Photon-counting detector CT-based virtual monoenergetic reconstructions: repeatability and reproducibility of radiomics features of an organic phantom and human myocardium
Tóm tắt
Photon-counting detector computed tomography (PCD-CT) may influence imaging characteristics for various clinical conditions due to higher signal and contrast-to-noise ratio in virtual monoenergetic images (VMI). Radiomics analysis relies on quantification of image characteristics. We evaluated the impact of different VMI reconstructions on radiomic features in in vitro and in vivo PCD-CT datasets. An organic phantom consisting of twelve samples (four oranges, four onions, and four apples) was scanned five times. Twenty-three patients who had undergone coronary computed tomography angiography on a first generation PCD-CT system with the same image acquisitions were analyzed. VMIs were reconstructed at 6 keV levels (40, 55, 70, 90, 120, and 190 keV). The phantoms and the patients’ left ventricular myocardium (LVM) were segmented for all reconstructions. Ninety-three original radiomic features were extracted. Repeatability and reproducibility were evaluated through intraclass correlations coefficient (ICC) and post hoc paired samples ANOVA t test. There was excellent repeatability for radiomic features in phantom scans (all ICC = 1.00). Among all VMIs, 36/93 radiomic features (38.7%) in apples, 28/93 (30.1%) in oranges, and 33/93 (35.5%) in onions were not significantly different. For LVM, the percentage of stable features was high between VMIs ≥ 90 keV (90 versus 120 keV, 77.4%; 90 versus 190 keV, 83.9%; 120 versus 190 keV, 89.3%), while comparison to lower VMI levels led to fewer reproducible features (40 versus 55 keV, 8.6%). VMI levels influence the stability of radiomic features in an organic phantom and patients’ LVM; stability decreases considerably below 90 keV. Spectral reconstructions significantly influence radiomic features in vitro and in vivo, necessitating standardization and careful attention to these reconstruction parameters before clinical implementation. • Radiomic features have an excellent repeatability within the same PCD-CT acquisition and reconstruction. • Differences in VMI lead to decreased reproducibility for radiomic features. • VMI ≥ 90 keV increased the reproducibility of the radiomic features.
Tài liệu tham khảo
Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–77. https://doi.org/10.1148/radiol.2015151169
Mayerhoefer ME, Materka A, Langs G et al (2020) Introduction to radiomics. J Nucl Med 61:488–95. https://doi.org/10.2967/jnumed.118.222893
Huang YQ, Liang CH, He L et al (2016) Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer. J Clin Oncol 34:2157–64. https://doi.org/10.1200/JCO.2015.65.9128
Aerts HJ, Velazquez ER, Leijenaar RT et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006. https://doi.org/10.1038/ncomms5006
Liu Z, Wang S, Dong D et al (2019) The applications of radiomics in precision diagnosis and treatment of oncology: opportunities and challenges. Theranostics 9:1303–22. https://doi.org/10.7150/thno.30309
Woznicki P, Westhoff N, Huber T et al. (2020) Multiparametric MRI for prostate cancer characterization: combined use of radiomics model with PI-RADS and clinical parameters. Cancers (Basel) 12. https://doi.org/10.3390/cancers12071767
Enke JS, Moltz JH, D'Anastasi M et al. (2022) Radiomics features of the spleen as surrogates for CT-based lymphoma diagnosis and subtype differentiation. Cancers (Basel) 14. https://doi.org/10.3390/cancers14030713
Tian X, Sun C, Liu Z et al (2020) Prediction of response to preoperative neoadjuvant chemotherapy in locally advanced cervical cancer using multicenter CT-based radiomic analysis. Front Oncol 10:77. https://doi.org/10.3389/fonc.2020.00077
Vaidya P, Bera K, Gupta A et al (2020) CT derived radiomic score for predicting the added benefit of adjuvant chemotherapy following surgery in stage I, II resectable non-small cell lung cancer: a retrospective multi-cohort study for outcome prediction. Lancet Digit Health 2:e116–e28. https://doi.org/10.1016/s2589-7500(20)30002-9
Li H, Zhang R, Wang S et al (2020) CT-based radiomic signature as a prognostic factor in stage IV ALK-positive non-small-cell lung cancer treated with TKI crizotinib: a proof-of-concept study. Front Oncol 10:57. https://doi.org/10.3389/fonc.2020.00057
Forghani R, De Man B, Gupta R (2017) Dual-energy computed tomography: physical principles, approaches to scanning, usage, and implementation: part 2. Neuroimaging Clin N Am 27:385–400. https://doi.org/10.1016/j.nic.2017.03.003
Euler A, Laqua FC, Cester D et al. (2021) Virtual monoenergetic images of dual-energy CT-impact on repeatability, reproducibility, and classification in radiomics. Cancers (Basel) 13. https://doi.org/10.3390/cancers13184710
Forghani R, Chatterjee A, Reinhold C et al (2019) Head and neck squamous cell carcinoma: prediction of cervical lymph node metastasis by dual-energy CT texture analysis with machine learning. Eur Radiol 29:6172–81. https://doi.org/10.1007/s00330-019-06159-y
Seidler M, Forghani B, Reinhold C et al (2019) Dual-energy CT texture analysis with machine learning for the evaluation and characterization of cervical lymphadenopathy. Comput Struct Biotechnol J 17:1009–15. https://doi.org/10.1016/j.csbj.2019.07.004
Al Ajmi E, Forghani B, Reinhold C et al (2018) Spectral multi-energy CT texture analysis with machine learning for tissue classification: an investigation using classification of benign parotid tumours as a testing paradigm. Eur Radiol 28:2604–11. https://doi.org/10.1007/s00330-017-5214-0
An C, Li D, Li S et al (2022) Deep learning radiomics of dual-energy computed tomography for predicting lymph node metastases of pancreatic ductal adenocarcinoma. Eur J Nucl Med Mol Imaging 49:1187–99. https://doi.org/10.1007/s00259-021-05573-z
Groen JM, Greuter MJ, Vliegenthart R et al (2008) Calcium scoring using 64-slice MDCT, dual source CT and EBT: a comparative phantom study. Int J Cardiovasc Imaging 24:547–56. https://doi.org/10.1007/s10554-007-9282-0
Ayx I, Tharmaseelan H, Hertel A et al. (2022) Comparison study of myocardial radiomics feature properties on energy-integrating and photon-counting detector CT. Diagnostics (Basel) 12. https://doi.org/10.3390/diagnostics12051294
Lohmann P, Bousabarah K, Hoevels M et al (2020) Radiomics in radiation oncology-basics, methods, and limitations. Strahlenther Onkol 196:848–55. https://doi.org/10.1007/s00066-020-01663-3
van Griethuysen JJM, Fedorov A, Parmar C et al (2017) Computational radiomics system to decode the radiographic phenotype. Cancer Res 77:e104–e7. https://doi.org/10.1158/0008-5472.CAN-17-0339
Tharmaseelan H, Rotkopf LT, Ayx I et al (2022) Evaluation of radiomics feature stability in abdominal monoenergetic photon counting CT reconstructions. Sci Rep 12:19594. https://doi.org/10.1038/s41598-022-22877-8
Wickham H, Averick M, Bryan J et al. (2019) Welcome to the tidyverse. Journal of Open Source Software 4. https://doi.org/10.21105/joss.01686
Wickham H (2016) ggplot2: elegant graphics for data analysis. Springer-Verlag, New York
Khan JN, Singh A, Nazir SA et al (2015) Comparison of cardiovascular magnetic resonance feature tracking and tagging for the assessment of left ventricular systolic strain in acute myocardial infarction. Eur J Radiol 84:840–8. https://doi.org/10.1016/j.ejrad.2015.02.002
Baessler B, Weiss K, Pinto Dos Santos D (2019) Robustness and reproducibility of radiomics in magnetic resonance imaging: a phantom study. Invest Radiol 54:221–8. https://doi.org/10.1097/RLI.0000000000000530
Emrich T, O’Doherty J, Schoepf UJ et al (2023) Reduced iodinated contrast media administration in coronary CT angiography on a clinical photon-counting detector CT system: a phantom study using a dynamic circulation model. Invest Radiol 58:148–55. https://doi.org/10.1097/RLI.0000000000000911
Mackin D, Fave X, Zhang L et al (2015) Measuring computed tomography scanner variability of radiomics features. Invest Radiol 50:757–65. https://doi.org/10.1097/RLI.0000000000000180
Berenguer R, Pastor-Juan MDR, Canales-Vazquez J et al (2018) Radiomics of CT features may be nonreproducible and redundant: influence of CT acquisition parameters. Radiology 288:407–15. https://doi.org/10.1148/radiol.2018172361
Mannil M, von Spiczak J, Muehlematter UJ et al (2019) Texture analysis of myocardial infarction in CT: comparison with visual analysis and impact of iterative reconstruction. Eur J Radiol 113:245–50. https://doi.org/10.1016/j.ejrad.2019.02.037
Milanese G, Mannil M, Martini K et al (2019) Quantitative CT texture analysis for diagnosing systemic sclerosis: effect of iterative reconstructions and radiation doses. Medicine (Baltimore) 98:e16423. https://doi.org/10.1097/MD.0000000000016423
Meyer M, Ronald J, Vernuccio F et al (2019) Reproducibility of CT radiomic features within the same patient: influence of radiation dose and CT reconstruction settings. Radiology 293:583–91. https://doi.org/10.1148/radiol.2019190928
van Timmeren JE, Cester D, Tanadini-Lang S et al (2020) Radiomics in medical imaging-"how-to" guide and critical reflection. Insights Imaging 11:91. https://doi.org/10.1186/s13244-020-00887-2
Park BW, Kim JK, Heo C et al (2020) Reliability of CT radiomic features reflecting tumour heterogeneity according to image quality and image processing parameters. Sci Rep 10:3852. https://doi.org/10.1038/s41598-020-60868-9
Hertel A, Tharmaseelan H, Rotkopf LT et al (2023) Phantom-based radiomics feature test-retest stability analysis on photon-counting detector CT. Eur Radiol. https://doi.org/10.1007/s00330-023-09460-z
Dunning CAS, Rajendran K, Fletcher JG et al. (2022) Impact of improved spatial resolution on radiomic features using photon-counting-detector CT. Proc SPIE Int Soc Opt Eng 12032. https://doi.org/10.1117/12.2612229