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Defining The Biological Basis Of Radiomic Phenotypes In Lung Cancer

P. Grossmann, O. Stringfield, N. El-Hachem, M. Bui, E. Rios Velazquez, Chintan Parmar, R. Leijenaar, B. Haibe-Kains, P. Lambin, R. Gillies, H. Aerts
Published 2017 · Biology, Medicine

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Medical imaging can visualize characteristics of human cancer noninvasively. Radiomics is an emerging field that translates these medical images into quantitative data to enable phenotypic profiling of tumors. While radiomics has been associated with several clinical endpoints, the complex relationships of radiomics, clinical factors, and tumor biology are largely unknown. To this end, we analyzed two independent cohorts of respectively 262 North American and 89 European patients with lung cancer, and consistently identified previously undescribed associations between radiomic imaging features, molecular pathways, and clinical factors. In particular, we found a relationship between imaging features, immune response, inflammation, and survival, which was further validated by immunohistochemical staining. Moreover, a number of imaging features showed predictive value for specific pathways; for example, intra-tumor heterogeneity features predicted activity of RNA polymerase transcription (AUC = 0.62, p=0.03) and intensity dispersion was predictive of the autodegration pathway of a ubiquitin ligase (AUC = 0.69, p<10-4). Finally, we observed that prognostic biomarkers performed highest when combining radiomic, genetic, and clinical information (CI = 0.73, p<10-9) indicating complementary value of these data. In conclusion, we demonstrate that radiomic approaches permit noninvasive assessment of both molecular and clinical characteristics of tumors, and therefore have the potential to advance clinical decision-making by systematically analyzing standard-of-care medical images. DOI: http://dx.doi.org/10.7554/eLife.23421.001
This paper references
10.1148/radiol.14131731
Glioblastoma multiforme: exploratory radiogenomic analysis by using quantitative image features.
O. Gevaert (2014)
10.1148/radiol.13132195
Behind the numbers: Decoding molecular phenotypes with radiogenomics--guiding principles and technical considerations.
M. Kuo (2014)
10.1001/jamaoncol.2016.2631
The Potential of Radiomic-Based Phenotyping in Precision Medicine: A Review.
H. Aerts (2016)
10.1073/pnas.1219747110
Intratumor heterogeneity in human glioblastoma reflects cancer evolutionary dynamics
A. Sottoriva (2013)
10.1097/CCO.0b013e32835b6386
Interactions between the tumor suppressor p53 and immune responses
D. Menéndez (2013)
10.1111/j.1420-9101.2005.00917.x
Combining probability from independent tests: the weighted Z‐method is superior to Fisher's approach
M. Whitlock (2005)
10.1371/journal.pone.0010312
Gene Expression-Based Classification of Non-Small Cell Lung Carcinomas and Survival Prediction
J. Hou (2010)
10.1371/journal.pone.0118261
Quantitative Computed Tomographic Descriptors Associate Tumor Shape Complexity and Intratumor Heterogeneity with Prognosis in Lung Adenocarcinoma
O. Grove (2015)
10.1073/pnas.0801279105
Identification of noninvasive imaging surrogates for brain tumor gene-expression modules
M. Diehn (2008)
10.4329/wjr.v4.i4.128
The 7th lung cancer TNM classification and staging system: Review of the changes and implications.
S. Mirsadraee (2012)
10.1200/JCO.2015.65.9128
Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer.
Yanqi Huang (2016)
10.1038/ncomms5006
Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
H. Aerts (2014)
10.1093/bioinformatics/btg405
affy - analysis of Affymetrix GeneChip data at the probe level
L. Gautier (2004)
10.1001/JAMA.1982.03320430047030
Evaluating the yield of medical tests.
F. Harrell (1982)
10.1016/j.ijrobp.2014.07.020
Prognostic value and reproducibility of pretreatment CT texture features in stage III non-small cell lung cancer.
D. Fried (2014)
10.1016/j.ijrobp.2015.12.369
Detection of Local Cancer Recurrence After Stereotactic Ablative Radiation Therapy for Lung Cancer: Physician Performance Versus Radiomic Assessment.
Sarah A. Mattonen (2016)
10.2967/jnumed.112.107375
Are Pretreatment 18F-FDG PET Tumor Textural Features in Non–Small Cell Lung Cancer Associated with Response and Survival After Chemoradiotherapy?
G. Cook (2013)
10.1007/s00234-015-1576-7
Somatic mutations associated with MRI-derived volumetric features in glioblastoma
D. Gutman (2015)
10.1111/J.2517-6161.1995.TB02031.X
Controlling the false discovery rate: a practical and powerful approach to multiple testing
Y. Benjamini (1995)
10.1148/radiol.12112428
Non-small cell lung cancer: histopathologic correlates for texture parameters at CT.
B. Ganeshan (2013)
10.1148/radiol.12111607
Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data--methods and preliminary results.
O. Gevaert (2012)
10.1093/bioinformatics/btr511
survcomp: an R/Bioconductor package for performance assessment and comparison of survival models
Markus S. Schröder (2011)
10.1016/j.mri.2012.06.010
Radiomics: the process and the challenges.
Virendra Kumar (2012)
10.1016/j.ejrad.2009.01.050
Radiogenomics: creating a link between molecular diagnostics and diagnostic imaging.
Aaron M Rutman (2009)
10.1097/MD.0000000000001753
Decoding Tumor Phenotypes for ALK, ROS1, and RET Fusions in Lung Adenocarcinoma Using a Radiomics Approach
Hyun Jung Yoon (2015)
10.4061/2011/605042
Tumor Suppressors and Cell-Cycle Proteins in Lung Cancer
A. Baldi (2011)
10.1158/1078-0432.CCR-12-1307
Tumor Heterogeneity and Permeability as Measured on the CT Component of PET/CT Predict Survival in Patients with Non–Small Cell Lung Cancer
T. Win (2013)
10.1148/radiol.2015151169
Radiomics: Images Are More than Pictures, They Are Data
R. Gillies (2016)
10.1038/srep23428
Reproducibility of radiomics for deciphering tumor phenotype with imaging
B. Zhao (2016)
10.1103/PhysRevE.67.031902
Iterative signature algorithm for the analysis of large-scale gene expression data.
S. Bergmann (2003)
10.1056/NEJMoa1113205
Intratumor heterogeneity and branched evolution revealed by multiregion sequencing.
M. Gerlinger (2012)
10.1186/1471-2105-14-7
GSVA: gene set variation analysis for microarray and RNA-Seq data
Sonja Hänzelmann (2012)
10.1016/j.radonc.2015.02.015
CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma.
T. Coroller (2015)
10.1093/bioinformatics/btt383
mRMRe: an R package for parallelized mRMR ensemble feature selection
Nicolas De Jay (2013)
10.3389/fonc.2015.00272
Radiomic Machine-Learning Classifiers for Prognostic Biomarkers of Head and Neck Cancer
Chintan Parmar (2015)
10.1148/RADIOL.2016152110
MR Imaging Radiomics Signatures for Predicting the Risk of Breast Cancer Recurrence as Given by Research Versions of MammaPrint, Oncotype DX, and PAM50 Gene Assays.
H. Li (2016)
10.3109/0284186X.2013.812798
Stability of FDG-PET Radiomics features: An integrated analysis of test-retest and inter-observer variability
R. Leijenaar (2013)
10.2307/807379
A Fourth Edition
G. Horn (1945)
10.1186/s12885-016-2659-5
Imaging-genomics reveals driving pathways of MRI derived volumetric tumor phenotype features in Glioblastoma
P. Grossmann (2016)
10.1158/1078-0432.CCR-09-1572
A Four-Gene Signature from NCI-60 Cell Line for Survival Prediction in Non–Small Cell Lung Cancer
Yi-Chiung Hsu (2009)
10.1016/j.lungcan.2014.08.003
Targeting hypoxia in the treatment of small cell lung cancer.
J.L. Bryant (2014)
10.1016/S0031-3203(96)00142-2
The use of the area under the ROC curve in the evaluation of machine learning algorithms
A. Bradley (1997)
10.1634/theoncologist.13-S4-2
The mechanism of anti-CTLA-4 activity and the negative regulation of T-cell activation.
J. Wolchok (2008)
10.1186/1471-2091-14-1
The COP1 E3-ligase interacts with FIP200, a key regulator of mammalian autophagy
S. Kobayashi (2012)
10.1002/SIM.1802
Overall C as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation.
M. Pencina (2004)
10.7603/S40730-014-0022-5
Constitutive Photomorphogensis Protein1 (COP1) mediated p53 pathway and its oncogenic role
M. G. Rabbani (2014)
10.1016/j.patrec.2005.10.010
An introduction to ROC analysis
T. Fawcett (2006)
10.1038/nbt1306
Decoding global gene expression programs in liver cancer by noninvasive imaging
E. Segal (2007)
10.18632/oncotarget.11693
Quantitative image variables reflect the intratumoral pathologic heterogeneity of lung adenocarcinoma
E. Choi (2016)
database ( MSigDB ) 3 . 0
Y Liu (2016)
10.1148/RADIOL.2016152234
Radiomics Signature: A Potential Biomarker for the Prediction of Disease-Free Survival in Early-Stage (I or II) Non-Small Cell Lung Cancer.
Yanqi Huang (2016)
10.1158/1078-0432.CCR-14-0990
Imaging Intratumor Heterogeneity: Role in Therapy Response, Resistance, and Clinical Outcome
J. O'Connor (2014)
10.1038/nrclinonc.2016.162
Imaging biomarker roadmap for cancer studies
J. O’Connor (2017)
10.1102/1470-7330.2010.0021
Texture analysis of non-small cell lung cancer on unenhanced computed tomography: initial evidence for a relationship with tumour glucose metabolism and stage
B. Ganeshan (2010)
10.1016/j.neurad.2014.02.006
Addition of MR imaging features and genetic biomarkers strengthens glioblastoma survival prediction in TCGA patients.
M. Nicolas-Jilwan (2015)
10.1038/srep33860
Defining a Radiomic Response Phenotype: A Pilot Study using targeted therapy in NSCLC
H. Aerts (2016)
10.1016/j.ejca.2011.11.036
Radiomics: extracting more information from medical images using advanced feature analysis.
P. Lambin (2012)
10.2217/fon.10.1
Hypoxia-activated prodrugs in cancer therapy: progress to the clinic.
W. Denny (2010)
10.1593/NEO.03490
Aberrant gene expression in human non small cell lung carcinoma cells exposed to demethylating agent 5-aza-2'-deoxycytidine.
Bao-Zhu Yuan (2004)
10.1200/JCO.2014.59.4358
Immune Checkpoint Blockade in Cancer Therapy.
M. Postow (2015)
10.1093/JNCI/95.13.961
p53 mutations and survival in stage I non-small-cell lung cancer: results of a prospective study.
S. Ahrendt (2003)
Molecular
A Liberzon (2011)
10.1371/journal.pone.0088598
Quantitative CT Variables Enabling Response Prediction in Neoadjuvant Therapy with EGFR-TKIs: Are They Different from Those in Neoadjuvant Concurrent Chemoradiotherapy?
Y. Chong (2014)
10.1007/s00330-015-3814-0
CT Radiogenomic Characterization of EGFR, K-RAS, and ALK Mutations in Non-Small Cell Lung Cancer
S. Rizzo (2015)
10.1007/s00330-011-2319-8
Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: a potential marker of survival
B. Ganeshan (2011)
10.1093/BIOSTATISTICS/4.2.249
Exploration, normalization, and summaries of high density oligonucleotide array probe level data.
R. Irizarry (2003)
10.1038/nrc3239
The blockade of immune checkpoints in cancer immunotherapy
D. Pardoll (2012)
10.1093/bioinformatics/btr260
Molecular signatures database (MSigDB) 3.0
A. Liberzon (2011)
10.1016/j.crad.2010.04.005
The biology underlying molecular imaging in oncology: from genome to anatome and back again.
R. Gillies (2010)
10.1093/bioinformatics/btq130
Modular analysis of gene expression data with R
Gábor Csárdi (2010)
10.1038/srep11044
Radiomic feature clusters and Prognostic Signatures specific for Lung and Head & Neck cancer
Chintan Parmar (2015)
10.1016/j.cllc.2016.02.001
Radiomic Features Are Associated With EGFR Mutation Status in Lung Adenocarcinomas.
Y. Liu (2016)
10.1038/bjc.2012.581
Cancer heterogeneity: implications for targeted therapeutics
R. Fisher (2013)
R: A language and environment for statistical computing.
R. Team (2014)
10.1038/nrclinonc.2014.158
Translational research in oncology—10 years of progress and future prospects
J. Doroshow (2014)
10.1073/pnas.0506580102
Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles
Aravind Subramanian (2005)
10.1056/NEJMOA060096
A five-gene signature and clinical outcome in non-small-cell lung cancer.
H. Chen (2007)
10.1593/TLO.13844
Reproducibility and Prognosis of Quantitative Features Extracted from CT Images.
Y. Balagurunathan (2014)
Fourth Edition. Pattern Recognition
S Theodoridis (2008)
10.1007/11915034_95
Reactome - A Knowledgebase of Biological Pathways
E. Schmidt (2005)
10.1093/neuonc/nox092
Quantitative imaging biomarkers for risk stratification of patients with recurrent glioblastoma treated with bevacizumab
P. Grossmann (2017)



This paper is referenced by
10.1007/s00330-018-5949-2
Integrative nomogram of CT imaging, clinical, and hematological features for survival prediction of patients with locally advanced non-small cell lung cancer
L. Wang (2018)
10.1177/1758835920971416
A deep-learning-based prognostic nomogram integrating microscopic digital pathology and macroscopic magnetic resonance images in nasopharyngeal carcinoma: a multi-cohort study
F. Zhang (2020)
10.1109/BIBM.2018.8621276
Radiomics for Predicting CyberKnife response in acoustic neuroma: a pilot study
N. C. D'Amico (2018)
10.1002/mp.13202
Identification of optimal mother wavelets in survival prediction of lung cancer patients using wavelet decomposition‐based radiomic features
Mazen Soufi (2018)
10.1101/190561
Combining radiomics and mathematical modeling to elucidate mechanisms of resistance to immune checkpoint blockade in non-small cell lung cancer
Daryoush Saeed-Vafa (2017)
10.1109/EMBC44109.2020.9175746
Predicting Local Failure after Stereotactic Radiation Therapy in Brain Metastasis using Quantitative CT and Machine Learning*
M. Jaberipour (2020)
10.1177/2397198319894851
Circulating biomarkers of systemic sclerosis – interstitial lung disease
A. Hoffmann-Vold (2020)
10.1016/j.canrad.2018.01.003
Adaptive radiotherapy for head and neck cancers: Fact or fallacy to improve therapeutic ratio?
Y. Q. Li (2018)
10.1016/j.ijrobp.2018.06.023
The Use of Quantitative Imaging in Radiation Oncology: A Quantitative Imaging Network (QIN) Perspective.
Robert H Press (2018)
10.7150/thno.48027
Assessing PD-L1 expression in non-small cell lung cancer and predicting responses to immune checkpoint inhibitors using deep learning on computed tomography images
P. Tian (2021)
10.1016/j.critrevonc.2020.102985
Radiomics in cervical cancer: Current applications and future potential.
Y. Ai (2020)
10.3389/fonc.2020.593831
Integrating Liquid Biopsy and Radiomics to Monitor Clonal Heterogeneity of EGFR-Positive Non-Small Cell Lung Cancer
Federico Cucchiara (2020)
10.3389/fonc.2020.00593
Differentiating Peripherally-Located Small Cell Lung Cancer From Non-small Cell Lung Cancer Using a CT Radiomic Approach
B. Chen (2020)
Emergence of Radiomics: Novel Methodology Identifying Imaging Biomarkers of Disease in Diagnosis, Response, and Progression
E. Florez (2019)
10.1002/jmri.25969
Radiomic features of pretreatment MRI could identify T stage in patients with rectal cancer: Preliminary findings
Y. Sun (2018)
10.1101/681577
A novel imaging biomarker for survival prediction in EGFR-mutated NSCLC patients treated with TKI
A. Collin (2019)
10.1007/s00066-020-01625-9
Radiomics and deep learning in lung cancer
M. Avanzo (2020)
10.1016/j.ijrobp.2018.01.006
A Quantitative CT Imaging Signature Predicts Survival and Complements Established Prognosticators in Stage I Non-Small Cell Lung Cancer.
Juheon Lee (2018)
10.1001/jamanetworkopen.2019.2561
Association of Peritumoral Radiomics With Tumor Biology and Pathologic Response to Preoperative Targeted Therapy for HER2 (ERBB2)–Positive Breast Cancer
Nathaniel Braman (2019)
10.1007/s00330-019-06073-3
Adaptive statistical iterative reconstruction (ASIR) affects CT radiomics quantification in primary colorectal cancer
D. Prezzi (2019)
10.26481/DIS.20180308PG
Defining the biological and clinical basis of radiomics: towards clinical imaging biomarkers
P. Grossmann (2018)
10.1016/j.radonc.2018.06.025
Unsupervised machine learning of radiomic features for predicting treatment response and overall survival of early stage non-small cell lung cancer patients treated with stereotactic body radiation therapy.
Hongming Li (2018)
Convolutional Neural Networks for the Automated Segmentation and Recurrence Risk Prediction of Surgically Resected Lung Tumors
Marguerite B. Basta (2020)
10.1007/s40336-018-0299-2
Heterogeneity analysis of 18F-FDG PET imaging in oncology: clinical indications and perspectives
P. Lovinfosse (2018)
10.1038/s41598-019-48488-4
Predicting Breast Cancer in Breast Imaging Reporting and Data System (BI-RADS) Ultrasound Category 4 or 5 Lesions: A Nomogram Combining Radiomics and BI-RADS
Wei-quan Luo (2019)
10.1016/j.radonc.2018.10.027
Vulnerabilities of radiomic signature development: The need for safeguards.
Mattea L Welch (2019)
10.1101/304105
Locoregional Radiogenomic Models Capture Gene Expression Heterogeneity in Glioblastoma
A. Depeursinge (2018)
10.1259/bjr.20190271
Comparison of radiomics tools for image analyses and clinical prediction in nasopharyngeal carcinoma
Zhong-Guo Liang (2019)
10.1007/s00259-019-04372-x
Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics
M. Sollini (2019)
10.1109/CBMS.2018.00051
Exploratory Radiomics for Predicting Adaptive Radiotherapy in Non-Small Cell Lung Cancer
R. Sicilia (2018)
10.31557/apjcb.2020.5.4.189-199
Reviewing the Role of Artificial Intelligence in Cancer
Shankargouda Patil (2020)
10.1158/1078-0432.CCR-20-0020
Radiomics, Tumor Volume, and Blood Biomarkers for Early Prediction of Pseudoprogression in Patients with Metastatic Melanoma Treated with Immune Checkpoint Inhibition
L. Basler (2020)
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