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External Validation and Radiologist Comparison of a Deep Learning Model (DLM) to Identify Extranodal Extension (ENE) in Head and Neck Squamous Cell Carcinoma (HNSCC) with Pretreatment Computed Tomography (CT) Imaging

      For HNSCC patients, pretreatment identification of ENE would aid risk stratification and guide management. ENE identification using diagnostic imaging is currently inaccurate, with reported areas under the receiver operating characteristic curve (AUC) <.70 and accuracies <70%. We previously developed and internally validated a 3D convolutional neural network-based DLM trained on a HNSCC lymph node database derived from preoperative, diagnostic, contrast-enhanced CT scans from patients who underwent lymph node dissection at a single institution. An AUC of 0.91 was achieved on the internal test set (Institution 1). In this study, we sought to externally validate our model and directly compare its performance with diagnostic radiologists.
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