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.
      To read this article in full you will need to make a payment
      ASTRO Member Login
      ASTRO Members, full access to the journal is a member benefit. Use your society credentials to access all journal content and features.
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Purchase one-time access:

      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect


      Commenting Guidelines

      To submit a comment for a journal article, please use the space above and note the following:

      • We will review submitted comments as soon as possible, striving for within two business days.
      • This forum is intended for constructive dialogue. Comments that are commercial or promotional in nature, pertain to specific medical cases, are not relevant to the article for which they have been submitted, or are otherwise inappropriate will not be posted.
      • We require that commenters identify themselves with names and affiliations.
      • Comments must be in compliance with our Terms & Conditions.
      • Comments are not peer-reviewed.