Contrast-Enhanced CT-Based Deep Learning and Habitat Radiomics for Analysing the Predictive Capability for Oral Squamous Cell Carcinoma.

OBJECTIVES

This study aims to explore a novel approach for predicting cervical lymph node metastasis (CLNM) and pathological subtypes in oral squamous cell carcinoma (OSCC) by comparing deep learning (DL) and habitat analysis models based on contrast-enhanced CT (CECT).

METHODS

A retrospective analysis was conducted using CECT images from patients diagnosed with OSCC via paraffin pathology at the Second Affiliated Hospital of Dalian Medical University. All patients underwent primary tumor resection and cervical lymph node dissection, with a total of 132 cases included. A DL model was developed by analysing regions of interest (ROIs) in the CECT images using a convolutional neural network (CNN). For habitat analysis, the ROI images were segmented into 3 regions using K-means clustering, and features were selected through a fully connected neural network (FCNN) to build the model. A separate clinical model was constructed based on nine clinical features, including age, gender, and tumor location. Using LNM and pathological subtypes as endpoints, the predictive performance of the clinical model, DL model, habitat analysis model, and a combined clinical + habitat model was evaluated using confusion matrices and receiver operating characteristic (ROC) curves.

RESULTS

For LNM prediction, the combined clinical + habitat model achieved an area under the ROC curve (AUC) of 0.97. For pathological subtype prediction, the AUC was 0.96. The DL model yielded an AUC of 0.83 for LNM prediction and 0.91 for pathological subtype classification. The clinical model alone achieved an AUC of 0.94 for predicting LNM.

CONCLUSION

The integrated habitat-clinical model demonstrates improved predictive performance. Combining habitat analysis with clinical features offers a promising approach for the prediction of oral cancer.

CLINICAL RELEVANCE: The habitat-clinical integrated model may assist clinicians in performing accurate preoperative prognostic assessments in patients with oral cancer.