A Few-shot Multimodal Deep Learning Framework For Precision Diagnosis Of Parotid Gland

Accurate differentiation between benign and malignant parotid tumors is imperative for patient prognosis. However, due to the high degree of heterogeneity of parotid tumors and the limited availability of imaging data, preoperative diagnosis remains a significant challenge. To address this issue, the present study proposes a multimodal deep learning framework based on small-sample learning. This framework integrates multi-sequence MRI information and incorporates a multi-scale spatial attention mechanism to improve feature extraction and classification performance in situations where samples are limited. The model was systematically evaluated using a retrospectively collected cohort of 198 patients with histopathologically confirmed primary parotid gland tumors (107 benign, 91 malignant) who underwent complete preoperative MRI-including T1-weighted, T2-weighted, and diffusion-weighted imaging-at our institution, with no prior treatment for the parotid lesion. The cohort was split at the patient level into training (70%), validation (15%), and test (15%) sets using stratified random sampling to preserve the benign-to-malignant ratio, ensuring mutually exclusive subsets and preventing data leakage. The model achieved an area under the curve (AUC) of 0.9822. This finding underscores the system's capacity to differentiate between benign and malignant tumors. Moreover, the model exhibited substantial advantages in terms of diagnostic accuracy when compared to experienced radiologists, underscoring its potential application in preoperative benign-malignant differentiation and clinical decision support for parotid tumors.