TY - JOUR
T1 - Development of an Immune-Pathology Informed Radiomics Model for Non-Small Cell Lung Cancer
AU - Tang, Chad
AU - Hobbs, Brian
AU - Amer, Ahmed
AU - Li, Xiao
AU - Behrens, Carmen
AU - Canales, Jaime Rodriguez
AU - Cuentas, Edwin Parra
AU - Villalobos, Pamela
AU - Fried, David
AU - Chang, Joe Y.
AU - Hong, David S.
AU - Welsh, James W.
AU - Sepesi, Boris
AU - Court, Laurence
AU - Wistuba, Ignacio I.
AU - Koay, Eugene J.
N1 - Funding Information:
We gratefully acknowledge help from Christine Wogan in preparing this manuscript. We also acknowledge support from the Andrew Sabin Family Fellowship, Center for Radiation Oncology Research, the Sheikh Ahmed Center for Pancreatic Cancer Research, institutional funds from the The University of Texas MD Anderson Cancer Center, equipment support from GE Healthcare and the Center for Advanced Biomedical Imaging (CABI), Philips Healthcare, and Cancer Center Support (Core) Grant CA016672 from the National Cancer Institute to MD Anderson. Dr. Chad Tang was supported by a Young Investigator Award from the American Society of Clinical Oncology and Radioloigcal Society of North America. Dr. Eugene Koay was supported by NIH (U54CA210181-01, U54CA143837, U01CA200468, and U01CA196403), the Pancreatic Cancer Action Network (14-20-25-KOAY and 16-65-SING), and the Radiological Society of North America (RSD1429).
Publisher Copyright:
© 2018 The Author(s).
PY - 2018/12/1
Y1 - 2018/12/1
N2 - With increasing use of immunotherapy agents, pretreatment strategies for identifying responders and non-responders is useful for appropriate treatment assignment. We hypothesize that the local immune micro-environment of NSCLC is associated with patient outcomes and that these local immune features exhibit distinct radiologic characteristics discernible by quantitative imaging metrics. We assembled two cohorts of NSCLC patients treated with definitive surgical resection and extracted quantitative parameters from pretreatment CT imaging. The excised primary tumors were then quantified for percent tumor PDL1 expression and density of tumor-infiltrating lymphocyte (via CD3 count) utilizing immunohistochemistry and automated cell counting. Associating these pretreatment radiomics parameters with tumor immune parameters, we developed an immune pathology-informed model (IPIM) that separated patients into 4 clusters (designated A-D) utilizing 4 radiomics features. The IPIM designation was significantly associated with overall survival in both training (5 year OS: 61%, 41%, 50%, and 91%, for clusters A-D, respectively, P = 0.04) and validation (5 year OS: 55%, 72%, 75%, and 86%, for clusters A-D, respectively, P = 0.002) cohorts and immune pathology (all P < 0.05). Specifically, we identified a favorable outcome group characterized by low CT intensity and high heterogeneity that exhibited low PDL1 and high CD3 infiltration, suggestive of a favorable immune activated state. We have developed a NSCLC radiomics signature based on the immune micro-environment and patient outcomes. This manuscript demonstrates model creation and validation in independent cohorts.
AB - With increasing use of immunotherapy agents, pretreatment strategies for identifying responders and non-responders is useful for appropriate treatment assignment. We hypothesize that the local immune micro-environment of NSCLC is associated with patient outcomes and that these local immune features exhibit distinct radiologic characteristics discernible by quantitative imaging metrics. We assembled two cohorts of NSCLC patients treated with definitive surgical resection and extracted quantitative parameters from pretreatment CT imaging. The excised primary tumors were then quantified for percent tumor PDL1 expression and density of tumor-infiltrating lymphocyte (via CD3 count) utilizing immunohistochemistry and automated cell counting. Associating these pretreatment radiomics parameters with tumor immune parameters, we developed an immune pathology-informed model (IPIM) that separated patients into 4 clusters (designated A-D) utilizing 4 radiomics features. The IPIM designation was significantly associated with overall survival in both training (5 year OS: 61%, 41%, 50%, and 91%, for clusters A-D, respectively, P = 0.04) and validation (5 year OS: 55%, 72%, 75%, and 86%, for clusters A-D, respectively, P = 0.002) cohorts and immune pathology (all P < 0.05). Specifically, we identified a favorable outcome group characterized by low CT intensity and high heterogeneity that exhibited low PDL1 and high CD3 infiltration, suggestive of a favorable immune activated state. We have developed a NSCLC radiomics signature based on the immune micro-environment and patient outcomes. This manuscript demonstrates model creation and validation in independent cohorts.
UR - http://www.scopus.com/inward/record.url?scp=85041576870&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85041576870&partnerID=8YFLogxK
U2 - 10.1038/s41598-018-20471-5
DO - 10.1038/s41598-018-20471-5
M3 - Article
C2 - 29386574
AN - SCOPUS:85041576870
VL - 8
JO - Scientific Reports
JF - Scientific Reports
SN - 2045-2322
IS - 1
M1 - 1922
ER -