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      Prediction of KRAS, NRAS and BRAF status in colorectal cancer patients with liver metastasis using a deep artificial neural network based on radiomics and semantic features

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          Abstract

          There is a critical need for development of improved methods capable of accurately predicting the RAS (KRAS and NRAS) and BRAF gene mutation status in patients with advanced colorectal cancer (CRC). The purpose of this study was to investigate whether radiomics and/or semantic features could improve the detection accuracy of RAS/BRAF gene mutation status in patients with colorectal liver metastasis (CRLM). In this retrospective study, 159 patients who had been diagnosed with CRLM in two hospitals were enrolled. All patients received lung and abdominal contrast-enhanced CT (CECT) scans prior to radiation therapy and chemotherapy. Semantic features were independently assessed by two radiologists. Radiomics features were extracted from the portal venous phase (PVP) of the CT scan for each patient. Seven machine learning algorithms were used to establish three scores based on the semantic, radiomics and the combination of both features. Two semantic and 851 radiomics features were used to predict the mutation status of RAS and BRAF using an artificial neural network method (ANN). This approach performed best out of the seven tested algorithms. We constructed three scores which were based on radiomics, semantic features and the combined scores. The combined score could distinguish between wild-type and mutant patients with an AUC of 0.95 in the primary cohort and 0.79 in the validation cohort. This study proved that the application of radiomics together with semantic features can improve non-invasive assessment of the gene mutation status of RAS (KRAS and NRAS) and BRAF in CRLM.

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          Author and article information

          Journal
          Am J Cancer Res
          Am J Cancer Res
          ajcr
          American Journal of Cancer Research
          e-Century Publishing Corporation
          2156-6976
          2020
          01 December 2020
          : 10
          : 12
          : 4513-4526
          Affiliations
          [1 ] Department of Medical Oncology, The First Hospital of China Medical University 110001, Liaoning, China
          [2 ] Key Laboratory of Anticancer Drugs and Biotherapy of Liaoning Province, The First Hospital of China Medical University 110001, Liaoning, China
          [3 ] Liaoning Province Clinical Research Center for Cancer 110001, Liaoning, China
          [4 ] Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors, Ministry of Education 110001, Liaoning, China
          [5 ] Paul C. Lauterbur Research Center for Biomedical Imaging, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences 518005, Guangdong, China
          [6 ] Cancer Center, The First Affiliated Hospital of Jinzhou Medical University 121001, Liaoning, China
          [7 ] Department of Medical Oncology, Liaoning Cancer Hospital and Institute, Cancer Hospital of China Medical University 110042, Liaoning, China
          Author notes
          Address correspondence to: Xiujuan Qu and Zhi Li, Department of Medical Oncology, The First Hospital of China Medical University, 155 Nanjing Street, 110001, Liaoning Province, China. Tel: +86-13604031355; E-mail: xiujuanqu@ 123456yahoo.com (XJQ); Tel: +86-15840247601; E-mail: zli@ 123456cmu.edu.cn (ZL)
          [*]

          Equal contributors.

          Article
          PMC7783758 PMC7783758 7783758
          7783758
          33415015
          96e746c7-d032-4aa0-bde0-baf7505d1053
          AJCR Copyright © 2020
          History
          : 01 September 2020
          : 17 November 2020
          Categories
          Original Article

          BRAF,radiomics,artificial neural network,colorectal cancer,RAS

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