Predicting Survival and Recurrence of Lung Ablation Patients Using Deep Learning-Based Automatic Segmentation and Radiomics Analysis.

Journal: Cardiovascular and interventional radiology
PMID: 39604700

Abstract

PURPOSE: To predict survival and tumor recurrence following image-guided thermal ablation (IGTA) of lung tumors segmented using a deep learning approach.

Authors

  • Hossam A Zaki
    Department of Diagnostic Imaging, The Warren Alpert Medical School of Brown University/Rhode Island Hospital, Providence, RI, USA. hossam_zaki@brown.edu.
  • Karim Oueidat
    Department of Diagnostic Imaging, The Warren Alpert Medical School of Brown University/Rhode Island Hospital, Providence, RI, USA.
  • Celina Hsieh
    Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI, USA.
  • Helen Zhang
    The Warren Alpert Medical School of Brown University, Providence, RI, 02903, USA.
  • Scott Collins
    Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI, USA.
  • Zhicheng Jiao
  • Aaron W P Maxwell
    Department of Diagnostic Imaging, Warren Alpert Medical School of Brown University, Providence, RI, USA.

Keywords

External Resources

Popular Topics
Recent Journals