Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers

Ann Oncol. 2019 Jun 1;30(6):998-1004. doi: 10.1093/annonc/mdz108.


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 < 0.001) and borderline significant for melanoma lymph nodes (0.64 AUC, P = 0.05). Combining these lesion-wide predictions on a patient level, immunotherapy response could be predicted with an AUC of up to 0.76 for both cancer types (P < 0.001), resulting in a 1-year survival difference of 24% (P = 0.02). We found highly significant associations with pathways involved in mitosis, indicating a relationship between increased proliferative potential and preferential response to immunotherapy.

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.

Keywords: artificial intelligence; immunotherapy; machine learning; medical imaging; radiomics; response prediction.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Antineoplastic Combined Chemotherapy Protocols / therapeutic use
  • Artificial Intelligence*
  • Carcinoma, Non-Small-Cell Lung / diagnostic imaging*
  • Carcinoma, Non-Small-Cell Lung / drug therapy*
  • Carcinoma, Non-Small-Cell Lung / immunology
  • Carcinoma, Non-Small-Cell Lung / pathology
  • Follow-Up Studies
  • Humans
  • Immunotherapy / methods
  • Lung Neoplasms / diagnostic imaging*
  • Lung Neoplasms / drug therapy*
  • Lung Neoplasms / immunology
  • Lung Neoplasms / pathology
  • Machine Learning
  • Melanoma / diagnostic imaging
  • Melanoma / drug therapy*
  • Melanoma / immunology
  • Melanoma / pathology*
  • Predictive Value of Tests
  • Prognosis
  • Programmed Cell Death 1 Receptor / antagonists & inhibitors
  • Programmed Cell Death 1 Receptor / immunology
  • Survival Rate
  • Tomography, X-Ray Computed / methods


  • PDCD1 protein, human
  • Programmed Cell Death 1 Receptor