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

192 Citations (Web of Science)

Abstract

Introduction Immunotherapy is regarded as one of the major breakthroughs in cancer treatment. Despite its success, only a subset of patients responds-urging the quest for predictive biomarkers. We hypothesize that artificial intelligence (AI) algorithms can automatically quantify radiographic characteristics that are related to and may therefore act as noninvasive radiomic biomarkers for immunotherapy response.

Patients and methods In this study, we analyzed 1055 primary and metastatic lesions from 203 patients with advanced melanoma and non-small-cell lung cancer (NSCLC) undergoing anti-PD1 therapy. We carried out an AI-based characterization of each lesion on the pretreatment contrast-enhanced CT imaging data to develop and validate a noninvasive machine learning biomarker capable of distinguishing between immunotherapy responding and nonresponding. To define the biological basis of the radiographic biomarker, we carried out gene set enrichment analysis in an independent dataset of 262 NSCLC patients.

Results The biomarker reached significant performance on NSCLC lesions (up to 0.83 AUC, P

Conclusions These results indicate that radiographic characteristics of lesions on standard-of-care imaging may function as noninvasive biomarkers for response to immunotherapy, and may show utility for improved patient stratification in both neoadjuvant and palliative settings.

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

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