Deep-learning models for differentiation of xanthogranulomatous cholecystitis and gallbladder cancer on ultrasound

The radiological differentiation of xanthogranulomatous cholecystitis (XGC) and gallbladder cancer (GBC) is challenging yet critical. We aimed at utilizing the deep learning (DL)-based approach for differentiating XGC and GBC on ultrasound (US).

This single-center study comprised consecutive patients with XGC and GBC from a prospectively acquired database who underwent pre-operative US evaluation of the gallbladder lesions. The performance of state-of-the-art (SOTA) DL models (GBCNet-convolutional neural network [CNN] and RadFormer, transformer) for XGC vs. GBC classification in US images was tested and compared with popular DL models and a radiologist.

Twenty-five patients with XGC (mean age, 57 ± 12.3, 17 females) and 55 patients with GBC (mean age, 54.6 ± 11.9, 38 females) were included. The performance of GBCNet and RadFormer was comparable (sensitivity 89.1% vs. 87.3%, p = 0.738; specificity 72% vs. 84%, p = 0.563; and AUC 0.744 vs. 0.751, p = 0.514). The AUCs of DenseNet-121, vision transformer (ViT) and data-efficient image transformer (DeiT) were significantly smaller than of GBCNet (p = 0.015, 0.046, 0.013, respectively) and RadFormer (p = 0.012, 0.027, 0.007, respectively). The radiologist labeled US images of 24 (30%) patients non-diagnostic. In the remaining patients, the sensitivity, specificity and AUC for GBC detection were 92.7%, 35.7% and 0.642, respectively. The specificity of the radiologist was significantly lower than of GBCNet and RadFormer (p = 0.001).

SOTA DL models have a better performance than radiologists in differentiating XGC and GBC on the US.