Integration of Deep Learning Radiomics and Counts of Circulating Tumor Cells Improves Prediction of Outcomes of Early Stage NSCLC Patients Treated With Stereotactic Body Radiation Therapy.

Journal: International journal of radiation oncology, biology, physics
PMID: 34775000

Abstract

PURPOSE: We develop a deep learning (DL) radiomics model and integrate it with circulating tumor cell (CTC) counts as a clinically useful prognostic marker for predicting recurrence outcomes of early-stage (ES) non-small cell lung cancer (NSCLC) patients treated with stereotactic body radiation therapy (SBRT).

Authors

  • Zhicheng Jiao
  • Hongming Li
    6Center for Biomedical Image Computing and Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
  • Ying Xiao
    Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, USA.
  • Jay Dorsey
    Department of Radiation Oncology, Perelman School of Medicine, The University of Pennsylvania, Philadelphia, Pennsylvania.
  • Charles B Simone
    Department of Radiation Oncology, University of Maryland Medical Center.
  • Steven Feigenberg
    Department of Radiation Oncology, Perelman School of Medicine, The University of Pennsylvania, Philadelphia, Pennsylvania.
  • Gary Kao
    Department of Radiation Oncology, Perelman School of Medicine, The University of Pennsylvania, Philadelphia, Pennsylvania.
  • Yong Fan
    CPB/ECMO Children's Hospital, Zhejiang University School of Medicine, 310052 Hangzhou, Zhejiang, China.

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