Background This study aimed to demonstrate both the potential and development progress in the identification of extracapsular nodal extension in head and neck cancer patients prior to surgery. Methodology A deep learning model has been developed utilizing multilayer gradient mapping-guided explainable network architecture involving a volume extractor. In addition, the gradient-weighted class activation mapping approach has been appropriated to generate a heatmap of anatomic regions indicating why the algorithm predicted extension or not. Results The prediction model shows excellent performance on the testing dataset with high values of accuracy, the area under the curve, sensitivity, and specificity of 0.926, 0.945, 0.924, and 0.930, respectively. The heatmap results show potential usefulness for some select patients but indicate the need for further training as the results may be misleading for other patients. Conclusions This work demonstrates continued progress in the identification of extracapsular nodal extension in diagnostic computed tomography prior to surgery. Continued progress stands to see the obvious potential realized where not only can unnecessary multimodality therapy be avoided but necessary therapy can be guided on a patient-specific level with information that currently is not available until postoperative pathology is complete.
Keywords: artificial intelligence; deep learning; extracapsular extension; head and neck squamous cell carcinoma; model explainability; preoperative.
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