Predicting the Recurrence of Sporadic Odontogenic Keratocyst Using Whole-Slide Histopathology Images With the Hybrid Encoder Iterative Attention Convolution Model

Objectives

Odontogenic keratocysts (OKCs) are challenging due to their aggressiveness and high recurrence rates, complicating decision-making for clinicians and pathologists. Despite efforts to identify predictive characteristics, management remains challenging. The study aims to design a reliable artificial intelligence model to enhance predictive models and distinguish between recurrent and nonrecurrent whole-slide images of OKCs.

Material and Methods

84 OKC cases were selected for this study, including 29 whole slide images (WSIs) of recurrent OKCs and 35 WSIs of non-recurrent OKCs for model development. The model was evaluated using 14 non-recurrent and 6 recurrent cases. The proposed Hybrid Encoder Iterative Attention Convolution (HEIAC) model integrates the strengths of three fundamental components: an encoder, an attention mechanism, and convolutional layers to classify images effectively. The encoder learns to extract useful features, resulting in more meaningful representations that capture the underlying structure of the image data. Iterative attention enables the model to capture intricate details and subtle patterns that may be crucial for accurate image classification. Convolutional layers are designed to learn hierarchical representations of image features automatically. This model harnesses the capabilities of each component to achieve robust and accurate image classification.

Results

The proposed HEIAC model attained 0.98 testing accuracy and exhibits superior performance across the majority of evaluation metrics, achieving 96% recall, 100% precision, a 97% F1-score, and a perfect AUC of 1.0, and used 96% fewer trainable parameters than the standard vision transformer.

Conclusions

This approach improves predictive models for distinguishing recurrent and non-recurrent OKCs.