Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers

S. Trebeschi, S. G. Drago, N. J. Birkbak, I. Kurilova, A. M. Calin, A. Delli Pizzi, F. Lalezari, D. M. J. Lambregts, M. W. Rohaan, C. Parmar, E. A. Rozeman, K. J. Hartemink, C. Swanton, J. B. A. G. Haanen, C. U. Blank, E. F. Smit, R. G. H. Beets-Tan, H. J. W. L. Aerts*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Original languageEnglish
Pages (from-to)998-1004
Number of pages7
JournalAnnals of Oncology
Volume30
Issue number6
DOIs
Publication statusPublished - Jun 2019

Keywords

  • BLOCKADE
  • CELL LUNG-CANCER
  • DOCETAXEL
  • EXPRESSION
  • FEATURES
  • NIVOLUMAB
  • PD-1
  • SELECTION
  • SENSITIVITY
  • SIGNATURES
  • artificial intelligence
  • immunotherapy
  • machine learning
  • medical imaging
  • radiomics
  • response prediction
  • RISK

Cite this

Trebeschi, S., Drago, S. G., Birkbak, N. J., Kurilova, I., Calin, A. M., Pizzi, A. D., Lalezari, F., Lambregts, D. M. J., Rohaan, M. W., Parmar, C., Rozeman, E. A., Hartemink, K. J., Swanton, C., Haanen, J. B. A. G., Blank, C. U., Smit, E. F., Beets-Tan, R. G. H., & Aerts, H. J. W. L. (2019). Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers. Annals of Oncology, 30(6), 998-1004. https://doi.org/10.1093/annonc/mdz108