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      Combining Many-objective Radiomics and 3-dimensional Convolutional Neural Network through Evidential Reasoning to Predict Lymph Node Metastasis in Head and Neck Cancer

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          Abstract

          Lymph node metastasis (LNM) is a significant prognostic factor in patients with head and neck cancer, and the ability to predict it accurately is essential to optimizing treatment. Positron emission tomography (PET) and computed tomography (CT) imaging are routinely used to identify LNM. Although large or highly active lymph nodes (LNs) have a high probability of being positive, identifying small or less reactive LNs is challenging. The accuracy of LNM identification strongly depends on the physician’s experience, so an automatic prediction model for LNM based on CT and PET images is warranted to assist LMN identification across care providers and facilities. Radiomics and deep learning are the two promising imaging-based strategies for node malignancy prediction. Radiomics models are built based on handcrafted features, while deep learning learns the features automatically. To build a more reliable model, we proposed a hybrid predictive model that takes advantages of both radiomics and deep learning based strategies. We designed a new many-objective radiomics (MaO-radiomics) model and a 3-dimensional convolutional neural network (3D-CNN) that fully utilizes spatial contextual information, and we fused their outputs through an evidential reasoning (ER) approach. We evaluated the performance of the hybrid method for classifying normal, suspicious and involved LNs. The hybrid method achieves an accuracy (ACC) of 0.88 while XmasNet and Radiomics methods achieve 0.81 and 0.75, respectively. The hybrid method provides a more accurate way for predicting LNM using PET and CT.

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          Author and article information

          Journal
          0401220
          6459
          Phys Med Biol
          Phys Med Biol
          Physics in medicine and biology
          0031-9155
          1361-6560
          27 March 2020
          29 March 2019
          29 March 2019
          23 April 2020
          : 64
          : 7
          : 075011
          Affiliations
          [1. ]Department of Radiation Oncology, the University of Texas Southwestern Medical Center, Dallas, TX 75390 USA
          [2. ]Medical Artificial Intelligence and Automation (MAIA) Lab, the University of Texas Southwestern Medical Center, Dallas, TX 75390 USA
          [3. ]Department of Head and Neck Cancer, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.
          Author notes
          [* ]corresponding author ( Jing.Wang@ 123456UTSouthwestern.edu )
          Article
          PMC7178778 PMC7178778 7178778 nihpa1580005
          10.1088/1361-6560/ab083a
          7178778
          30780137
          918bffd2-a140-4533-b952-3c7cd01bb821
          History
          Categories
          Article

          Convolutional neural network,Radiomics,Head & Neck Cancer,evidential reasoning,Lymph node metastasis

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