Automatic recognition of tumour-infiltrating lymphocytes in pathological biopsy images of the gastric mucosa

Figure 1 Overall workflow of the multiscale two-stage convolutional neural network model for automatic recognition of gastric mucosal tumour-infiltrating lymphocytes and prognostic analysis. This workflow shows four core stages of automated tumour-infiltrating lymphocyte (TIL) analysis and prognostic evaluation in gastric mucosal biopsy images. A: A total of 320 whole slide images (WSIs) from three centres were divided into training and independent test sets (7:3); B: WSIs underwent regions of interest extraction, Ruifrok-Johnston colour deconvolution (to eliminate staining heterogeneity) and 299 pixel × 299 pixel patch generation; C: A two-stage convolutional neural network (CNN) was constructed-a gastric CNN (based on pretrained Inception-ResNet-v2) for lesion segmentation/grading and a gastric artificial intelligence-TIL (G-AI-TIL) with three multiscale branches (10 × /20 × /40 ×) and attention fusion for TIL enumeration; D: The G-AI-TIL index was calculated to analyse its correlation with lesion grade; Kaplan-Meier and Cox regression verified its prognostic value in early gastric cancer, and a prognostic nomogram was constructed. WSI: Whole slide image; ROI: Region of interest; H/E: Hematoxylin and eosin; CNN: Convolutional neural network; CAG: Chronic atrophic gastritis; IM: Intestinal metaplasia; HGIN: High-grade intraepithelial neoplasia; EGC: Early gastric cancer; TIL: Tumor-infiltrating lymphocytes; G-AI-TIL: Gastric artificial intelligence-based tumor-infiltrating lymphocytes.