@article{d7454be66b2945bf890a50beb320859b,
title = "Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers",
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, PConclusions 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 = "BLOCKADE, CELL LUNG-CANCER, DOCETAXEL, EXPRESSION, FEATURES, NIVOLUMAB, PD-1, SELECTION, SENSITIVITY, SIGNATURES, artificial intelligence, immunotherapy, machine learning, medical imaging, radiomics, response prediction, RISK",
author = "S. Trebeschi and Drago, {S. G.} and Birkbak, {N. J.} and I. Kurilova and Calin, {A. M.} and Pizzi, {A. Delli} and F. Lalezari and Lambregts, {D. M. J.} and Rohaan, {M. W.} and C. Parmar and Rozeman, {E. A.} and Hartemink, {K. J.} and C. Swanton and Haanen, {J. B. A. G.} and Blank, {C. U.} and Smit, {E. F.} and Beets-Tan, {R. G. H.} and Aerts, {H. J. W. L.}",
note = "Funding Information: This work was supported by the Dutch national e-infrastructure with the support of the SURF Cooperative. The authors acknowledge financial support from the Informatics Technology for Cancer Research (ITCR) program (NIH-USA U24CA194354) and the Quantitative Imaging Network (QIN) program (NIH-USA U01CA190234) of the NIH. Funding Information: CS reports grant support from Cancer Research UK, UCLH Biomedical Research Council, and Rosetrees Trust, AstraZeneca and personal fees from Boehringer Ingelheim, Novartis, Eli Lilly, Roche, GlaxoSmithKline, Pfizer, Servier, MSD, BMS, AstraZeneca, Illumina, Sarah Canon Research Institute and Celgene. CS also reports stock options in GRAIL, APOGEN Biotechnologies, and EPIC Bioscience and has stock options and is co-founder of Achilles Therapeutics. HJWLA reports shares from Genospace and Sphera, outside of the submitted work. All remaining authors have declared no conflicts of interest. Funding Information: This work was supported by the Dutch national e-infrastructure with the support of the SURF Cooperative. The authors acknowledge financial support from the Informatics Technology for Cancer Research (ITCR) program(NIH-USA U24CA194354) and the Quantitative Imaging Network (QIN) program (NIHUSA U01CA190234) of the NIH. Publisher Copyright: {\textcopyright} 2019 The Author(s) 2019. Published by Oxford University Press on behalf of the European Society for Medical Oncology.",
year = "2019",
month = jun,
doi = "10.1093/annonc/mdz108",
language = "English",
volume = "30",
pages = "998--1004",
journal = "Annals of Oncology",
issn = "0923-7534",
publisher = "Oxford University Press",
number = "6",
}