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Precision of MRI radiomics features in the liver and hepatocellular carcinoma

  • Magnetic Resonance
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To assess the precision of MRI radiomics features in hepatocellular carcinoma (HCC) tumors and liver parenchyma.

Methods

The study population consisted of 55 patients, including 16 with untreated HCCs, who underwent two repeat contrast-enhanced abdominal MRI exams within 1 month to evaluate: (1) test–retest repeatability using the same MRI system (n = 28, 10 HCCs); (2) inter-platform reproducibility between different MRI systems (n = 27, 6 HCCs); (3) inter-observer reproducibility (n = 16, 16 HCCs). Shape and 1st- and 2nd-order radiomics features were quantified on pre-contrast T1-weighted imaging (WI), T1WI portal venous phase (pvp), T2WI, and ADC (apparent diffusion coefficient), on liver regions of interest (ROIs) and HCC volumes of interest (VOIs). Precision was assessed by calculating intraclass correlation coefficient (ICC), concordance correlation coefficient (CCC), and coefficient of variation (CV).

Results

There was moderate to excellent test–retest repeatability of shape and 1st- and 2nd-order features for all sequences in HCCs (ICC: 0.53–0.99; CV: 3–29%), and moderate to good test–retest repeatability of 1st- and 2nd-order features for T1WI sequences, and 2nd-order features for T2WI in the liver (ICC: 0.53–0.73; CV: 12–19%). There was poor inter-platform reproducibility for all features and sequences, except for shape and 1st-order features on T1WI in HCCs (CCC: 0.58–0.99; CV: 3–15%). Good to excellent inter-observer reproducibility was found for all features and sequences in HCCs (CCC: 0.80–0.99; CV: 4–15%) and moderate to good for liver (CCC: 0.45–0.86; CV: 6–25%).

Conclusions

MRI radiomics features have acceptable repeatability in the liver and HCC when using the same MRI system and across readers but have low reproducibility across MR systems, except for shape and 1st-order features on T1WI. Data must be interpreted with caution when performing multiplatform radiomics studies.

Key Points

• MRI radiomics features have acceptable repeatability when using the same MRI system but less reproducible when using different MRI platforms.

• MRI radiomics features extracted from T1 weighted-imaging show greater stability across exams than T2 weighted-imaging and ADC.

• Inter-observer reproducibility of MRI radiomics features was found to be good in HCC tumors and acceptable in liver parenchyma.

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Abbreviations

ADC:

Apparent diffusion coefficient

CCC:

Concordance correlation coefficient

CV:

Coefficient of variation

GLCM:

Gray-level co-occurrence matrix

GLDM:

Gray-level dependence matrix

GLRLM:

Gray-level run length matrix

GLSZM:

Gray-level size zone matrix

HCC:

Hepatocellular carcinoma

IBSI:

Image Biomarker Standardization Initiative

ICC:

Intraclass correlation coefficient

NGTDM:

Neighboring gray tone difference matrix

QIB:

Quantitative imaging biomarker

QIBA:

Quantitative Imaging Biomarkers Alliance

ROI:

Region of interest

T1WIpre:

T1-weighted imaging pre-contrast

T1WIpvp:

T1-weighted imaging portal venous phase

T2WI:

T2-weighted imaging

TE:

Echo time

TR:

Repetition time

VOI:

Volume of interest

References

  1. Kumar V, Gu Y, Basu S et al (2012) Radiomics: the process and the challenges. Magn Reson Imaging 30:1234–1248

    Article  Google Scholar 

  2. Lambin P, Rios-Velazquez E, Leijenaar R et al (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48:441–446

    Article  Google Scholar 

  3. Aerts HJWL, Velazquez ER, Leijenaar RTH et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:1–9

    Google Scholar 

  4. Lambin P, Leijenaar RTH, Deist TM et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14:749–762

    Article  Google Scholar 

  5. Sullivan DC, Obuchowski NA, Kessler LG et al (2015) Metrology standards for quantitative imaging biomarkers. Radiology 277:813–825

    Article  Google Scholar 

  6. Hagiwara A, Fujita S, Ohno Y, Aoki S (2020) Variability and standardization of quantitative imaging: monoparametric to multiparametric quantification, radiomics, and artificial intelligence. Invest Radiol 55:601

    Article  Google Scholar 

  7. van Timmeren JE, Cester D, Tanadini-Lang S, Alkadhi H, Baessler B (2020) Radiomics in medical imaging—“how-to” guide and critical reflection. Insights Imaging 11:1–16

    Article  Google Scholar 

  8. Zwanenburg A, Vallières M, Abdalah MA et al (2020) The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology 295:328–338

    Article  Google Scholar 

  9. Zwanenburg A, Leger S, Vallières M, Löck S (2016) Image biomarker standardisation initiative. arXiv preprint arXiv:161207003

  10. Park JE, Kim D, Kim HS et al (2020) Quality of science and reporting of radiomics in oncologic studies: room for improvement according to radiomics quality score and TRIPOD statement. Eur Radiol 30:523–536

    Article  Google Scholar 

  11. Vallières M, Zwanenburg A, Badic B, Le Rest CC, Visvikis D, Hatt M (2018) Responsible radiomics research for faster clinical translation. Soc Nuclear Med 59:189–193

  12. Heus P, Damen JAAG, Pajouheshnia R et al (2018) Poor reporting of multivariable prediction model studies: towards a targeted implementation strategy of the TRIPOD statement. BMC Med 16:1–12

    Article  Google Scholar 

  13. Traverso A, Wee L, Dekker A, Gillies R (2018) Repeatability and reproducibility of radiomic features: a systematic review. Int J Radiat Oncol Biol Phys 102:1143–1158

    Article  Google Scholar 

  14. Bakr S, Gevaert O, Patel B et al (2020) Interreader variability in semantic annotation of microvascular invasion in hepatocellular carcinoma on contrast-enhanced triphasic CT images. Radiology: Imaging Cancer 2:e190062

    PubMed  PubMed Central  Google Scholar 

  15. Echegaray S, Gevaert O, Shah R et al (2015) Core samples for radiomics features that are insensitive to tumor segmentation: method and pilot study using CT images of hepatocellular carcinoma. J Med Imaging 2:041011

    Article  Google Scholar 

  16. Baessler B, Weiss K, Pinto Dos Santos D (2019) Robustness and reproducibility of radiomics in magnetic resonance imaging: a phantom study. Invest Radiol 54:221–228

    Article  Google Scholar 

  17. Bianchini L, Botta F, Origgi D et al (2020) PETER PHAN: an MRI phantom for the optimisation of radiomic studies of the female pelvis. Physica Med 71:71–81

    Article  Google Scholar 

  18. Fiset S, Welch ML, Weiss J et al (2019) Repeatability and reproducibility of MRI-based radiomic features in cervical cancer. Radiother Oncol 135:107–114

    Article  Google Scholar 

  19. Schwier M, van Griethuysen J, Vangel MG et al (2019) Repeatability of multiparametric prostate MRI radiomics features. Sci Rep 9:9441

    Article  Google Scholar 

  20. Peerlings J, Woodruff HC, Winfield JM et al (2019) Stability of radiomics features in apparent diffusion coefficient maps from a multi-centre test-retest trial. Sci Rep 9:1–10

    Article  CAS  Google Scholar 

  21. Mahon RN, Hugo GD, Weiss E (2019) Repeatability of texture features derived from magnetic resonance and computed tomography imaging and use in predictive models for non-small cell lung cancer outcome. Phys Med Biol 64:145007

    Article  Google Scholar 

  22. Bologna M, Corino V, Mainardi L (2019) Virtual phantom analyses for preprocessing evaluation and detection of a robust feature set for MRI-radiomics of the brain. Med Phys 46:5116–5123

    Article  Google Scholar 

  23. Cattell R, Chen S, Huang C (2019) Robustness of radiomic features in magnetic resonance imaging: review and a phantom study. Vis Comput Ind Biomed Art 2:19

    Article  Google Scholar 

  24. Yang F, Dogan N, Stoyanova R, Ford JC (2018) Evaluation of radiomic texture feature error due to MRI acquisition and reconstruction: a simulation study utilizing ground truth. Phys Med 50:26–36

    Article  Google Scholar 

  25. Um H, Tixier F, Bermudez D, Deasy JO, Young RJ, Veeraraghavan H (2019) Impact of image preprocessing on the scanner dependence of multi-parametric MRI radiomic features and covariate shift in multi-institutional glioblastoma datasets. Phys Med Biol 64:165011

    Article  Google Scholar 

  26. Ammari S, Pitre-Champagnat S, Dercle L et al (2020) Influence of magnetic field strength on magnetic resonance imaging radiomics features in brain imaging, an in vitro and in vivo study. Front Oncol 10:541663

    Article  Google Scholar 

  27. Chernyak V, Fowler KJ, Kamaya A et al (2018) Liver Imaging Reporting and Data System (LI-RADS) version 2018: imaging of hepatocellular carcinoma in at-risk patients. Radiology 289:816–830

    Article  Google Scholar 

  28. O’Sullivan F, Roy S, O’Sullivan J, Vernon C, Eary J (2005) Incorporation of tumor shape into an assessment of spatial heterogeneity for human sarcomas imaged with FDG-PET. Biostatistics 6:293–301

    Article  Google Scholar 

  29. Berenguer R, Pastor-Juan MdR, Canales-Vázquez J et al (2018) Radiomics of CT features may be nonreproducible and redundant: influence of CT acquisition parameters. Radiology 288:407–415

    Article  Google Scholar 

  30. Kessler LG, Barnhart HX, Buckler AJ et al (2015) The emerging science of quantitative imaging biomarkers terminology and definitions for scientific studies and regulatory submissions. Stat Methods Med Res 24:9–26

    Article  Google Scholar 

  31. Hectors SJ, Wagner M, Bane O et al (2017) Quantification of hepatocellular carcinoma heterogeneity with multiparametric magnetic resonance imaging. Sci Rep 7:2452

    Article  Google Scholar 

  32. Hectors SJ, Lewis S, Besa C et al (2020) MRI radiomics features predict immuno-oncological characteristics of hepatocellular carcinoma. Eur Radiol 30:3759–3769

    Article  Google Scholar 

  33. Chen S, Feng S, Wei J et al (2019) Pretreatment prediction of immunoscore in hepatocellular cancer: a radiomics-based clinical model based on Gd-EOB-DTPA-enhanced MRI imaging. Eur Radiol 29:4177–4187

    Article  Google Scholar 

  34. Borhani AA, Catania R, Velichko YS, Hectors S, Taouli B, Lewis S (2021) Radiomics of hepatocellular carcinoma: promising roles in patient selection, prediction, and assessment of treatment response. Abdom Radiol (NY). https://doi.org/10.1007/s00261-021-03085-w

    Article  Google Scholar 

  35. Mayerhoefer ME, Szomolanyi P, Jirak D et al (2009) Effects of magnetic resonance image interpolation on the results of texture-based pattern classification: a phantom study. Invest Radiol 44:405–411

    Article  Google Scholar 

  36. Bartlett JW, Frost C (2008) Reliability, repeatability and reproducibility: analysis of measurement errors in continuous variables. Ultrasound Obstet Gynecol 31:466–475

    Article  CAS  Google Scholar 

  37. Saha A, Harowicz MR, Mazurowski MA (2018) Breast cancer MRI radiomics: An overview of algorithmic features and impact of inter-reader variability in annotating tumors. Med Phys 45:3076–3085

    Article  Google Scholar 

  38. Hu P, Wang J, Zhong H et al (2016) Reproducibility with repeat CT in radiomics study for rectal cancer. Oncotarget 7:71440

    Article  Google Scholar 

  39. van Timmeren JE, Leijenaar RTH, van Elmpt W et al (2016) Test–retest data for radiomics feature stability analysis: generalizable or study-specific? Tomography 2:361

    Article  Google Scholar 

Download references

Funding

This study has received funding by NCI U01 CA172320.

Author information

Authors and Affiliations

  1. BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Guillermo Carbonell, Paul Kennedy, Octavia Bane, Ammar Kirmani, Daniel Stocker, Daniela Said, Sara Lewis, Stefanie Hectors & Bachir Taouli

  2. Department of Radiology, University Hospital Virgen de La Arrixaca, Murcia, Spain

    Guillermo Carbonell

  3. Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Maria El Homsi, Sara Lewis & Bachir Taouli

  4. Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Maria El Homsi

  5. Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland

    Daniel Stocker

  6. Department of Radiology, Universidad de los Andes, Santiago, Chile

    Daniela Said

  7. Department of Radiology, Stanford University, Stanford, CA, USA

    Pritam Mukherjee & Olivier Gevaert

Authors
  1. Guillermo Carbonell
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  2. Paul Kennedy
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Corresponding author

Correspondence to Bachir Taouli.

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Guarantor

The scientific guarantor of this publication is Bachir Taouli.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was obtained from 16 prospectively recruited patients as part of the NCI U01 CA172320. Written informed consent was waived by the Institutional Review Board for the rest of the cohort.

Ethical approval

Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

Data from 16 patients have been previously reported in Bane-2016, Hectors-2016, Hectors-2017, and Jajamovich-2016.

Methodology

  • Retrospective

  • Observational

  • Performed at one institution

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Carbonell, G., Kennedy, P., Bane, O. et al. Precision of MRI radiomics features in the liver and hepatocellular carcinoma. Eur Radiol 32, 2030–2040 (2022). https://doi.org/10.1007/s00330-021-08282-1

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  • DOI: https://doi.org/10.1007/s00330-021-08282-1

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