Artificial intelligence applications in predicting the behavior of gastrointestinal cancers in pathology

Table 3 Artificial intelligence-based applications in pathology for the determination of tumor behavior in gastric cancer

Ref.	Task	Data sets	Algorithm/Model	Performance	Comments
Yasuda et al[66]	NC, GR1, GR2, GR3; PDL-1, ATF7IP/MCAF1	66 WSIs	SV, ML, wndchrm	AUCs: 0.98-0.99	The model allows grading emphasizing a correlation between molecular expression and tissue structures
Kanavati et al[67]	NC, ADC-D, ADC-O	1-stage training: 1950 WSIs, 2-stage training: 874 WSIs	CNN and RNN	AUCs: 0.95-0.99	The tool can aid pathologists by potentially accelerating their diagnostic workflow
Fu et al[68]	NC, TC, MC, PC	Training 2938 WSIs, Testing 980 WSIs	StoHisNet	The accuracy: 94.69%, F1 score: 94.96%, Recall: 94.95%, Precision: 94.97%	The model has high performance in the multi-classification on gastric images and shows strong generalization ability on other pathological datasets
Su et al[69]	NC, WD, PD, MSS vs MSI	GR: Training 348 WSIs, Testing 88 WSIs MSS: Training 212 WSIs, Testing: 52 WSIs, MSI: Training 136 WSIs, Testing: 36 WSIs	ResNet-18	PD vs WD, F1 score: 0.8615, PD vs WD vs NC, F1 score: 0.8977; MSI vs MSS accuracy: 0.7727	The proposed system integrated the tumor GR and MSI status recognition problems into the same workflow and was suitable for exploring the relationships between pathological features and molecular status
Muti et al[79]	MSI vs MSS; EBV (+) vs EBV (-)	2823 patients with known MSI status; 2685 patients with known EBV status	CNN, Shufflenet	MSI vs MSS, AUROCs: 0.723-0.863; EBV (+) vs EBV (-), AUROCs: 0.672-0.859	DL-based classifiers have the potential to provide faster decisions for pathologists and to offer therapeutic options tailored to the molecular profile of the individual patient
Kather et al[80]	MSI vs MSS	Training 81 patients +216 patients (TCGA-STAD)	ResNet-18	AUC: 0.84	This system provides significant improvements in molecular alterations screening workflow
Kather et al[81]	EBV (+) vs. EBV (-)	Training 317 patients (TCGA-STAD)	CNN, VGG19	AUC: 0.80	This workflow enables a fast and low-cost method to identify EBV and enables pathologists to check the plausibility of computer-based image classification ( the black box of DL)
Hinata et al[82]	EBV+MSI/dMMR vs EBV- non MSI/dMMR	UTokyo training cohort: 326 patients; TCGA training cohort: 48 patients	CNNs,VGG16, VGG19, ResNet50, EfficientNetB0	AUCs: 0.901–0.992 (Utokyo cohort); AUCs: 0.809–0.931 (TCGA cohort)	The model detects immunotherapy-sensitive GC subtypes from histological images at a lower cost and in a shorter time than the conventional methods
Zheng et al[83]	EBV (+) vs EBV (-)	EBV (+) 203 WSIs; EBV (-) 803 WSIs	EBVNet	AUROC: 0.969, Internal validation; AUROC: 0.941, External dataset AUROC: 0.895, TCGA dataset	The human-machine fusion significantly improves the diagnostic performance of both the EBVNet and the pathologist, provides an approach for the identification of EBV(+) GC, and may help effectively select patients for immunotherapy
Flinner et al[87]	EBV, MSI, GS, CIN	Training 84 WSIs (TCGA-STAD); Testing: 133 WSIs (TCGA-STAD)	CNN, DenseNet161	AUC: 0.76 for four classes	The simplified molecular TCGA and GC subclasses could be predicted by DL directly based on H-E staining
Jang et al[88]	CDH1, ERBB2, KRAS, PIK3CA, TP53 mutations	425 FF slides (TCGA-STAD); 320 FT slides (TCGA-STAD)	CNN, Inception-v3	AUCs (FF-FT): CDH1 (0.667-0.778), ERBB2(0.63-0.833), KRAS (0.657-0.838); PIK3CA (0.688-0.761), TP53 (0.572-0.775)	When trained with appropriate tissue data, DL could predict genetic mutations in H-E-stained tissue slides
Huang et al[109]	Metastatic LNs	983 WSIs	ESCNN	AUC: 0.9936	ESCNN improves the accuracy of pathologists in identifying metastatic LNs, micrometastases, and isolated tumor cells, allowing for shortening the review time
Hu et al[107]	Metastatic LNs	222 patients	RCNN, Xception and DenseNet-121	Accuracy 97.13%; PPV: 93.53, NPV: 97.99%	The system can be implemented into clinical workflow to assist pathologists in preliminary screening for LN metastases in GC patients
Matsushima et al[108]	Metastatic LNs	827 lymph nodes	CNN	AUROC: 0.9994	This DL-based diagnosis-aid system can assist pathologists in detecting LN metastasis in GC and reduce their workload
Wang et al[106]	Metastatic LNs, T/LNM	9366 slides (7736 with metastasis)	Resnet-50	LNM (+) vs (-): Sensitivity 98.5%, Specificity 96.1%; T/LNM: HR: 2.05 (univariate analysis); 1.39 (multivariate analysis)	This system can assist pathologists in detecting LN metastasis in GC and reduce their workload. Besides, T/LNM is prognostic of OS in GC patients
Hong et al[116]	dTSR (HE and CK7)	Training 13 WSIs; Testing 358 WSIs	cGAN	Kappa value: 0.623 (dTSR and vTSR); AUROC: 0.907; OS (P: 0.0024)	By diagnosing TSR in GC, this model predicts OS in the advanced stage of GC
Meier et al[127]	TME + Ki-67	248 patients	CNN	HRs: Ki67&CD20: 1.364, CD20&CD68: 1.338; Ki67&CD68: 1.473	In combination with a panel of IHC markers, this model predicts the prognosis of patients with GC
Huang et al[128]	OS	Training: 2261 pictures; Internal validation: 960 pictures	GastroMIL	HR: 2.414 (univariate analysis), 1.843 (multivariate analysis)	The risk score computed by MIL-GC was proved to be the independent prognostic value of GC
Jiang et al[129]	5-YS, 5-YDFS	786 patients	ML, SVM	AUCs: 5-YS: 0.834; 5-YDFS: 0.828	The classifier can accurately distinguishes GC patients with different OS and DFS and identifies a subgroup of patients with stage II and III disease who could benefit from adjuvant chemotherapy
Jiang et al[130]	Low SVM vs High SVM, 5-YS, 5-YDFS	Training: 223 patients; Internal validation: 218 patientsExternal validation: 227 patients	ML, SVM	AUCs: 5-YS: 0.818; 5-YDFS: 0.827	SVM signature distinguish GC patients with different OS and DFS and identifies a subgroup of patients with stage II and III disease who could benefit from adjuvant chemotherapy
Wang et al[131]	TME	172 patients	CGSignature powered by AI	AUROCs: 0.960 ± 0.01 (binary classification), 0.771 ± 0.024 to 0.904 ± 0.012 (ternary classification)	Digital grade cancer staging produced by CGSignature predicts the prognosis of GC and significantly outperforms the AJCC 8th edition Tumor Node Metastasis staging system

NC: Non cancer; GR: Grade; ATF7IP/MCAF1: Activating Transcription Factor 7 Interacting Protein; WSIs: Whole slide images; SV: Supervised; ML: Machine learning; wndchrm: weighted neighbor distances using a compound hierarchy of algorithms representing morphology; AUC: Area under the curve; ADC-D: Diffuse adenocarcinoma; ADC-O: Adenocarcinoma other; DL: Deep learning; CNN: Convolutional neural networks; RNN: Recurrent neural network; TC: Tubular carcinoma; MC: Mucinous carcinoma; PC: Papillary carcinoma; WD: Well differentiated; PD: Poorly differentiated; MSI: Microsatellite instable; MSS: Microsatellite stable EBV: Epstein-Barr virus; TCGA-STAD: Tumor Cancer Genome Atlas, Stomach adenocarcinoma dMMR: Deficient mismatch repair; GC: Gastric cancer; AUROC: Area under the receiver operating characteristics; GS: Genomically stable; CIN: Chromosomally unstable; LN: Lymph node; ESCNN: Enhanced streaming CNN; RCNN: Region based CNN; PPV: Positive predictive value; NPV: Negative predictive value; T/LNM: Tumor area-to-metastatic LN-area ratio; dTSR: Digital tumor-stroma ratio; HE: Hematoxylin and eosin; CK7: Cytokeratin 7; cGAN: Conditional generative adversarial network; vTSR: Visual tumor-stroma ratio; OS: Overall survival; TME: Tumor microenvironment; HR: Hazard ratio; 5-YS: Five year survival; 5-YDFS: Five year disease free survival; SVM: Support vector machine; AI: Artificial intelligence.