CT-based radiomic analysis of stereotactic body radiation therapy patients with lung cancer

Radiotherapy and Oncology

Volumn 120, Issue 2, 2016, Pages 258-266

  Huynh, Elizabeth ^a   Coroller, Thibaud P ^a   Narayan, Vivek ^a   Agrawal, Vishesh ^a   Hou, Ying ^a   Romano, John ^a   Franco, Idalid ^a   Mak, Raymond H ^a   Aerts, Hugo J W L ^a  

^a BRIGHAM AND WOMEN S HOSPITAL   (United States)

Author keywords

Imaging; Lung cancer; Radiomics; Stereotactic body radiation therapy

Indexed keywords

TUMOR MARKER;

ADULT; AGED; ARTICLE; BIOMICS; CANCER PROGNOSIS; CANCER RADIOTHERAPY; CANCER RECURRENCE; CANCER SPECIFIC SURVIVAL; CANCER SURVIVAL; CLINICAL OUTCOME; COMPUTED TOMOGRAPHY SCANNER; CONE BEAM COMPUTED TOMOGRAPHY; DISTANT METASTASIS; FEMALE; HUMAN; MAJOR CLINICAL STUDY; MALE; MEDIAN SURVIVAL TIME; NON SMALL CELL LUNG CANCER; OVERALL SURVIVAL; PERSONALIZED MEDICINE; PRIORITY JOURNAL; RADIOMIC ANALYSIS; STEREOTACTIC BODY RADIATION THERAPY; CANCER STAGING; CARCINOMA, NON-SMALL-CELL LUNG; DIAGNOSTIC IMAGING; IMAGE PROCESSING; LUNG NEOPLASMS; MIDDLE AGED; PATHOLOGY; PROCEDURES; PROGNOSIS; RADIOSURGERY; TUMOR VOLUME; VERY ELDERLY; X-RAY COMPUTED TOMOGRAPHY;

AGED; AGED, 80 AND OVER; CARCINOMA, NON-SMALL-CELL LUNG; FEMALE; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; LUNG NEOPLASMS; MALE; MIDDLE AGED; NEOPLASM STAGING; PROGNOSIS; RADIOSURGERY; TOMOGRAPHY, X-RAY COMPUTED; TUMOR BURDEN;

EID: 84991101893     PISSN: 01678140     EISSN: 18790887     Source Type: Journal    
DOI: 10.1016/j.radonc.2016.05.024     Document Type: Article

References (49)

1. [1] Lambin, P., van Stiphout, R.G., Starmans, M.H., Rios-Velazquez, E., Nalbantov, G., Aerts, H.J., et al. Predicting outcomes in radiation oncology–multifactorial decision support systems. Nat Rev Clin Oncol 10 (2013), 27–40.
2. [2] Crino, L., Weder, W., van Meerbeeck, J., Felip, E., Group EGW, Early stage and locally advanced (non-metastatic) non-small-cell lung cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol 21 (2010), v103–v115.
3. [3] Potters, L., Kavanagh, B., Galvin, J.M., Hevezi, J.M., Janjan, N.A., Larson, D.A., et al. American Society for Therapeutic Radiology and Oncology (ASTRO) and American College of Radiology (ACR) practice guideline for the performance of stereotactic body radiation therapy. Int J Radiat Oncol Biol Phys 76 (2010), 326–332.
4. [4] Solda, F., Lodge, M., Ashley, S., Whitington, A., Goldstraw, P., Brada, M., Stereotactic radiotherapy (SABR) for the treatment of primary non-small cell lung cancer; systematic review and comparison with a surgical cohort. Radiother Oncol 109 (2013), 1–7.
5. [5] Baumann, P., Nyman, J., Hoyer, M., Wennberg, B., Gagliardi, G., Lax, I., et al. Outcome in a prospective phase II trial of medically inoperable stage I non-small-cell lung cancer patients treated with stereotactic body radiotherapy. J Clin Oncol 27 (2009), 3290–3296.
6. [6] Chi, A., Liao, Z., Nguyen, N.P., Xu, J., Stea, B., Komaki, R., Systemic review of the patterns of failure following stereotactic body radiation therapy in early-stage non-small-cell lung cancer: clinical implications. Radiother Oncol 94 (2010), 1–11.
7. [7] Mak, R.H., Hermann, G., Lewis, J.H., Aerts, H.J., Baldini, E.H., Chen, A.B., et al. Outcomes by tumor histology and KRAS mutation status after lung stereotactic body radiation therapy for early-stage non-small-cell lung cancer. Clin Lung Cancer 16 (2015), 24–32.
8. [8] Fakiris, A.J., McGarry, R.C., Yiannoutsos, C.T., Papiez, L., Williams, M., Henderson, M.A., et al. Stereotactic body radiation therapy for early-stage non-small-cell lung carcinoma: four-year results of a prospective phase II study. Int J Radiat Oncol Biol Phys 75 (2009), 677–682.
9. [9] Timmerman, R., Paulus, R., Galvin, J., Michalski, J., Straube, W., Bradley, J., et al. Stereotactic body radiation therapy for inoperable early stage lung cancer. JAMA 303 (2010), 1070–1076.
10. [10] Onishi, H., Araki, T., Shirato, H., Nagata, Y., Hiraoka, M., Gomi, K., et al. Stereotactic hypofractionated high-dose irradiation for stage I nonsmall cell lung carcinoma: clinical outcomes in 245 subjects in a Japanese multiinstitutional study. Cancer 101 (2004), 1623–1631.
11. [11] Grills, I.S., Mangona, V.S., Welsh, R., Chmielewski, G., McInerney, E., Martin, S., et al. Outcomes after stereotactic lung radiotherapy or wedge resection for stage I non-small-cell lung cancer. J Clin Oncol 28 (2010), 928–935.
12. [12] Network NCC, NCCN guidelines: non-small cell lung cancer (Version 7.2015). 2015.
13. [13] Chen, Y., Guo, W., Lu, Y., Zou, B., Dose-individualized stereotactic body radiotherapy for T1–3N0 non-small cell lung cancer: long-term results and efficacy of adjuvant chemotherapy. Radiother Oncol 88 (2008), 351–358.
14. [14] Lambin, P., Rios-Velazquez, E., Leijenaar, R., Carvalho, S., van Stiphout, R.G., Granton, P., et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48 (2012), 441–446.
15. [15] Kumar, V., Gu, Y., Basu, S., Berglund, A., Eschrich, S.A., Schabath, M.B., et al. Radiomics: the process and the challenges. Magn Reson Imaging 30 (2012), 1234–1248.
16. [16] Leijenaar, R.T., Carvalho, S., Velazquez, E.R., van Elmpt, W.J., Parmar, C., Hoekstra, O.S., et al. Stability of FDG-PET Radiomics features: an integrated analysis of test-retest and inter-observer variability. Acta Oncol 52 (2013), 1391–1397.
17. [17] Parmar, C., Rios Velazquez, E., Leijenaar, R., Jermoumi, M., Carvalho, S., Mak, R.H., et al. Robust radiomics feature quantification using semiautomatic volumetric segmentation. PLoS ONE, 9, 2014, e102107.
18. [18] Rios Velazquez, E., Aerts, H.J., Gu, Y., Goldgof, D.B., De Ruysscher, D., Dekker, A., et al. A semiautomatic CT-based ensemble segmentation of lung tumors: comparison with oncologists’ delineations and with the surgical specimen. Radiother Oncol 105 (2012), 167–173.
19. [19] Aerts, H.J., Velazquez, E.R., Leijenaar, R.T., Parmar, C., Grossmann, P., Carvalho, S., et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun, 5, 2014, 4006.
20. [20] Segal, E., Sirlin, C.B., Ooi, C., Adler, A.S., Gollub, J., Chen, X., et al. Decoding global gene expression programs in liver cancer by noninvasive imaging. Nat Biotechnol 25 (2007), 675–680.
21. [21] Zinn, P.O., Mahajan, B., Sathyan, P., Singh, S.K., Majumder, S., Jolesz, F.A., et al. Radiogenomic mapping of edema/cellular invasion MRI-phenotypes in glioblastoma multiforme. PLoS ONE, 6, 2011, e25451.
22. [22] Coroller, T.P., Grossmann, P., Hou, Y., Rios Velazquez, E., Leijenaar, R.T., Hermann, G., et al. CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. Radiother Oncol 114 (2015), 345–350.
23. [23] Coroller, T.P., Agrawal, V., Narayan, V., Hou, Y., Grossmann, P., Lee, S.W., et al. Radiomic phenotype features predict pathological response in non-small cell lung cancer. Radiother Oncol, 2016.
24. [24] Parmar, C., Grossmann, P., Rietveld, D., Rietbergen, M.M., Lambin, P., Aerts, H.J., Radiomic machine learning classifiers for prognostic biomarkers of head & neck cancer. Front. Oncol., 2015, 5.
25. [25] Cunliffe, A., Armato, S.G. 3rd, Castillo, R., Pham, N., Guerrero, T., Al-Hallaq, H.A., 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 (2015), 1048–1056.
26. [26] Parmar, C., Leijenaar, R.T., Grossmann, P., Rios Velazquez, E., Bussink, J., Rietveld, D., et al. Radiomic feature clusters and prognostic signatures specific for lung and head & neck cancer. Sci Rep, 5, 2015, 11044.
27. [27] Kim, H., Park, C.M., Goo, J.M., Wildberger, J.E., Kauczor, H.U., Quantitative computed tomography imaging biomarkers in the diagnosis and management of lung cancer. Invest Radiol 50 (2015), 571–583.
28. [28] De Ruysscher, D., Sharifi, H., Defraene, G., Kerns, S.L., Christiaens, M., De Ruyck, K., et al. Quantification of radiation-induced lung damage with CT scans: the possible benefit for radiogenomics. Acta Oncol 52 (2013), 1405–1410.
29. [29] Palma, D.A., van Sornsen de Koste, J., Verbakel, W.F., Vincent, A., Senan, S., Lung density changes after stereotactic radiotherapy: a quantitative analysis in 50 patients. Int J Radiat Oncol Biol Phys 81 (2011), 974–978.
30. [30] Kyas, I., Hof, H., Debus, J., Schlegel, W., Karger, C.P., Prediction of radiation-induced changes in the lung after stereotactic body radiation therapy of non-small-cell lung cancer. Int J Radiat Oncol Biol Phys 67 (2007), 768–774.
31. [31] Mattonen, S.A., Palma, D.A., Haasbeek, C.J., Senan, S., Ward, A.D., Early prediction of tumor recurrence based on CT texture changes after stereotactic ablative radiotherapy (SABR) for lung cancer. Med Phys, 41, 2014, 033502.
32. [32] Defraene, G., van Elmpt, W., Crijns, W., Slagmolen, P., De Ruysscher, D., CT characteristics allow identification of patient-specific susceptibility for radiation-induced lung damage. Radiother Oncol, 2015.
33. [33] Pieper, S., Kikinis, R., 3D SLICER. Proceedings of the 1st IEEE international symposium on biomedical imaging: from nano to macro, 2004, 632–635.
34. [34] Zhao, B., James, L.P., Moskowitz, C.S., Guo, P., Ginsberg, M.S., Lefkowitz, R.A., et al. Evaluating variability in tumor measurements from same-day repeat CT scans of patients with non-small cell lung cancer. Radiology 252 (2009), 263–272.
35. [35] Gamer, M., Lemon, J., Singh, I.F.P, irr: various coefficients of interrater reliability and agreement. R package version 0.84. 2012.
36. [36] Husson, F., Josse, J., Le, S., Mazet, J., FactoMineR: multivariate exploratory data analysis and data mining. R package version 1.31.3. 2015.
37. [37] Team, R.C., R: a language and environment for statistical computing. 2015, R Foundation for Statistical Computing, Vienna, Austria.
38. [38] Schroder, M.S., Culhane, A.C., Quackenbush, J., Haibe-Kains, B., Survcomp: an R/bioconductor package for performance assessment and comparison of survival models. Bioinformatics 27 (2011), 3206–3208.
39. [39] Haibe-Kains, B., Desmedt, C., Sotiriou, C., Bontempi, G., A comparative study of survival models for breast cancer prognostication based on microarray data: does a single gene beat them all?. Bioinformatics 24 (2008), 2200–2208.
40. [40] Gentleman, R.C., Carey, V.J., Bates, D.M., Bolstad, B., Dettling, M., Dudoit, S., et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol, 5, 2004, R80.
41. [41] Benjamini, Y.H., Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Roy Stat Soc B 57 (1995), 289–300.
42. [42] Storey, J.D., The positive false discovery rate: a Bayesian interpretation and the q-value. Ann Stat, 2003, 2013–2035.
43. [43] Therneau, T., A package for survival analysis in S. Version 2.38. 2015.
44. [44] Steck, H., Krishnapuram, B., Dehing-oberije, C., Lambin, P., Raykar, V.C., On ranking in survival analysis: bounds on the concordance index. Adv Neural Inf Process Syst, 2008, 1209–1216.
45. [45] Harrell, F.E., Lee, K.L., Mark, D.B., Tutorial in biostatistics multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med 15 (1996), 361–387.
46. [46] Muren, L.P., Thwaites, D.I., The on-going quest for treatment precision and conformality in radiotherapy. Radiother Oncol 109 (2013), 337–341.
47. [47] Visbal, A.L., Leighl, N.B., Feld, R., Shepherd, F.A., Adjuvant chemotherapy for early-stage non-small cell lung cancer. Chest 128 (2005), 2933–2943.
48. [48] Lo, A., Chernoff, H., Zheng, T., Lo, S.H., Why significant variables aren't automatically good predictors. Proc Natl Acad Sci USA 112 (2015), 13892–13897.
49. [49] Pan, H., Simpson, D.R., Mell, L.K., Mundt, A.J., Lawson, J.D., A survey of stereotactic body radiotherapy use in the United States. Cancer 117 (2011), 4566–4572.