Photon-counting Detector CT-based Virtual Monoenergetic Reconstructions: Repeatability and Reproducibility of Radiomics Features of an Organic Phantom and Human Myocardium

Overview

Journal Eur Radiol Exp

Date 2023 Oct 24

PMID 37875769

Authors

Elias V Wolf

Lukas Muller

U Joseph Schoepf

Nicola Fink

Joseph P Griffith 3rd

Emese Zsarnoczay

Dhiraj Baruah

Pal Suranyi

Ismael M Kabakus

Moritz C Halfmann

Tilman Emrich

Akos Varga-Szemes

Jim ODoherty

Affiliations

Soon will be listed here.

Abstract

Background: 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.

Methods: 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.

Results: 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%).

Conclusions: VMI levels influence the stability of radiomic features in an organic phantom and patients' LVM; stability decreases considerably below 90 keV.

Relevance Statement: Spectral reconstructions significantly influence radiomic features in vitro and in vivo, necessitating standardization and careful attention to these reconstruction parameters before clinical implementation.

Key Points: • 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.

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References

Khan J, Singh A, Nazir S, Kanagala P, Gershlick A, McCann G . Comparison of cardiovascular magnetic resonance feature tracking and tagging for the assessment of left ventricular systolic strain in acute myocardial infarction. Eur J Radiol. 2015; 84(5):840-8. DOI: 10.1016/j.ejrad.2015.02.002. View

Gillies R, Kinahan P, Hricak H . Radiomics: Images Are More than Pictures, They Are Data. Radiology. 2015; 278(2):563-77. PMC: 4734157. DOI: 10.1148/radiol.2015151169. View

Park B, Kim J, Heo C, Park K . Reliability of CT radiomic features reflecting tumour heterogeneity according to image quality and image processing parameters. Sci Rep. 2020; 10(1):3852. PMC: 7052198. DOI: 10.1038/s41598-020-60868-9. View

Tian X, Sun C, Liu Z, Li W, Duan H, Wang L . Prediction of Response to Preoperative Neoadjuvant Chemotherapy in Locally Advanced Cervical Cancer Using Multicenter CT-Based Radiomic Analysis. Front Oncol. 2020; 10:77. PMC: 7010718. DOI: 10.3389/fonc.2020.00077. View

Huang Y, Liang C, He L, Tian J, Liang C, Chen X . Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer. J Clin Oncol. 2016; 34(18):2157-64. DOI: 10.1200/JCO.2015.65.9128. View

Liu Z, Wang S, Dong D, Wei J, Fang C, Zhou X . The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges. Theranostics. 2019; 9(5):1303-1322. PMC: 6401507. DOI: 10.7150/thno.30309. View

Groen J, Greuter M, Vliegenthart R, Suess C, Schmidt B, Zijlstra F . Calcium scoring using 64-slice MDCT, dual source CT and EBT: a comparative phantom study. Int J Cardiovasc Imaging. 2007; 24(5):547-56. PMC: 2373860. DOI: 10.1007/s10554-007-9282-0. View

Lohmann P, Bousabarah K, Hoevels M, Treuer H . Radiomics in radiation oncology-basics, methods, and limitations. Strahlenther Onkol. 2020; 196(10):848-855. PMC: 7498498. DOI: 10.1007/s00066-020-01663-3. View

Woznicki P, Westhoff N, Huber T, Riffel P, Froelich M, Gresser E . Multiparametric MRI for Prostate Cancer Characterization: Combined Use of Radiomics Model with PI-RADS and Clinical Parameters. Cancers (Basel). 2020; 12(7). PMC: 7407326. DOI: 10.3390/cancers12071767. View

10.

Ayx I, Tharmaseelan H, Hertel A, Norenberg D, Overhoff D, Rotkopf L . Comparison Study of Myocardial Radiomics Feature Properties on Energy-Integrating and Photon-Counting Detector CT. Diagnostics (Basel). 2022; 12(5). PMC: 9141463. DOI: 10.3390/diagnostics12051294. View

11.

Mannil M, von Spiczak J, Muehlematter U, Thanabalasingam A, Keller D, Manka R . Texture analysis of myocardial infarction in CT: Comparison with visual analysis and impact of iterative reconstruction. Eur J Radiol. 2019; 113:245-250. DOI: 10.1016/j.ejrad.2019.02.037. View

12.

Vaidya P, Bera K, Gupta A, Wang X, Corredor G, Fu P . 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. 2020; 2(3):e116-e128. PMC: 7051021. DOI: 10.1016/s2589-7500(20)30002-9. View

13.

An C, Li D, Li S, Li W, Tong T, Liu L . Deep learning radiomics of dual-energy computed tomography for predicting lymph node metastases of pancreatic ductal adenocarcinoma. Eur J Nucl Med Mol Imaging. 2021; 49(4):1187-1199. DOI: 10.1007/s00259-021-05573-z. View

14.

Emrich T, ODoherty J, Schoepf U, Suranyi P, Aquino G, Kloeckner R . 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. 2022; 58(2):148-155. DOI: 10.1097/RLI.0000000000000911. View

15.

Baessler B, Weiss K, Pinto Dos Santos D . Robustness and Reproducibility of Radiomics in Magnetic Resonance Imaging: A Phantom Study. Invest Radiol. 2018; 54(4):221-228. DOI: 10.1097/RLI.0000000000000530. View

16.

van Timmeren J, Cester D, Tanadini-Lang S, Alkadhi H, Baessler B . Radiomics in medical imaging-"how-to" guide and critical reflection. Insights Imaging. 2020; 11(1):91. PMC: 7423816. DOI: 10.1186/s13244-020-00887-2. View

17.

Meyer M, Ronald J, Vernuccio F, Nelson R, Ramirez-Giraldo J, Solomon J . Reproducibility of CT Radiomic Features within the Same Patient: Influence of Radiation Dose and CT Reconstruction Settings. Radiology. 2019; 293(3):583-591. PMC: 10860741. DOI: 10.1148/radiol.2019190928. View

18.

Tharmaseelan H, Rotkopf L, Ayx I, Hertel A, Norenberg D, Schoenberg S . Evaluation of radiomics feature stability in abdominal monoenergetic photon counting CT reconstructions. Sci Rep. 2022; 12(1):19594. PMC: 9665022. DOI: 10.1038/s41598-022-22877-8. View

19.

Berenguer R, Pastor-Juan M, Canales-Vazquez J, Castro-Garcia M, Villas M, Mansilla Legorburo F . Radiomics of CT Features May Be Nonreproducible and Redundant: Influence of CT Acquisition Parameters. Radiology. 2018; 288(2):407-415. DOI: 10.1148/radiol.2018172361. View

20.

Euler A, Laqua F, Cester D, Lohaus N, Sartoretti T, Pinto Dos Santos D . Virtual Monoenergetic Images of Dual-Energy CT-Impact on Repeatability, Reproducibility, and Classification in Radiomics. Cancers (Basel). 2021; 13(18). PMC: 8467875. DOI: 10.3390/cancers13184710. View