Artificial intelligence in gastrointestinal cancer: Recent advances and future perspectives

Table 4 Previous studies on the pathology of colorectal cancer using artificial intelligence

Ref.	Targets	Sample size	Input	Task	Analysis method	Diagnostic performance
Van Eycke et al[43]	CRC		H&E staining, IHC image	Segmentation of the glandular epithelium	TMA, CNN	F1 value: 0,912
Graham et al[44]	CRC		H&E staining	Differentiation of intratumor glands	CNN	F1 values: 0.90
Abdelsamea et al[45]	CRC	333 samples	H&E staining, IHC (CD3)	Differentiation of the tumor epithelium	TMA, CNN	Accuracy: 0.93-0.94
Yan et al[46]	CRC		H&E staining	Tumor classification,segmentation of tumors,	CNN	Accuracy: Classification, 97.8%; segmentation, 84%
Haj-Hassan et al[47]	CRC		Multispectral images	Segmentation of carcinoma	CNN	Accuracy: 99.1%
Rathore et al[48]	CRC	Biopsy samples	H&E staining	Detection and grading of tumors	Texture and morphology patterns, SVM	Recognition rate: Detection, 95.4%; grading; 93.4%
Yang et al[49]	CRC	180 samples	H&E staining	Diagnosis of benign tumors, neoplasms, and carcinoma	SVM, histogram, texture	AUC: 0.852
Chaddad et al[50]	CRC	30 cases	H&E staining	Diagnosis of carcinoma, adenoma, and benign tumors	Automatic segmentation, texture	Accuracy: 98.9%
Yoshida et al[51]	CRC	1328 samples	H&E staining	Diagnosis of benign tumors, neoplasms, and carcinoma	CNN, automatic analysis of structure	Undetected rate of carcinoma and adenoma: 0-9.3% and 0-9.9%, respectively
Takamatsu et al[52]	CRC surgery	397 samples	H&E staining	Prediction of lymph node metastasis	LR, shape analysis	AUC: 0.94
Weis et al[53]	CRC	596 cases	IHC (AE1/AE3)	Automatic evaluation of tumor budding	TMA, CNN	Correlation; R2 value: 0.86
Bychkov et al[54]	CRC surgery	420 cases	H&E staining	Prediction of survival	TMA, CNN	Good biomarker for predicting survival
Kather et al[55]	CRC	973 slides	H&E staining	Prediction of survival	Stromal pattern, CNN	Good biomarker for predicting survival
Reichling et al[56]	CRC surgery	1018 cases	HE, IHC (CD3, CD8)	Prediction of survival	RF, monogram	Good biomarker for predicting survival

CRC: Colorectal cancer; H&E: Hematoxylin eosin staining; IHC: Immunohistochemistry; TMA: Tissue microarray; CNN: Convolutional neural network; SVM: Support vector machine; AUC: Area under the curve; LR: Linear regression.