Artificial intelligence in pathological evaluation of gastrointestinal cancers

Table 3 Artificial intelligence-based applications in colorectal cancer

Ref.	Task	No. of cases/data set	Method	Performance
Xu et al[96]	Classification	717 patches (N, ADC subtypes)	AlexNet	Accuracy (97.5%)
Awan et al[97]		454 cases (N, ADC grades LG vs HG)	NN	Accuracy (97%, for 2-class; 91%, for 3-class)
Haj-Hassan et al[98]		30 multispectral image patches (N, AD, ADC)	CNN	Accuracy (99.2%)
Kainz et al[99]		165 images (benign vs malignant)	CNN (LeNet-5)	Accuracy (95%-98%)
Korbar et al[34]		697 cases (N, AD subtypes)	ResNet	Accuracy (93.0%)
Yoshida et al[100]		1328 colorectal biopsy WSIs	ML	Accuracy (90.1% for adenoma)
Wei et al[35]		326 slides (training), 25 slides (validation) 157 slides (internal set)	ResNet	157 slides: Accuracy 93.5% vs 91.4%(pathologists) 238 slides: Accuracy 87.0% vs 86.6%(pathologists)
Ponzio et al[101]		27 WSIs (13500 patches) (N, AD, ADC)	VGG16	Accuracy (96%)
Kather et al[47]		94 WSIs1	ResNet18	AUC (> 0.99)
Yoon et al[102]		57 WSIs (10280 patches)	VGG	Accuracy (93.5%)
Iizuka et al[33]		4036 WSIs (N, AD, ADC)	CNN/RNN	AUCs (0.96, ADC; 0.99, AD)
Sena et al[103]		393 WSIs (12565 patches) (N, HP, AD, ADC)	CNN	Accuracy (80%)
Bychkov et al[45]	Prognosis	420 cases	RNN	HR of 2.3, AUC (0.69)
Kather et al[46]		1296 WSIs	VGG19	Accuracy (94%-99%)
Kather et al[46]		934 cases	DL (comp. 5 networks)	HR for overall survival of 1.63-1.99
Geessink et al[104]		129 cases	NN	HR of 2.04 for disease free survival
Skrede et al [105]		2022 cases	Neural networks with MIL	HR 3.04
Kather et al[47]	Genetic alterations	TCGA-DX (93408 patches)1TCGA-KR (60894 patches)	ResNet18	AUC (0.77), TCGA-DXAUC (0.84), TCGA KR)
Echle et al[55]		8836 cases (MSI)	ShuffleNet DL	AUC (0.92-0.96 in two cohorts)
Kather et al[47]	Tumor microenvironment analysis	86 WSIs (100000)1	VGG19	Accuracy (94%-99%)
Shapcott et al[48]		853 patches and 142 TCGA images	CNN with a grid-based attention network	Accuracy (65-84% in two sets)
Swiderska-Chadaj et al[49]		28 WSIs	FCN/LSM/U-Net	Sensitivity (74.0%)
Alom et al[106]		21135 patches	DCRN/R2U-Net	Accuracy (91.9%)
Sirinukunwattana et al[107]	Molecular subtypes	1206 cases	NN with domain-adversarial learning	AUC (0.84-0.95 in the two validation sets)
Weis et al[50]	Tumor budding	401 cases	CNN	Correlation R (0.86)

¹Gastric, colorectal, esophageal, and liver cancers.

AI: Artificial intelligence; N: Normal; ADC: Adenocarcinoma; LG: Low grade; HG : High grade; NN: Neural networks; AD: Adenoma; CNN: Convolutional neural networks; WSIs: Whole slide images; ML: Machine learning; VGG: Visual geometry group; AUC: Area under the curve; RNN: Recurrent neural networks; HR: Hazard ratio; DL: Deep learning; MIL: Multi-instance learning; TCGA: The cancer genome Atlas; MSI: Microsatellite instability; FCN: Fully convolutional neural networks; LSM: Locally sensitive method; DCRN: Densely connected recurrent convolutional network; R2U-Net: Recurrent residual U-Net.