Radiomics to predict immunotherapy-induced pneumonitis: proof of concept

Summary We present the first reported work that explores the potential of radiomics to predict patients who are at risk for developing immunotherapy-induced pneumonitis. Despite promising results with immunotherapies, immune-related adverse events (irAEs) are challenging. Although less common, pneum...

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Bibliographic Details
Published in:Investigational new drugs Vol. 36; no. 4; pp. 601 - 607
Main Authors: Colen, Rivka R., Fujii, Takeo, Bilen, Mehmet Asim, Kotrotsou, Aikaterini, Abrol, Srishti, Hess, Kenneth R., Hajjar, Joud, Suarez-Almazor, Maria E., Alshawa, Anas, Hong, David S., Giniebra-Camejo, Dunia, Stephen, Bettzy, Subbiah, Vivek, Sheshadri, Ajay, Mendoza, Tito, Fu, Siqing, Sharma, Padmanee, Meric-Bernstam, Funda, Naing, Aung
Format: Journal Article
Language:English
Published: New York Springer US 01.08.2018
Springer Nature B.V
Subjects:
Adult
Aged
Biomarkers
Biomarkers - metabolism
Clinical outcomes
Computed tomography
Feature extraction
Female
Humans
Immunologic Factors - metabolism
Immunotherapy
Immunotherapy - adverse effects
Male
Medical imaging
Medicine
Medicine & Public Health
Middle Aged
Oncology
Patients
Pharmacology/Toxicology
Phase I Studies
Pilot Projects
Pneumonia - etiology
Pneumonia - metabolism
Pneumonitis
Radiomics
Redundancy
Risk
Studies
Treatment Outcome
Young Adult
Pneumonitis
Immune-related adverse event
Immunotherapy
Radiomics
ISSN:0167-6997, 1573-0646, 1573-0646
Online Access:Get full text
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Summary:Summary We present the first reported work that explores the potential of radiomics to predict patients who are at risk for developing immunotherapy-induced pneumonitis. Despite promising results with immunotherapies, immune-related adverse events (irAEs) are challenging. Although less common, pneumonitis is a potentially fatal irAE. Thus, early detection is critical for improving treatment outcomes; an urgent need to identify biomarkers that predict patients at risk for pneumonitis exists. Radiomics, an emerging field, is the automated extraction of high fidelity, high-dimensional imaging features from standard medical images and allows for comprehensive visualization and characterization of the tissue of interest and corresponding microenvironment. In this pilot study, we sought to determine whether radiomics has the potential to predict development of pneumonitis. We performed radiomic analyses using baseline chest computed tomography images of patients who did ( N  = 2) and did not ( N  = 30) develop immunotherapy-induced pneumonitis. We extracted 1860 radiomic features in each patient. Maximum relevance and minimum redundancy feature selection method, anomaly detection algorithm, and leave-one-out cross-validation identified radiomic features that were significantly different and predicted subsequent immunotherapy-induced pneumonitis (accuracy, 100% [ p  = 0.0033]). This study suggests that radiomic features can classify and predict those patients at baseline who will subsequently develop immunotherapy-induced pneumonitis, further enabling risk-stratification that will ultimately lead to better treatment outcomes.
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ISSN:0167-6997
1573-0646
1573-0646
DOI:10.1007/s10637-017-0524-2