TY - JOUR
T1 - Radiomics to predict immunotherapy-induced pneumonitis
T2 - proof of concept
AU - Colen, Rivka R.
AU - Fujii, Takeo
AU - Bilen, Mehmet Asim
AU - Kotrotsou, Aikaterini
AU - Abrol, Srishti
AU - Hess, Kenneth R.
AU - Hajjar, Joud
AU - Suarez-Almazor, Maria E.
AU - Alshawa, Anas
AU - Hong, David S.
AU - Giniebra-Camejo, Dunia
AU - Stephen, Bettzy
AU - Subbiah, Vivek
AU - Sheshadri, Ajay
AU - Mendoza, Tito
AU - Fu, Siqing
AU - Sharma, Padmanee
AU - Meric-Bernstam, Funda
AU - Naing, Aung
N1 - Publisher Copyright:
© 2017, Springer Science+Business Media, LLC.
PY - 2018/8/1
Y1 - 2018/8/1
N2 - 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.
AB - 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.
KW - Immune-related adverse event
KW - Immunotherapy
KW - Pneumonitis
KW - Radiomics
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U2 - 10.1007/s10637-017-0524-2
DO - 10.1007/s10637-017-0524-2
M3 - Article
C2 - 29075985
AN - SCOPUS:85032392480
SN - 0167-6997
VL - 36
SP - 601
EP - 607
JO - Investigational New Drugs
JF - Investigational New Drugs
IS - 4
ER -