Deep learning for classification of aggressive versus non-aggressive central giant cell granuloma using whole-slide histopathology images

Microscopic images of aggressive and non-aggressive cases of central giant cell granuloma (CGCG) were analyzed by deep learning algorithms in order to assess its potential as a tool in predicting the biological behavior of CGCG. CGCGs with cortical expansion/perforation, tooth resorption/displacement, or recurrence were classified as aggressive (A-group; N = 48), CGCGs without these features as non-aggressive (N-group; N = 39). Data on patient age, gender, and jaw location were collected. Hematoxylin–eosin (H&E)–stained sections were scanned at × 10 magnification, yielding 9982 sections (5236 A-group, 4746 N-group). After excluding artifacts, 4272 sections (2629 A-group, 1643 N-group) were used to train a ResNet-50 model pre-trained on ImageNet. Data augmentation included random rotation, flipping, and zooming. Model was trained for 100 epochs with an 80/20 train/validation split and tested on 100 images (50 A-group, 50 N-group). Receiver Operating Characteristic (ROC) analysis with area under the curve (AUC), sensitivity, and specificity was performed; t-test and chi-square test were used for age and frequency (p < 0.05). AUC was 52%, sensitivity 54%, and specificity 50%. Mean age of patients in A-group was lower than in N-groups (32.6 ± 19.98 years and 42.2 ± 21.58 years, respectively; p = 0.038). F:M ratio was 1:1 in both groups. Mandible was twofold more frequently than maxilla in both groups. This pioneering study to differentiate between aggressive and non-aggressive CGCGs based on whole microscopic sections using a deep machine learning model was not successful, probably due to lack of specific segmentations and technical staining issues. Further investigation with advanced preprocessing is needed to enhance model performance and clinical utility.