Table 3.

Summary of AI-based prognostic algorithms in breast cancer pathology

Year of publication	Reference	Number of slides for training / validation	Pathologists review (training/validation)	Algorithm details	Algorithm endpoints/outputs	Algorithm performance
2020	Pantanowitz et al [24]	320 breast invasive ductal carcinoma cases, 16,800 digital image patches from 120 WSIs/140 digital image	Ten expert pathologists and 24 readers of varying expertise	RCNN by ResNet-101	Mitotic figures counting	Accuracy with AI = 55.2% compared to manually 43.9%
2020	Chow et al [25]	93 cases of phyllodes tumor	N/A	Image Management System viewer	Phyllodes tumor mitoses counting	correlation = .794; R2 = 0.63; P < .001; 95% CI, 0.270–0.373
2021	Balkenhol et al [26]	94 TNBC specimens	two histopathologists	convolutional neural networks (CNN)	TILs assessment and prognostic values	Relapse free survival HR ranging between 0.777 (CD8, IM2) and 0.915 (CD3, ITS); overall survival HR varying between 0.722 (FOXP3, ITT) and 0.908 (CD3, ITA)
2022	Wang et al [6]	Training:1567Test:1262	Pathologists	deep CNN model	Categorization of NHG2 breast tumors and its risk of recurrence	increased risk for recurrence in DG2-high (HR 1.91, 95% CI 1.11–3.29, P = 0.019)
2022	Mantrala et al [27]	Training: 46 Test: 91	Six pathologists	Unet, DenseNet backbone, preloaded ImageNet for TF, HoVerNet, pretrained ImageNet ResNet50-Preact for NP, LinkNet with EfficientNet B4 backbone for MC	Breast cancer grading	Tubular formation (κ = 0.471 each) Nuclear pleomorphism (κ = 0.342) and was worst for mitotic count (κ = 0.233)