Requirements for implementation of artificial intelligence in the practice of gastrointestinal pathology

Table 2 Artificial intelligence applications in colorectal cancer pathology

Ref.	Task	No. of cases/data set	Machine learning method	Performance
Xu et al[38]	Tumor classification: 6 classes (NL/ADC/MC/SC/PC/CCTA)	717 patches	AlexNet	Accuracy (97.5%)
Awan et al[87]	Tumor classification: Normal/Low-grade cancer/High-grade cancer	454 cases	Neural networks	Accuracy (97%, for 2-class; 91%, for 3-class)
Haj-Hassan et al[37]	Tumor classification: 3 classes (NL/AD/ADC)	30 multispectral image patches	CNN	Accuracy (99.2%)
Kainz et al[88]	Tumor classification: Benign/Malignant	165 images	CNN (LeNet-5)	Accuracy (95%-98%)
Korbar et al[36]	Tumor classification: 6 classes (NL/HP/SSP/TSA/TA/TVA-VA)	697 cases	ResNet	Accuracy (93.0%)
Yoshida et al[35]	Tumor classification	1328 colorectal biopsy WSIs	ML	Accuracy (90.1%, adenoma)
Alom et al[89]	Tumor microenvironment analysis: Classification, Segmentation and Detection	21135 patches	DCRN/R2U-Net	Accuracy (91.1%, classification)
Bychkov et al[42]	Prediction of colorectal cancer outcome (5-yr disease-specific survival).	420 cases	Recurrent neural networks	HR of 2.3, AUC (0.69)
Weis et al[90]	Evaluation of tumor budding	401 cases	CNN	Correlation R (0.86)
Ponzio et al[91]	Tumor classification: 3 classes (NL/AD/ADC)	27 WSIs (13500 patches)	VGG16	Accuracy (96 %)
Kather et al[34]	Tumor classification: 2 classes (NL/Tumor)	94 WSIs	ResNet18	AUC (> 0.99)
Kather et al[34]	Prediction of microsatellite instability	360 TCGA- DX (93408 patches), 378 TCGA- KR (60894 patches)	ResNet18	AUC: TCGA-DX—(0.77, TCGA-DX; 0.84, TCGA-KR)
Kather et al[26]	Tumor microenvironment analysis: classification of 9 cell types	86 WSIs (100000)	VGG19	Accuracy (94%-99%)
Kather et al[26]	Prognosis predictions	1296 WSIs	VGG19	Accuracy (94%-99%)
Kather et al[26]	Prognosis prediction	934 cases	Deep learning (comparison of 5 networks)	HR for overall survival of 1.99 (training set) and 1.63 (test set)
Geessink et al[29]	Prognosis prediction, quantification of intratumoral stroma	129 cases	Neural networks	HRs of 2.04 for disease-free survival
Sena et al[40]	Tumor classification: 4 classes (NL/HP/AD/ADC)	393 WSIs (12,565 patches)	CNN	Accuracy (80%)
Shapcott et al[92]	Tumor microenvironment analysis: detection and classification	853 patches and 142 TCGA images	CNN with a grid-based attention network	Accuracy (84%, training set; 65%, test set)
Sirinukunwattana et al[31]	Prediction of consensus molecular subtypes of colorectal cancer	1206 cases	Neural networks with domain-adversarial learning	AUC (0.84 and 0.95 in the two validation sets)
Swiderska-Chadaj et al[93]	Tumor Microenvironment Analysis: Detection of immune cell, CD3+, CD8+	28 WSIs	FCN/LSM/U-Net	Sensitivity (74.0%)
Yoon et al[39]	Tumor classification: 2 classes (NL/Tumor)	57 WSIs (10280 patches)	VGG	Accuracy (93.5%)
Echle et al[46]	Prediction of microsatellite instability	8836 cases	ShuffleNet Deep learning	AUC (0.92 in development cohort; 0.96 in validation cohort)
Iizuka et al[22]	Tumor classification: 3 classes (NL/AD/ADC)	4036 WSIs	CNN/RNN	AUCs (0.96, ADC; 0.99, AD)
Skrede et al[28]	Prognosis predictions	2022 cases	Neural networks with multiple instance learning	HR (3.04 after adjusting for established prognostic markers)

NL: Normal mucosa; ADC: Adenocarcinoma; MC: Mucinous carcinoma; SC: Serrated carcinoma; PC: Papillary carcinoma; CCTA: Cribriform comedo-type adenocarcinoma; AD: Adenoma; CNN: Convolutional neural network; HP: Hyperplastic polyp; SSP: Sessile serrated polyp; TSA: Traditional serrated adenoma; TA: Tubular adenoma; TVA: Tubulovillous adenoma; VA: Villous adenoma; WSI: Whole slide images; ML: Machine learning; DCRN: Densely connected recurrent convolutional network; R2U-Net: Recurrent residual U-Net; HR: Hazard ratio; AUC: Area under the curve; TCGA: The Cancer Genome Atlas; ResNet: Residual network; VGG: Visual geometry group; RNN: Recurrent neural network; FCN: Fully convolutional networks; LSM: Locality-sensitive method.