | 69 | NA/NA |
| Kather et al[140], 2019 | Germany | CRC | Retrospective | Prediction of survival from CRC histology slides | Training: 86 CRC tissue slides (> 100000 HE image patches); Testing: 25 CRC patients (7180 images) | CNNs | 98.7 | NA/NA |
| Echle et al[141], 2020 | Germany | CRC | Retrospective | Detection of dMMR or MSI in CRC | Training: 5500 patients; Testing: 906 patients | A modified shufflenet DL system | 92 | 98/52 |
| Skrede et al[142], 2020 | 3R23 Song 2020 | CRC | Retrospective | Prediction of CRC outcome after resection | Training: 828 patients (> 12000000 image tiles); Testing: 920 patients | CNNs | 76 | 52/78 |
| Sirinukunwattana et al[143], 2020 | UnitedKingdom | CRC | Retrospective | Identification of consensus molecular subtypes of CRC | Training: 278 patients with CRC; Testing: 574 patients with CRC: 144 biopsies and 430 TCGA | Neural networks with domain-adversarial learning | Biopsies: 85; TCGA: 84 | NA/NA |
| Jang et al[144], 2020 | South Korea | CRC | Retrospective | Prediction of gene mutations in CRC | Training: 629 WSIs with CRC (HE stained) Testing: 142 WSIs with CRC (HE stained) | CNNs | 64.8-88.0 | NA/NA |
| Chaudhary et al[145], 2018 | United States | HCC | Retrospective | Identification of survival subgroups of HCC | Training: 360 HCC patients’ data using RNA-seq, miRNA-seq and methylation data from TCGA; Testing: 684 HCC patients’ data (LIRI-JP cohort: 230; NCI cohort: 221; Chinese cohort: 166, E-TABM-36 cohort: 40, and Hawaiian cohort: 27) | DL | LIRI-JP cohort: 75; NCI cohort: 67; Chinese cohort: 69; E-TABM-36 cohort: 77; Hawaiian cohort: 82 | NA/NA |
| Saillard et al[146], 2020 | France | HCC | Retrospective | Prediction of the survival of HCC patients treated by surgical resection | Training: 206 HCC (390 WSIs); Testing: 328 HCC (342 WSIs) | CNNs (SCHMOWDER and CHOWDER) | SCHMOWDER: 78; CHOWDER: 75 | NA/NA |
| Chen et al[11], 2020 | China | HCC | Retrospective | Classification and gene mutation prediction of HCC | Training: 472 WSIs: 383 HCC and 89 normal liver tissue; Testing: 101 WSIs: 67 HCC and 34 normal liver tissue | CNNs | Classification: 96.0; Tumor differentiation: 89.6; Gene mutation: 71-89 | NA/NA |
| Fu et al[147], 2020 | UnitedKingdom | EAC, GC, CRC, and liver cancers | Retrospective | Prediction of mutations, tumor composition and prognosis | 17335 HE-stained images of 28 cancer types | CNNs | Variable across tumors/gene alterations | NA/NA |
AI: Artificial intelligence; BE: Barrett’s esophagus; EAC: Esophageal adenocarcinoma; CNN: Convolutional neural network; GC: Gastric cancer; WSI: Whole-slide image; NA: Not available; DNN: Deep neural network; RF: Random forests; RNN: Recurrent neural network; CRC: Colorectal cancer; HBV: Hepatitis-B virus; SVM: Support vector machine; MAFLD: Metabolic associated fatty liver disease; HCC: Hepatocellular carcinoma; CC: Cholangiocarcinoma; ROI: Region of interest; IHC: Immunohistochemistry; MSI: Microsatellite instability; dMMR: Mismatch-repair deficiency; TCGA: The Cancer Genome Atlas; DL: Deep learning.