Nomograms integrating CT radiomic and deep learning signatures to predict overall survival and progression-free survival in NSCLC patients treated with chemotherapy.

Allergy & Immunology Dermatology Hematology Oncology/Hematology Pulmonology
Journal: Cancer imaging : the official publication of the International Cancer Imaging Society
Published Date: Oct 22, 2023

Abstract

OBJECTIVES: This study aims to establish nomograms to accurately predict the overall survival (OS) and progression-free survival (PFS) in patients with non-small cell lung cancer (NSCLC) who received chemotherapy alone as the first-line treatment.

Authors

  • Runsheng Chang
    College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
  • Shouliang Qi
    Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Life Science Building, 500 Zhihui Street, Hun'nan District, Shenyang, 110169, China. [email protected].
  • Yanan Wu
    School of Physics and Optoelectronic Engineering, Ludong University, Yantai, Shandong 264025, China.
  • Yong Yue
    Department of Radiology, Shengjing Hospital of China Medical University, No. 36 Sanhao Street, Shenyang, 110004, China.
  • Xiaoye Zhang
    Department of Oncology, Shengjing Hospital of China Medical University, Shenyang, China.
  • Wei Qian
    Department of Electrical and Computer Engineering, University of Texas at El Paso, 500 West University Avenue, El Paso, TX 79968, USA; Sino-Dutch Biomedical and Information Engineering School, Northeastern University, No.11, Lane 3, Wenhua Road, Heping District, Shenyang, Liaoning 110819, China. Electronic address: [email protected].

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