A Quantitative CT Imaging Signature Predicts Survival and Complements Established Prognosticators in Stage I Non-Small Cell Lung Cancer

International Journal of Radiation Oncology Biology Physics

Volumn 102, Issue 4, 2018, Pages 1098-1106

  Lee, Juheon ^a   Li, Bailiang ^a   Cui, Yi ^a   Sun, Xiaoli ^c   Wu, Jia ^a   Zhu, Hui ^d   Yu, Jinming ^d   Gensheimer, Michael F ^a   Loo, Billy W ^a,b   Diehn, Maximilian ^a,b   Li, Ruijiang ^a,b  

^a STANFORD UNIVERSITY SCHOOL OF MEDICINE   (United States)

^b STANFORD CANCER INSTITUTE   (United States)

^c ZHEJIANG UNIVERSITY   (China)

^d SHANDONG CANCER HOSPITAL AND INSTITUTE   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ACTIVATION ANALYSIS; BIOLOGICAL ORGANS; BIOMARKERS; DISEASES; GENE EXPRESSION; STATISTICAL TESTS; TUMORS;

CELL-CYCLE PROGRESSION; COMPOSITE SIGNATURES; FALSE DISCOVERY RATE; FUNCTIONAL ENRICHMENT ANALYSIS; GENE EXPRESSION DATA; LYMPHOCYTE ACTIVATION; METHODS AND MATERIALS; NON SMALL CELL LUNG CANCER;

COMPUTERIZED TOMOGRAPHY;

TUMOR MARKER;

ACCURACY; ARTICLE; CANCER PROGNOSIS; CANCER STAGING; CANCER SURVIVAL; CELL CYCLE PROGRESSION; CHEMOTAXIS; CLINICAL ARTICLE; COHORT ANALYSIS; COMPUTER ASSISTED TOMOGRAPHY; DISTANT METASTASIS FREE SURVIVAL; FEMALE; GENE EXPRESSION; GENOMICS; HUMAN; IMMUNE RESPONSE; LYMPHOCYTE ACTIVATION; MALE; NON SMALL CELL LUNG CANCER; OVERALL SURVIVAL; PLEURA; PRIORITY JOURNAL; QUANTITATIVE ANALYSIS; RETROSPECTIVE STUDY; DIAGNOSTIC IMAGING; GENETICS; KAPLAN MEIER METHOD; LUNG TUMOR; PATHOLOGY; PROGNOSIS; X-RAY COMPUTED TOMOGRAPHY;

CARCINOMA, NON-SMALL-CELL LUNG; FEMALE; GENOMICS; HUMANS; KAPLAN-MEIER ESTIMATE; LUNG NEOPLASMS; MALE; NEOPLASM STAGING; PROGNOSIS; RETROSPECTIVE STUDIES; TOMOGRAPHY, X-RAY COMPUTED;

EID: 85044523511     PISSN: 03603016     EISSN: 1879355X     Source Type: Journal    
DOI: 10.1016/j.ijrobp.2018.01.006     Document Type: Article

References (40)

1. Siegel, R.L., Miller, K.D., Jemal, A., Cancer statistics, 2016. CA Cancer J Clin 66 (2016), 7–30.
2. Bradley, J.D., El Naqa, I., Drzymala, R.E., et al. Stereotactic body radiation therapy for early-stage non-small-cell lung cancer: The pattern of failure is distant. Int J Radiat Oncol Biol Phys 77 (2010), 1146–1150.
3. Senthi, S., Lagerwaard, F.J., Haasbeek, C.J., et al. Patterns of disease recurrence after stereotactic ablative radiotherapy for early stage non-small-cell lung cancer: A retrospective analysis. Lancet Oncol 13 (2012), 802–809.
4. Neal, J.W., Gainor, J.F., Shaw, A.T., Developing biomarker-specific end points in lung cancer clinical trials. Nat Rev Clin Oncol 12 (2015), 135–146.
5. Rami-Porta, R., Bolejack, V., Crowley, J., et al. The IASLC Lung Cancer Staging Project: Proposals for the revisions of the T descriptors in the forthcoming eighth edition of the TNM classification for lung cancer. J Thorac Oncol 10 (2015), 990–1003.
6. Ganeshan, B., Panayiotou, E., Burnand, K., et al. Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: A potential marker of survival. Eur Radiol 22 (2012), 796–802.
7. Gevaert, O., Xu, J., Hoang, C.D., et al. Non-small cell lung cancer: Identifying prognostic imaging biomarkers by leveraging public gene expression microarray data—Methods and preliminary results. Radiology 264 (2012), 387–396.
8. Win, T., Miles, K.A., Janes, S.M., et al. Tumor heterogeneity and permeability as measured on the CT component of PET/CT predict survival in patients with non–small cell lung cancer. Clin Cancer Res 19 (2013), 3591–3599.
9. Aerts, H.J., Velazquez, E.R., Leijenaar, R.T., et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun, 5, 2014, 4006.
10. Wang, H., Schabath, M.B., Liu, Y., et al. Association between computed tomographic features and Kirsten rat sarcoma viral oncogene mutations in patients with stage I lung adenocarcinoma and their prognostic value. Clin Lung Cancer 17 (2016), 271–278.
11. Coroller, T.P., Grossmann, P., Hou, Y., et al. CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. Radiother Oncol 114 (2015), 345–350.
12. Huynh, E., Coroller, T.P., Narayan, V., et al. CT-based radiomic analysis of stereotactic body radiation therapy patients with lung cancer. Radiother Oncol 120 (2016), 258–266.
13. Mattonen, S.A., Palma, D.A., Johnson, C., et al. 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 (2016), 1121–1128.
14. Wu, J., Aguilera, T., Shultz, D., et al. Early-stage non–small cell lung cancer: Quantitative imaging characteristics of 18F fluorodeoxyglucose PET/CT allow prediction of distant metastasis. Radiology 281 (2016), 270–278.
15. Wu, J., Gensheimer, M.F., Dong, X., et al. Robust intra-tumor partitioning to identify high-risk subregions in lung cancer: A pilot study. Int J Radiat Oncol Biol Phys 95 (2016), 1504–1512.
16. Tibshirani, R., The lasso method for variable selection in the Cox model. Stat Med 16 (1997), 385–395.
17. Gautier, L., Cope, L., Bolstad, B.M., et al. affy—Analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 20 (2004), 307–315.
18. Tibshirani, R., Hastie, T., Narasimhan, B., et al. Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc Natl Acad Sci U S A 99 (2002), 6567–6572.
19. Wistuba, I.I., Behrens, C., Lombardi, F., et al. Validation of a proliferation-based expression signature as prognostic marker in early stage lung adenocarcinoma. Clin Cancer Res 19 (2013), 6261–6271.
20. Bueno, R., Hughes, E., Wagner, S., et al. Validation of a molecular and pathological model for five-year mortality risk in patients with early stage lung adenocarcinoma. J Thorac Oncol 10 (2015), 67–73.
21. Pencina, M.J., D'Agostino, R.B., Steyerberg, E.W., Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Stat Med 30 (2011), 11–21.
22. Harrell, F.E., Lee, K.L., Califf, R.M., et al. Regression modelling strategies for improved prognostic prediction. Stat Med 3 (1984), 143–152.
23. Kang, L., Chen, W., Petrick, N.A., et al. Comparing two correlated C indices with right-censored survival outcome: A one-shot nonparametric approach. Stat Med 34 (2015), 685–703.
24. National Lung Screening Trial Research Team, Aberle, D.R., Adams, A.M., Berg, C.D., et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med 365 (2011), 395–409.
25. Aberle, D.R., Abtin, F., Brown, K., Computed tomography screening for lung cancer: Has it finally arrived? Implications of the national lung screening trial. J Clin Oncol 31 (2013), 1002–1008.
26. Gillies, R.J., Kinahan, P.E., Hricak, H., Radiomics: Images are more than pictures, they are data. Radiology 278 (2016), 563–577.
27. O'Connor, J.P., Rose, C.J., Waterton, J.C., et al. Imaging intratumor heterogeneity: Role in therapy response, resistance, and clinical outcome. Clin Cancer Res 21 (2015), 249–257.
28. Wang, H., Schabath, M.B., Liu, Y., et al. Semiquantitative computed tomography characteristics for lung adenocarcinoma and their association with lung cancer survival. Clin Lung Cancer 16 (2015), e141–e163.
29. Grossmann, P., Stringfield, O., El-Hachem, N., et al. Defining the biological basis of radiomic phenotypes in lung cancer. Elife, 6, 2017, e23421.
30. Li, B., Cui, Y., Diehn, M., et al. Development and validation of an individualized immune prognostic signature in early-stage nonsquamous non–small cell lung cancer. JAMA Oncol 3 (2017), 1529–1537.
31. Hynes, R.O., The extracellular matrix: Not just pretty fibrils. Science 326 (2009), 1216–1219.
32. Paz, H., Pathak, N., Yang, J., Invading one step at a time: The role of invadopodia in tumor metastasis. Oncogene 33 (2014), 4193–4202.
33. Donnem, T., Hald, S.M., Paulsen, E.E., et al. Stromal CD8+ T-cell density—A promising supplement to TNM staging in non–small cell lung cancer. Clin Cancer Res 21 (2015), 2635–2643.
34. Gentles, A.J., Bratman, S.V., Lee, L.J., et al. Integrating tumor and stromal gene expression signatures with clinical indices for survival stratification of early-stage non–small cell lung cancer. J Natl Cancer Inst, 107, 2015, djv211.
35. Hsu, K.H., Chen, K.C., Yang, T.Y., et al. Epidermal growth factor receptor mutation status in stage I lung adenocarcinoma with different image patterns. J Thorac Oncol 6 (2011), 1066–1072.
36. Lee, H.J., Kim, Y.T., Kang, C.H., et al. Epidermal growth factor receptor mutation in lung adenocarcinomas: Relationship with CT characteristics and histologic subtypes. Radiology 268 (2013), 254–264.
37. Liu, Y., Kim, J., Qu, F., et al. CT features associated with epidermal growth factor receptor mutation status in patients with lung adenocarcinoma. Radiology 280 (2016), 271–280.
38. Zhao, B., Tan, Y., Tsai, W.Y., et al. Reproducibility of radiomics for deciphering tumor phenotype with imaging. Sci Rep, 6, 2016, 23428.
39. Jochems, A., Deist, T.M., El Naqa, I., et al. Developing and validating a survival prediction model for NSCLC patients through distributed learning across 3 countries. Int J Radiat Oncol Biol Phys 99 (2017), 344–352.
40. de Bruin, E.C., McGranahan, N., Mitter, R., et al. Spatial and temporal diversity in genomic instability processes defines lung cancer evolution. Science 346 (2014), 251–256.