Development of an Immune-Pathology Informed Radiomics Model for Non-Small Cell Lung Cancer

Research output: Contribution to journalArticle

Chad Tang, Brian Hobbs, Ahmed Amer, Xiao Li, Carmen Behrens, Jaime Rodriguez Canales, Edwin Parra Cuentas, Pamela Villalobos, David Fried, Joe Y. Chang, David S. Hong, James W. Welsh, Boris Sepesi, Laurence Court, Ignacio I. Wistuba, Eugene J. Koay

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.

Original languageEnglish (US)
Article number1922
JournalScientific Reports
Volume8
Issue number1
DOIs
StatePublished - Dec 1 2018

Altmetrics

Cite this

Standard

Development of an Immune-Pathology Informed Radiomics Model for Non-Small Cell Lung Cancer. / Tang, Chad; Hobbs, Brian; Amer, Ahmed; Li, Xiao; Behrens, Carmen; Canales, Jaime Rodriguez; Cuentas, Edwin Parra; Villalobos, Pamela; Fried, David; Chang, Joe Y.; Hong, David S.; Welsh, James W.; Sepesi, Boris; Court, Laurence; Wistuba, Ignacio I.; Koay, Eugene J.

In: Scientific Reports, Vol. 8, No. 1, 1922, 01.12.2018.

Research output: Contribution to journalArticle

Harvard

Tang, C, Hobbs, B, Amer, A, Li, X, Behrens, C, Canales, JR, Cuentas, EP, Villalobos, P, Fried, D, Chang, JY, Hong, DS, Welsh, JW, Sepesi, B, Court, L, Wistuba, II & Koay, EJ 2018, 'Development of an Immune-Pathology Informed Radiomics Model for Non-Small Cell Lung Cancer' Scientific Reports, vol. 8, no. 1, 1922. DOI: 10.1038/s41598-018-20471-5

APA

Tang, C., Hobbs, B., Amer, A., Li, X., Behrens, C., Canales, J. R., ... Koay, E. J. (2018). Development of an Immune-Pathology Informed Radiomics Model for Non-Small Cell Lung Cancer. DOI: 10.1038/s41598-018-20471-5

Vancouver

Tang C, Hobbs B, Amer A, Li X, Behrens C, Canales JR et al. Development of an Immune-Pathology Informed Radiomics Model for Non-Small Cell Lung Cancer. Scientific Reports. 2018 Dec 1;8(1). 1922. Available from, DOI: 10.1038/s41598-018-20471-5

Author

Tang, Chad ; Hobbs, Brian ; Amer, Ahmed ; Li, Xiao ; Behrens, Carmen ; Canales, Jaime Rodriguez ; Cuentas, Edwin Parra ; Villalobos, Pamela ; Fried, David ; Chang, Joe Y. ; Hong, David S. ; Welsh, James W. ; Sepesi, Boris ; Court, Laurence ; Wistuba, Ignacio I. ; Koay, Eugene J./ Development of an Immune-Pathology Informed Radiomics Model for Non-Small Cell Lung Cancer. In: Scientific Reports. 2018 ; Vol. 8, No. 1.

BibTeX

@article{c0b491116f9a4dac92a53193f2cea222,
title = "Development of an Immune-Pathology Informed Radiomics Model for Non-Small Cell Lung Cancer",
abstract = "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.",
author = "Chad Tang and Brian Hobbs and Ahmed Amer and Xiao Li and Carmen Behrens and Canales, {Jaime Rodriguez} and Cuentas, {Edwin Parra} and Pamela Villalobos and David Fried and Chang, {Joe Y.} and Hong, {David S.} and Welsh, {James W.} and Boris Sepesi and Laurence Court and Wistuba, {Ignacio I.} and Koay, {Eugene J.}",
year = "2018",
month = "12",
day = "1",
doi = "10.1038/s41598-018-20471-5",
language = "English (US)",
volume = "8",
journal = "Scientific Reports",
issn = "2045-2322",
publisher = "Nature Publishing Group",
number = "1",

}

RIS

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.

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

VL - 8

JO - Scientific Reports

T2 - Scientific Reports

JF - Scientific Reports

SN - 2045-2322

IS - 1

M1 - 1922

ER -

ID: 39164573