Development of a novel combined nomogram integrating deep-learning-assisted CT texture and clinical-radiological features to predict the invasiveness of clinical stage IA part-solid lung adenocarcinoma: a multicentre study.

Journal: Clinical radiology
Published Date: Jul 13, 2023

Abstract

AIM: To develop a novel combined nomogram based on deep-learning-assisted computed tomography (CT) texture (DL-TA) and clinical-radiological features for the preoperative prediction of invasiveness in patients with clinical stage IA lung adenocarcinoma manifesting as part-solid nodules (PSNs).

Authors

  • Z Zuo
    Department of Radiology, Xiangtan Central Hospital, Xiangtan, Hunan 411000, China.
  • W Zeng
    Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University & State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, Chengdu 610041, China.
  • K Peng
    Department of Spine Surgery, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China.
  • Y Mao
    Department of Radiology, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, Hunan 410004, China.
  • Y Wu
    School of Biomedical Informatics, The University of Texas Health Science Center at Houston , Houston, Texas, USA.
  • Y Zhou
    Department of Radiation Oncology, First Affiliated Hospital, Bengbu Medical College, Bengbu, Anhui 233004, China.
  • W Qi
    Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan 646100, China. Electronic address: qiwanyin0508@163.com.

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