Radiomics features of the primary tumor fail to improve prediction of overall survival in large cohorts of CT- and PET-imaged head and neck cancer patients
Autoři:
Rachel B. Ger aff001; Shouhao Zhou aff002; Baher Elgohari aff004; Hesham Elhalawani aff004; Dennis M. Mackin aff001; Joseph G. Meier aff002; Callistus M. Nguyen aff001; Brian M. Anderson aff002; Casey Gay aff001; Jing Ning aff003; Clifton D. Fuller aff002; Heng Li aff001; Rebecca M. Howell aff001; Rick R. Layman aff002; Osama Mawlawi aff002; R. Jason Stafford aff002; Hugo Aerts aff006; Laurence E. Court aff001
Působiště autorů:
Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
aff001; MD Anderson Cancer Center UTHealth Science Center at Houston Graduate School of Biomedical Sciences, Houston, Texas, United States of America
aff002; Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
aff003; Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
aff004; Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
aff005; Department of Radiation Oncology, Dana Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
aff006
Vyšlo v časopise:
PLoS ONE 14(9)
Kategorie:
Research Article
prolekare.web.journal.doi_sk:
https://doi.org/10.1371/journal.pone.0222509
Souhrn
Radiomics studies require many patients in order to power them, thus patients are often combined from different institutions and using different imaging protocols. Various studies have shown that imaging protocols affect radiomics feature values. We examined whether using data from cohorts with controlled imaging protocols improved patient outcome models. We retrospectively reviewed 726 CT and 686 PET images from head and neck cancer patients, who were divided into training or independent testing cohorts. For each patient, radiomics features with different preprocessing were calculated and two clinical variables—HPV status and tumor volume—were also included. A Cox proportional hazards model was built on the training data by using bootstrapped Lasso regression to predict overall survival. The effect of controlled imaging protocols on model performance was evaluated by subsetting the original training and independent testing cohorts to include only patients whose images were obtained using the same imaging protocol and vendor. Tumor volume, HPV status, and two radiomics covariates were selected for the CT model, resulting in an AUC of 0.72. However, volume alone produced a higher AUC, whereas adding radiomics features reduced the AUC. HPV status and one radiomics feature were selected as covariates for the PET model, resulting in an AUC of 0.59, but neither covariate was significantly associated with survival. Limiting the training and independent testing to patients with the same imaging protocol reduced the AUC for CT patients to 0.55, and no covariates were selected for PET patients. Radiomics features were not consistently associated with survival in CT or PET images of head and neck patients, even within patients with the same imaging protocol.
Klíčová slova:
Biology and life sciences – Organisms – Engineering and technology – Research and analysis methods – Neuroscience – Computer and information sciences – Medicine and health sciences – Microbiology – Medical microbiology – Microbial pathogens – Pathology and laboratory medicine – Pathogens – Diagnostic medicine – Viral pathogens – Viruses – Oncology – Cancer treatment – Cancers and neoplasms – DNA viruses – Imaging techniques – Papillomaviruses – Human papillomavirus – Neuroimaging – Diagnostic radiology – Radiology and imaging – Tomography – Computed axial tomography – Signal processing – Carcinomas – Software engineering – Preprocessing – Noise reduction – Positron emission tomography – Head and neck cancers – Head and neck tumors – Head and neck squamous cell carcinoma – Squamous cell carcinomas
Zdroje
1. Fave X, Zhang L, Yang J, Mackin D, Balter P, Gomez D, et al. Delta-radiomics features for the prediction of patient outcomes in non-small cell lung cancer. Sci Rep. 2017;7(1):588.
2. Fried DV, Mawlawi O, Zhang L, Fave X, Zhou S, Ibbott G, et al. Stage III Non-Small Cell Lung Cancer: Prognostic Value of FDG PET Quantitative Imaging Features Combined with Clinical Prognostic Factors. Radiology. 2016;278(1):214–22. doi: 10.1148/radiol.2015142920 26176655
3. Fried DV, Tucker SL, Zhou S, Liao Z, Mawlawi O, Ibbott G, et al. Prognostic value and reproducibility of pretreatment CT texture features in stage III non-small cell lung cancer. International journal of radiation oncology, biology, physics. 2014;90(4):834–42. doi: 10.1016/j.ijrobp.2014.07.020 25220716
4. Thawani R, McLane M, Beig N, Ghose S, Prasanna P, Velcheti V, et al. Radiomics and radiogenomics in lung cancer: A review for the clinician. Lung Cancer. 2017.
5. Cook GJ, Azad G, Owczarczyk K, Siddique M, Goh V. Challenges and Promises of PET Radiomics. International Journal of Radiation Oncology* Biology* Physics. 2018.
6. Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014;5:4006. doi: 10.1038/ncomms5006 24892406
7. Parmar C, Grossmann P, Rietveld D, Rietbergen MM, Lambin P, Aerts HJ. Radiomic machine-learning classifiers for prognostic biomarkers of head and neck cancer. Frontiers in oncology. 2015;5:272. doi: 10.3389/fonc.2015.00272 26697407
8. Parmar C, Leijenaar RT, 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. 2015;5:11044. doi: 10.1038/srep11044 26251068
9. Bogowicz M, Riesterer O, Ikenberg K, Stieb S, Moch H, Studer G, et al. Computed Tomography Radiomics Predicts HPV Status and Local Tumor Control After Definitive Radiochemotherapy in Head and Neck Squamous Cell Carcinoma. Int J Radiat Oncol Biol Phys. 2017. doi: 10.1016/j.ijrobp.2017.06.002 28807534.
10. Bogowicz M, Riesterer O, Stark LS, Studer G, Unkelbach J, Guckenberger M, et al. Comparison of PET and CT radiomics for prediction of local tumor control in head and neck squamous cell carcinoma. Acta Oncol. 2017:1–6. doi: 10.1080/0284186X.2017.1346382 28820287.
11. Ou D, Blanchard P, Rosellini S, Levy A, Nguyen F, Leijenaar RT, et al. Predictive and prognostic value of CT based radiomics signature in locally advanced head and neck cancers patients treated with concurrent chemoradiotherapy or bioradiotherapy and its added value to Human Papillomavirus status. Oral oncology. 2017;71:150–5. doi: 10.1016/j.oraloncology.2017.06.015 28688683
12. Vallieres M, Kay-Rivest E, Perrin LJ, Liem X, Furstoss C, Aerts H, et al. Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer. Sci Rep. 2017;7(1):10117. doi: 10.1038/s41598-017-10371-5 28860628
13. El Naqa I, Grigsby P, Apte A, Kidd E, Donnelly E, Khullar D, et al. Exploring feature-based approaches in PET images for predicting cancer treatment outcomes. Pattern recognition. 2009;42(6):1162–71. doi: 10.1016/j.patcog.2008.08.011 20161266
14. Folkert MR, Setton J, Apte AP, Grkovski M, Young RJ, Schöder H, et al. Predictive modeling of outcomes following definitive chemoradiotherapy for oropharyngeal cancer based on FDG-PET image characteristics. Physics in Medicine & Biology. 2017;62(13):5327.
15. Mackin D, Fave X, Zhang L, Fried D, Yang J, Taylor B, et al. Measuring Computed Tomography Scanner Variability of Radiomics Features. Investigative radiology. 2015;50(11):757–65. doi: 10.1097/RLI.0000000000000180 26115366
16. Mackin D, Fave X, Zhang L, Yang J, Jones AK, Ng CS, et al. Harmonizing the pixel size in retrospective computed tomography radiomics studies. PLoS One. 2017;12(9):e0178524. doi: 10.1371/journal.pone.0178524 28934225.
17. Mackin D, Ger R, Dodge C, Fave X, Chi P-C, Zhang L, et al. Effect of tube current on computed tomography radiomic features. Scientific reports. 2018;8(1):2354. doi: 10.1038/s41598-018-20713-6 29403060
18. Shafiq‐ul‐Hassan M, Zhang GG, Latifi K, Ullah G, Hunt DC, Balagurunathan Y, et al. Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels. Medical physics. 2017;44(3):1050–62. doi: 10.1002/mp.12123 28112418
19. Ger RB, Zhou S, Chi P-CM, Lee HJ, Layman RR, Jones AK, et al. Comprehensive Investigation on Controlling for CT Imaging Variabilities in Radiomics Studies. Scientific reports. 2018;8(1):13047. doi: 10.1038/s41598-018-31509-z 30158540
20. Shiri I, Rahmim A, Ghaffarian P, Geramifar P, Abdollahi H, Bitarafan-Rajabi A. The impact of image reconstruction settings on 18F-FDG PET radiomic features: multi-scanner phantom and patient studies. European radiology. 2017;27(11):4498–509. doi: 10.1007/s00330-017-4859-z 28567548
21. Orlhac F, Boughdad S, Philippe C, Stalla-Bourdillon H, Nioche C, Champion L, et al. A postreconstruction harmonization method for multicenter radiomic studies in PET. Journal of Nuclear Medicine. 2018;59(8):1321–8. doi: 10.2967/jnumed.117.199935 29301932
22. Galavis PE, Hollensen C, Jallow N, Paliwal B, Jeraj R. Variability of textural features in FDG PET images due to different acquisition modes and reconstruction parameters. Acta oncologica. 2010;49(7):1012–6. doi: 10.3109/0284186X.2010.498437 20831489
23. van Velden FH, Kramer GM, Frings V, Nissen IA, Mulder ER, de Langen AJ, et al. Repeatability of radiomic features in non-small-cell lung cancer [18F] FDG-PET/CT studies: impact of reconstruction and delineation. Molecular imaging and biology. 2016;18(5):788–95. doi: 10.1007/s11307-016-0940-2 26920355
24. Yan J, Chu-Shern JL, Loi HY, Khor LK, Sinha AK, Quek ST, et al. Impact of image reconstruction settings on texture features in 18F-FDG PET. Journal of nuclear medicine. 2015;56(11):1667–73. doi: 10.2967/jnumed.115.156927 26229145
25. Ger RB, Craft DF, Mackin DS, Zhou S, Layman RR, Jones AK, et al. Practical guidelines for handling head and neck computed tomography artifacts for quantitative image analysis. Computerized Medical Imaging and Graphics. 2018;69:134–9. doi: 10.1016/j.compmedimag.2018.09.002 30268005
26. Fave X, Cook M, Frederick A, Zhang L, Yang J, Fried D, et al. Preliminary investigation into sources of uncertainty in quantitative imaging features. Computerized Medical Imaging and Graphics. 2015;44:54–61. doi: 10.1016/j.compmedimag.2015.04.006 26004695
27. Zhao B, Tan Y, Tsai WY, Qi J, Xie C, Lu L, et al. Reproducibility of radiomics for deciphering tumor phenotype with imaging. Scientific reports. 2016;6:23428. doi: 10.1038/srep23428 27009765
28. Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, et al. The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. Journal of digital imaging. 2013;26(6):1045–57. doi: 10.1007/s10278-013-9622-7 23884657
29. Vallieres M, Kay-Rivest E, Perrin L, Liem X, Furstoss C, Khaouam N, et al. Data from Head-Neck-PET-CT. The Cancer Imaging Archive; 2017.
30. Ger RB, Cardenas CE, Anderson BM, Yang J, Mackin DS, Zhang L. Guidelines and Experience Using Imaging Biomarker Explorer (IBEX) for Radiomics. Journal of visualized experiments: JoVE. 2018;(131).
31. Zhang L, Fried DV, Fave XJ, Hunter LA, Yang J, Court LE. IBEX: an open infrastructure software platform to facilitate collaborative work in radiomics. Medical physics. 2015;42(3):1341–53. doi: 10.1118/1.4908210 25735289.
32. Fave X, Zhang L, Yang J, Mackin D, Balter P, Gomez D, et al. Impact of image preprocessing on the volume dependence and prognostic potential of radiomics features in non-small cell lung cancer. Translational Cancer Research. 2016;5(4):349–63.
33. Leijenaar RT, Nalbantov G, Carvalho S, Van Elmpt WJ, Troost EG, Boellaard R, et al. The effect of SUV discretization in quantitative FDG-PET Radiomics: the need for standardized methodology in tumor texture analysis. Scientific reports. 2015;5:11075. doi: 10.1038/srep11075 26242464
34. Hatt M, Majdoub M, Vallieres M, Tixier F, Le Rest CC, Groheux D, et al. 18F-FDG PET uptake characterization through texture analysis: investigating the complementary nature of heterogeneity and functional tumor volume in a multi-cancer site patient cohort. Journal of nuclear medicine: official publication, Society of Nuclear Medicine. 2015;56(1):38–44. doi: 10.2967/jnumed.114.144055 25500829.
35. Lydiatt WM, Patel SG, O’Sullivan B, Brandwein MS, Ridge JA, Migliacci JC, et al. Head and Neck cancers-major changes in the American Joint Committee on cancer eighth edition cancer staging manual. CA: a cancer journal for clinicians. 2017;67(2):122–37. doi: 10.3322/caac.21389 28128848.
36. Feliciani G, Fioroni F, Grassi E, Bertolini M, Rosca A, Timon G, et al. Radiomic Profiling of Head and Neck Cancer: 18F-FDG PET Texture Analysis as Predictor of Patient Survival. Contrast media & molecular imaging. 2018;2018.
37. Kuno H, Qureshi M, Chapman M, Li B, Andreu-Arasa V, Onoue K, et al. CT texture analysis potentially predicts local failure in head and neck squamous cell carcinoma treated with chemoradiotherapy. American Journal of Neuroradiology. 2017;38(12):2334–40. doi: 10.3174/ajnr.A5407 29025727
38. Foy JJ, Mitta P, Nowosatka LR, Mendel KR, Li H, Giger ML, et al. Variations in algorithm implementation among quantitative texture analysis software packages. Medical Imaging 2018: Computer-Aided Diagnosis; 2018: International Society for Optics and Photonics.
39. Kann BH, Aneja S, Loganadane GV, Kelly JR, Smith SM, Decker RH, et al. Pretreatment Identification of Head and Neck Cancer Nodal Metastasis and Extranodal Extension Using Deep Learning Neural Networks. Scientific reports. 2018;8(1):14036. doi: 10.1038/s41598-018-32441-y 30232350
40. Vial A, Stirling D, Field M, Ros M, Ritz C, Carolan M, et al. The role of deep learning and radiomic feature extraction in cancer-specific predictive modelling: a review. Translational Cancer Research. 2018;7(3):803–16.
41. Wu J, Gensheimer MF, Zhang N, Han F, Liang R, Qian Y, et al. Integrating Tumor and Nodal imaging Characteristics at Baseline and Mid-Treatment Computed Tomography Scans to Predict Distant Metastasis in Oropharyngeal Cancer Treated with Concurrent Chemoradiotherapy. International Journal of Radiation Oncology, Biology, Physics. 2019; 104(4):942–952]. doi: 10.1016/j.ijrobp.2019.03.036 30940529
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