Multi-Site Pathological Images-Guided ViT for Oral Squamous Cell Carcinoma Recurrence Prediction

Background: Head and neck squamous cell carcinoma is one of the most common cancers. Postoperative recurrence is a risk factor for poor prognosis and decreased survival rate. There is a lag and passivity in the diagnosis and monitoring of postoperative recurrence in clinical diagnosis and treatment. The application of artificial intelligence to explore and develop a model for predicting postoperative recurrence of oral squamous cell carcinoma is expected to solve clinical difficulties and guide the formulation of postoperative monitoring and diagnosis and treatment plans. Materials and Methods： We recruited patients diagnosed with oral squamous cell carcinoma who had received surgical treatment, collected patient follow-up information and postoperative pathological images, enhanced and standardized pathological images, and extracted pathological features through visual transformation model (ViT). The features of pathological images were fused into a multi-layer perceptron model (MLP) for training, verification and testing, and the predictive performance of the model was evaluated by various indexes. Results: Among the 1325 patients enrolled, 275 relapsed, accounting for 20.8%. The optimized ViT-Small model has a validation AUC of 94.79% (90% accuracy) and a test AUC of 95.68% (91.5% accuracy), and outperforms other models on both validation and test sets. Conclusion: ViT-Small model have high predictive performance, which is expected to predict postoperative recurrence, guide the formulation of clinical diagnosis and treatment plan.

Sex

All

Inclusion Criteria:Head and neck squamous cell carcinoma and underwent surgery -

Exclusion Criteria:Head and neck squamous cell carcinoma and without underwent surgery

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