Artificial intelligence in pathological evaluation of gastrointestinal cancers

Table 1 Strengths and weaknesses of machine learning methods in development of artificial intelligence models for gastrointestinal pathology

AI model	Advantages	Disadvantages
Traditional ML (supervised)	Allows users to produce a data output from the previously labeled training set	Labeling big data can be time-consuming and challenging
	Users can reflect domain knowledge features	Accuracy depends heavily on the quality of feature extraction
Traditional ML (unsupervised)	Users do not label any data or supervise the model	Input data is unknown and not labeled by users
	Can detect patterns automatically	Users cannot get precise information regarding data sorting
	Save time	Challenges during interpreting
CNN	Detects the important information and features without labeling	A large training data is required
	High performance in image recognition	Lack of interpretability (black boxes)
FCN	Provides computational speed	Requires large amounts of labeled data for training
	Automatically eliminates the background noise	High labeling cost
RNN	Can decide which information to remember from its past experience	Harder to train the model
	A deep learning model for sequential data	High computational cost
MIL	Does not require detailed annotation	A large amount of training data is required
	Can be applied to large data sets	High computational cost
GAN	Generates new realistic data resembling the original data	Harder to train the model

AI: Artificial intelligence; ML: Machine learning; CNN: Convolutional neural networks; FCN: Fully convolutional neural networks; RNN: Recurrent neural networks; MIL: Multi-instance learning; GAN: Generative adversarial networks.

Table 2 Artificial intelligence-based applications in gastric cancer

Ref.	Task	No. of cases/data set	Method	Performance
Duraipandian et al[89]	Classification	700 slides	GastricNet	Accuracy (100%)
Cosatto et al[72]		> 12000 WSIs	MIL	AUC (0.96)
Sharma et al[31]		454 cases	CNN	Accuracy (69%)
Qu et al[90]		9720 images	DL	AUCs (up to 0.97)
Yoshida et al[32]		3062 gastric biopsies	ML	Overall concordance rate (55.6%)
León et al[91]		40 images	CNN	Accuracy (up to 89.7%)
Liang et al[92]		1900 images	DL	Accuracy (91.1%)
Sun et al[93]		500 images	DL	Accuracy (91.6%)
Tomita et al[94]		502 images1	Attention-based DL	Accuracy (83%)
Wang et al[95]		608 images	Recalibrated multi-instance-DL	Accuracy (86.5%)
Iizuka et al[33]		1746 biopsy WSIs	CNN, RNN	Accuracy (95.6%), AUCs (up to 0.98)
Bollschweiler et al[41]	Prognosis	135 cases	ANN	Accuracy (93%)
Hensler et al[42]		4302 cases	QUEEN technique	Accuracy (72.73%)
Jagric et al[43]		213 cases	Learning vector quantization NN	Sensitivity (71%), specificity (96.1%)
Lu et al[36]		939 cases	MMHG	Accuracy (69.28%)
Jiang et al[37]		786 cases	SVM classifier	AUCs (up to 0.83)
Liu et al[40]		432 tissue samples	SVM classifier	Accuracy (up to 94.19%)
Korhani Kangi and Bahrampour[38]		339 cases	ANN, BNN	Sensitivity (88.2% for ANN, 90.3% for BNN)Specificity (95.4% for ANN, 90.9% for BNN)
Zhang et al[39]		669 cases	ML	AUCs (up to 0.831)
García et al[44]	Tumor infiltrating lymphocytes	3257 images	CNN	Accuracy (96.9%)
Kather et al[56]	Genetic alterations	1147 cases2	Deep residual learning	AUC (0.81 for gastric cancer)
Kather et al[47]		> 1000 cases3	NN	AUC (up to 0.8)
Fu et al[57]		> 1000 cases4	NN	Variable across tumors/gene alterations. Strongest relations in whole genome duplications

¹Esophageal adenocarcinoma and Barrett’s esophagus.

²Gastric and colorectal cancers.

³Gastric, colorectal, esophageal, and liver cancers.

⁴Gastric, colorectal, and pancreatic cancers.

AI: Artificial intelligence; GastricNet: The deep learning framework; WSIs: Whole slide images; MIL: Multi-instance learning; AUC: Area under the curve; CNN: Convolutional neural networks; DL: Deep learning; ML: Machine learning; RNN: Recurrent neural networks; ANN: Artificial neural network; QUEEN technique: Quality assured efficient engineering of feedforward neural networks with supervised learning; NN: Neural network; MMHG: Multimodal hypergraph learning framework; SVM: Support vector machine.

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.

Table 4 Summary of challenges and suggested solutions in development process of artificial intelligence applications

Process	Challenges	Suggested solutions
Ethical considerations	Lack of patient’s approval for commercial use	Approval for both research and product development
Design of AI models	Underestimation of end-users’ needs	Collaboration with skate holders
Optimization of data-sets	CNN: Large amounts of images	Augmentation techniques, transfer learning
	Rare tumors: Limited number of images	Global data sharing
	Variations in preanalytical and analytical phases	AI algorithms to standardize staining, color properties, and WSIs quality
Annotation of data-sets	Interobserver variations in diagnosis	MIL algorithms
	Discrepancies among performances for trained algorithms
Validation	Presence of ground truth without objectivity	Multicenter evaluations that include many pathologists and data-set
Regulation	Lack of current regulatory guidance specific for AI tools	New guidelines and regulations for safer and effective AI tools
Implementation	Changes in work-flow	Selection of AI applications that will speed up the work-flow
	IT infrastructure investment	Augmented microscopy directed to the cloud network service
	The relative inexperience of pathologists	Training about AI, integration of AI in medical education
	AI applications that lack interpretability ( Black-box)	Constructions of interpretable models, generating attention heat map
	Lack of external quality assurance	Sheme for this purpose should be designed
	Legal implications	The performance of AI algorithms should be assured for reporting

CNN: Convolutional neural networks; MIL: Multi-instance learning.