Identification of Non–Small Cell Lung Cancer Sensitive to Systemic Cancer Therapies Using Radiomics

Clinical Cancer Research - Tập 26 Số 9 - Trang 2151-2162 - 2020
Laurent Dercle1,2, Matthew Fronheiser3, Lin Lü1, Shuyan Du3, Wendy Hayes3, David Leung3, Amit Roy4, Julia Wilkerson5, Pingzhen Guo1, Antonio Tito Fojo6, Lawrence H. Schwartz1, Binsheng Zhao1
11Department of Radiology, Columbia University Medical Center/New York Presbyterian Hospital, New York, New York.
22Gustave Roussy, Université Paris-Saclay, Villejuif, France.
33Translational Medicine, Bristol-Myers Squibb, Princeton, New Jersey.
44Clinical Pharmacology and Pharmacometrics, Bristol-Myers Squibb, Princeton, New Jersey.
55The National Cancer Institute, NIH, Bethesda, Maryland.
66Columbia University/New York Presbyterian Hospital and James J. Peters VA Medical Center, New York, New York.

Tóm tắt

Abstract Purpose: Using standard-of-care CT images obtained from patients with a diagnosis of non–small cell lung cancer (NSCLC), we defined radiomics signatures predicting the sensitivity of tumors to nivolumab, docetaxel, and gefitinib. Experimental Design: Data were collected prospectively and analyzed retrospectively across multicenter clinical trials [nivolumab, n = 92, CheckMate017 (NCT01642004), CheckMate063 (NCT01721759); docetaxel, n = 50, CheckMate017; gefitinib, n = 46, (NCT00588445)]. Patients were randomized to training or validation cohorts using either a 4:1 ratio (nivolumab: 72T:20V) or a 2:1 ratio (docetaxel: 32T:18V; gefitinib: 31T:15V) to ensure an adequate sample size in the validation set. Radiomics signatures were derived from quantitative analysis of early tumor changes from baseline to first on-treatment assessment. For each patient, 1,160 radiomics features were extracted from the largest measurable lung lesion. Tumors were classified as treatment sensitive or insensitive; reference standard was median progression-free survival (NCT01642004, NCT01721759) or surgery (NCT00588445). Machine learning was implemented to select up to four features to develop a radiomics signature in the training datasets and applied to each patient in the validation datasets to classify treatment sensitivity. Results: The radiomics signatures predicted treatment sensitivity in the validation dataset of each study group with AUC (95 confidence interval): nivolumab, 0.77 (0.55–1.00); docetaxel, 0.67 (0.37–0.96); and gefitinib, 0.82 (0.53–0.97). Using serial radiographic measurements, the magnitude of exponential increase in signature features deciphering tumor volume, invasion of tumor boundaries, or tumor spatial heterogeneity was associated with shorter overall survival. Conclusions: Radiomics signatures predicted tumor sensitivity to treatment in patients with NSCLC, offering an approach that could enhance clinical decision-making to continue systemic therapies and forecast overall survival.

Từ khóa


Tài liệu tham khảo

Yamamoto, 2014, ALK molecular phenotype in non-small cell lung cancer: CT radiogenomic characterization, Radiology, 272, 568, 10.1148/radiol.14140789

Kadota, 2014, Associations between mutations and histologic patterns of mucin in lung adenocarcinoma: invasive mucinous pattern and extracellular mucin are associated with KRAS mutation, Am J Surg Pathol, 38, 1118, 10.1097/PAS.0000000000000246

Kim, 2016, Radiologic characteristics of surgically resected non-small cell lung cancer with ALK rearrangement or EGFR mutations, Ann Thorac Surg, 101, 473, 10.1016/j.athoracsur.2015.07.062

Zhou, 2015, Comparative analysis of clinicoradiologic characteristics of lung adenocarcinomas with ALK rearrangements or EGFR mutations, Eur Radiol, 25, 1257, 10.1007/s00330-014-3516-z

Yang, 2015, EGFR L858R mutation is associated with lung adenocarcinoma patients with dominant ground-glass opacity, Lung Cancer, 87, 272, 10.1016/j.lungcan.2014.12.016

Liu, 2016, CT features associated with epidermal growth factor receptor mutation status in patients with lung adenocarcinoma, Radiology, 280, 271, 10.1148/radiol.2016151455

Rizzo, 2016, CT radiogenomic characterization of EGFR, K-RAS, and ALK mutations in non-small cell lung cancer, Eur Radiol, 26, 32, 10.1007/s00330-015-3814-0

Lee, 2013, Epidermal growth factor receptor mutation in lung adenocarcinomas: relationship with CT characteristics and histologic subtypes, Radiology, 268, 254, 10.1148/radiol.13112553

Choi, 2015, Advanced adenocarcinoma of the lung: comparison of CT characteristics of patients with anaplastic lymphoma kinase gene rearrangement and those with epidermal growth factor receptor mutation, Radiology, 275, 272, 10.1148/radiol.14140848

Shi, 2017, Radiological and clinical features associated with epidermal growth factor receptor mutation status of exon 19 and 21 in lung adenocarcinoma, Sci Rep, 7, 364, 10.1038/s41598-017-00511-2

Hsu, 2014, Correlation between EGFR mutation status and computed tomography features in patients with advanced pulmonary adenocarcinoma, J Thorac Imaging, 29, 357, 10.1097/RTI.0000000000000116

Ozkan, 2015, CT gray-level texture analysis as a quantitative imaging biomarker of epidermal growth factor receptor mutation status in adenocarcinoma of the lung, AJR Am J Roentgenol, 205, 1016, 10.2214/AJR.14.14147

Liu, 2016, Radiomic features are associated with EGFR mutation status in lung adenocarcinomas, Clin Lung Cancer, 17, 441, 10.1016/j.cllc.2016.02.001

Yoon, 2015, Decoding tumor phenotypes for ALK, ROS1, and RET fusions in lung adenocarcinoma using a radiomics approach, Medicine (Baltimore), 94, e1753, 10.1097/MD.0000000000001753

Wu, 2016, Exploratory study to identify radiomics classifiers for lung cancer histology, Front Oncol, 6, 71, 10.3389/fonc.2016.00071

Liu, 2010, Assessment of therapy responses and prediction of survival in malignant pleural mesothelioma through computer-aided volumetric measurement on computed tomography scans, J Thorac Oncol, 5, 879, 10.1097/JTO.0b013e3181dd0ef1

Zhao, 2010, A pilot study of volume measurement as a method of tumor response evaluation to aid biomarker development, Clin Cancer Res, 16, 4647, 10.1158/1078-0432.CCR-10-0125

Chow, 2014, Semiautomated volumetric measurement on postcontrast MR imaging for analysis of recurrent and residual disease in glioblastoma multiforme, AJNR Am J Neuroradiol, 35, 498, 10.3174/ajnr.A3724

Chang, 2016, Multimodal imaging patterns predict survival in recurrent glioblastoma patients treated with bevacizumab, Neuro-oncol, 18, 1680, 10.1093/neuonc/now086

Ha, 2016, Three-dimensional quantitative validation of breast magnetic resonance imaging background parenchymal enhancement assessments, Curr Probl Diagn Radiol, 45, 297, 10.1067/j.cpradiol.2016.02.003

Ha, 2016, Quantitative 3D breast magnetic resonance imaging fibroglandular tissue analysis and correlation with qualitative assessments: a feasibility study, Quant Imaging Med Surg, 6, 144, 10.21037/qims.2016.03.03

Koshkin, 2016, Assessment of imaging modalities and response metrics in Ewing sarcoma: correlation with survival, J Clin Oncol, 34, 3680, 10.1200/JCO.2016.68.1858

Aerts, 2016, Defining a radiomic response phenotype: a pilot study using targeted therapy in NSCLC, Sci Rep, 6, 33860, 10.1038/srep33860

Coroller, 2016, Radiomic phenotype features predict pathological response in non-small cell lung cancer, Radiother Oncol, 119, 480, 10.1016/j.radonc.2016.04.004

Mattonen, 2016, Detection of local cancer recurrence after stereotactic ablative radiation therapy for lung cancer: physician performance versus radiomic assessment, Int J Radiat Oncol Biol Phys, 94, 1121, 10.1016/j.ijrobp.2015.12.369

Cunliffe, 2015, Lung texture in serial thoracic computed tomography scans: correlation of radiomics-based features with radiation therapy dose and radiation pneumonitis development, Int J Radiat Oncol Biol Phys, 91, 1048, 10.1016/j.ijrobp.2014.11.030

Fried, 2014, Prognostic value and reproducibility of pretreatment CT texture features in stage III non-small cell lung cancer, Int J Radiat Oncol Biol Phys, 90, 834, 10.1016/j.ijrobp.2014.07.020

Coroller, 2015, CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma, Radiother Oncol, 114, 345, 10.1016/j.radonc.2015.02.015

Huang, 2016, Radiomics signature: a potential biomarker for the prediction of disease-free survival in early-stage (I or II) non-small cell lung cancer, Radiology, 281, 947, 10.1148/radiol.2016152234

Emaminejad, 2016, Fusion of quantitative image and genomic biomarkers to improve prognosis assessment of early stage lung cancer patients, IEEE Trans Biomed Eng, 63, 1034, 10.1109/TBME.2015.2477688

Oxnard, 2011, Variability of lung tumor measurements on repeat computed tomography scans taken within 15 minutes, J Clin Oncol, 29, 3114, 10.1200/JCO.2010.33.7071

Zhao, 2009, Evaluating variability in tumor measurements from same-day repeat CT scans of patients with non-small cell lung cancer, Radiology, 252, 263, 10.1148/radiol.2522081593

Eisenhauer, 2009, New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1), Eur J Cancer, 45, 228, 10.1016/j.ejca.2008.10.026

Planchard, 2019, Metastatic non-small cell lung cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up, Ann Oncol, 30, 863, 10.1093/annonc/mdy474

Obuchowski, 2005, ROC analysis, Am J Roentgenol, 184, 364, 10.2214/ajr.184.2.01840364

Lambin, 2017, Radiomics: the bridge between medical imaging and personalized medicine, Nat Rev Clin Oncol, 14, 749, 10.1038/nrclinonc.2017.141

Dercle, 2017, Impact of variability in portal venous phase acquisition timing in tumor density measurement and treatment response assessment: metastatic colorectal cancer as a paradigm, JCO Clin Cancer Inform, 1, 10.1200/CCI.17.00108

Zhao, 2016, Reproducibility of radiomics for deciphering tumor phenotype with imaging, Sci Rep, 6, 23428, 10.1038/srep23428

Huang, 2018, Interobserver variability in tumor contouring affects the use of radiomics to predict mutational status, J Med Imaging, 5, 011005

Tan, 2013, Segmentation of lung lesions on CT scans using watershed, active contours, and Markov random field, Med Phys, 40, 043502, 10.1118/1.4793409

Breiman, 2001, Random forests, Mach learn, 45, 5, 10.1023/A:1010933404324

Wilkerson, 2017, Estimation of tumour regression and growth rates during treatment in patients with advanced prostate cancer: a retrospective analysis, Lancet Oncol, 18, 143, 10.1016/S1470-2045(16)30633-7

Seymour, 2017, iRECIST: guidelines for response criteria for use in trials testing immunotherapeutics, Lancet Oncol, 18, e143, 10.1016/S1470-2045(17)30074-8

Dercle, 2020, Radiomics response signature for identification of metastatic colorectal cancer sensitive to therapies targeting EGFR pathway, JNCI, 10.1093/jnci/djaa017

Li, 2018, CT slice thickness and convolution kernel affect performance of a radiomic model for predicting EGFR status in non-small cell lung cancer: a preliminary study, Sci Rep, 8, 17913, 10.1038/s41598-018-36421-0

Sun, 2018, A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study, Lancet Oncol, 19, 1180, 10.1016/S1470-2045(18)30413-3

Limkin, 2017, Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology, Ann Oncol, 28, 1191, 10.1093/annonc/mdx034

Hanahan, 2011, Hallmarks of cancer: the next generation, Cell, 144, 646, 10.1016/j.cell.2011.02.013

Terranova, 2018, Assessing similarity among individual tumor size lesion dynamics: the CICIL methodology, CPT Pharmacometrics Syst Pharmacol, 7, 228, 10.1002/psp4.12284

Trebeschi, 2017, Radiomic biomarkers for the prediction of immunotherapy outcome in patients with metastatic non-small cell lung cancer, J Clin Oncol, 35, e14520, 10.1200/JCO.2017.35.15_suppl.e14520

Grossmann, 2017, Defining the biological basis of radiomic phenotypes in lung cancer, Elife, 6, 10.7554/eLife.23421

Grove, 2015, Quantitative computed tomographic descriptors associate tumor shape complexity and intratumor heterogeneity with prognosis in lung adenocarcinoma, PLoS One, 10, e0118261, 10.1371/journal.pone.0118261

Al-Kadi, 2008, Texture analysis of aggressive and nonaggressive lung tumor CE CT images, IEEE Trans Biomed Eng, 55, 1822, 10.1109/TBME.2008.919735

Thông tin
Thông tin xuất bản

Clinical Cancer Research

Tập 26 Số 9

2151-2162

Thông tin tác giả