European Radiology

, Volume 26, Issue 1, pp 103–113 | Cite as

Radiologic-pathologic analysis of quantitative 3D tumour enhancement on contrast-enhanced MR imaging: a study of ROI placement

  • Arun Chockalingam
  • Rafael Duran
  • Jae Ho Sohn
  • Rüdiger Schernthaner
  • Julius Chapiro
  • Howard Lee
  • Sonia Sahu
  • Sonny Nguyen
  • Jean-François GeschwindEmail author
  • MingDe Lin



To investigate the influence of region-of-interest (ROI) placement on 3D tumour enhancement [Quantitative European Association for the Study of the Liver (qEASL)] in hepatocellular carcinoma (HCC) patients treated with transcatheter arterial chemoembolization (TACE).


Phase 1: 40 HCC patients had nine ROIs placed by one reader using systematic techniques (3 ipsilateral to the lesion, 3 contralateral to the lesion, and 3 dispersed throughout the liver) and qEASL variance was measured. Intra-class correlations were computed. Phase 2: 15 HCC patients with histosegmentation were selected. Six ROIs were systematically placed by AC (3 ROIs ipsilateral and 3 ROIs contralateral to the lesion). Three ROIs were placed by 2 radiologists. qEASL values were compared to histopathology by Pearson’s correlation, linear regression, and median difference.


Phase 1: The dispersed method (abandoned in phase 2) had low consistency and high variance. Phase 2: qEASL correlated strongly with pathology in systematic methods [Pearson’s correlation coefficient = 0.886 (ipsilateral) and 0.727 (contralateral)] and in clinical methods (0.625 and 0.879). However, ipsilateral placement matched best with pathology (median difference: 5.4 %; correlation: 0.89; regression CI: [0.904, 0.1409]).


qEASL is a robust method with comparable values among tested placements. Ipsilateral placement showed high consistency and better pathological correlation.

Key points

Ipsilateral and contralateral ROI placement produces high consistency and low variance.

Both ROI placement methods produce qEASL values that correlate well with histopathology.

Ipsilateral ROI placement produces best correlation to pathology along with high consistency.


Tumour segmentation MRI Hepatocellular carcinoma TACE ROI 



The scientific guarantor of this publication is Jean-François Geschwind. The authors of this manuscript declare relationships with the following companies: Dr. Geschwind reports grants from NIH, grants from Philips Medical, during the conduct of the study; personal fees from Consultant to Nordion, personal fees from Consultant to Biocompatibles/BTG, personal fees from Consultant to Bayer HealthCare, grants from DOB, grants from Biocompatibles/BTG, grants from Bayer HealthCare, grants from Nordion, grants from Context Vision, grants from SIR, grants from RSNA, grants from Guerbet, outside the submitted work. Dr. Lin reports grants from NIH, during the conduct of the study; and is a Philips employee.

All other authors do not have any conflicts of interest to disclose. Our study has received funding by NIH/NCI R01 CA160771, P30 CA006973, Philips Research North America, Briarcliff Manor, NY, USA.

Institutional Review Board approval was obtained. Written informed consent was waived by the Institutional Review Board.

Our study follows our recently published manuscript to European Radiology where qEASL was shown to predict survival in patients with colorectal metastases to the liver better than the uni- dimensional and bi-dimensional measurements (mRECIST, RECIST, WHO, EASL): Julius Chapiro, Rafael Duran, MingDe Lin, et al. (2015) Early Survival Prediction after Intra-arterial Therapies: A 3D quantitative MRI assessment of Tumour Response after TACE or Radioembolization of Colorectal Cancer Metastases to the Liver. Eur Radiol DOI  10.1007/s00330-015-3595-5.

Methodology: retrospective, experimental, performed at one institution.

Supplementary material

330_2015_3812_MOESM1_ESM.docx (27 kb)
Supporting Table S1 Average qEASL Percentages with Segmentation Data. Tumour location and volume, mean qEASL percentages of enhancing tumour according to the systematic ROI placement method. qEASL, quantitative European Association for the Study of the Liver. Highlighted is the patient used for the segmentation and ROI placement figures. (DOCX 26 kb)


  1. 1.
    Siegel RMJ, Zou Z, Jemal A (2014) Cancer statistics. CA Cancer J Clin 64:9–29CrossRefPubMedGoogle Scholar
  2. 2.
    Chockalingam APC (2013) Battle of the bulge and the burden of gastrointestinal cancers. Pract Gastroenterol XXXVII:15–24Google Scholar
  3. 3.
    European Association For The Study Of The L, European Organisation For R, Treatment Of C (2012) EASL-EORTC clinical practice guidelines: management of hepatocellular carcinoma. J Hepatol 56:908–943CrossRefGoogle Scholar
  4. 4.
    Lo CM, Ngan H, Tso WK et al (2002) Randomized controlled trial of transarterial lipiodol chemoembolization for unresectable hepatocellular carcinoma. Hepatology 35:1164–1171CrossRefPubMedGoogle Scholar
  5. 5.
    Llovet JM, Real MI, Montana X et al (2002) Arterial embolisation or chemoembolisation versus symptomatic treatment in patients with unresectable hepatocellular carcinoma: a randomised controlled trial. Lancet 359:1734–1739CrossRefPubMedGoogle Scholar
  6. 6.
    Duran R, Chapiro J, Frangakis C et al (2014) Uveal melanoma metastatic to the liver: the role of quantitative volumetric contrast-enhanced MR imaging in the assessment of early tumor response after transarterial chemoembolization. Transl Oncol 7:447–455PubMedCentralCrossRefPubMedGoogle Scholar
  7. 7.
    Chapiro J, Wood LD, Lin M et al (2014) Radiologic-pathologic analysis of contrast-enhanced and diffusion-weighted MR imaging in patients with HCC after TACE: diagnostic accuracy of 3D quantitative image analysis. Radiology 273:746–758PubMedCentralCrossRefPubMedGoogle Scholar
  8. 8.
    Chapiro J, Lin M, Duran R, Schernthaner RE, Geschwind JF (2014) Assessing tumor response after loco-regional liver cancer therapies: the role of 3D MRI. Expert Rev Anticancer Ther. doi: 10.1586/14737140.2015.978861:1-7 PubMedCentralPubMedGoogle Scholar
  9. 9.
    Takeda H, Osaki Y, Inuzuka T, Nakajima J, Matsuda F, Sakamoto A, Hatamaru K, Henmi S, Ishikawa T, Saito S, Nishikawa H, Kita R, Kimura T (2012) Appropriateness and limitations of modified response evaluation criteria in solid tumors (mRECIST) in evaluating the efficacy of molecular-targeted therapy for patients with hepatocellular carcinoma. Jpn Soc Hepatol Kanzo 53:147–154Google Scholar
  10. 10.
    Chapiro J, Duran R, MingDe Lin, Rüdiger Schernthaner, David Lesage, Zhijun Wang, Lynn Jeannette Savic, Jean-François Geschwind (2014) Early Survival Prediction after Intraarterial Therapies: A 3D quantitative MRI assessment of Tumor Response after TACE or Radioembolization of Colorectal Cancer Metastases to the Liver. Eur Radiol Epub ahead of printGoogle Scholar
  11. 11.
    Lin M, Pellerin O, Bhagat N et al (2012) Quantitative and volumetric european association for the study of the liver and response evaluation criteria in solid tumors measurements: feasibility of a semiautomated software method to assess tumor response after transcatheter arterial chemoembolization. J Vasc Interv Radiol 23:1629–1637PubMedCentralCrossRefPubMedGoogle Scholar
  12. 12.
    Pellerin O, Lin M, Bhagat N, Ardon R, Mory B, Geschwind JF (2013) Comparison of semi-automatic volumetric VX2 hepatic tumor segmentation from cone beam CT and multi-detector CT with histology in rabbit models. Acad Radiol 20:115–121PubMedCentralCrossRefPubMedGoogle Scholar
  13. 13.
    Tacher V, Lin M, Chao M et al (2013) Semiautomatic volumetric tumor segmentation for hepatocellular carcinoma: comparison between C-arm cone beam computed tomography and MRI. Acad Radiol 20:446–452PubMedCentralCrossRefPubMedGoogle Scholar
  14. 14.
    Viviani R (2010) Unbiased ROI selection in neuroimaging studies of individual differences. Neuroimage 50:184–189CrossRefPubMedGoogle Scholar
  15. 15.
    Murtz P, Flacke S, Traber F, van den Brink JS, Gieseke J, Schild HH (2002) Abdomen: diffusion-weighted MR imaging with pulse-triggered single-shot sequences. Radiology 224:258–264CrossRefPubMedGoogle Scholar
  16. 16.
    Bossuyt PM, Reitsma JB, Bruns DE et al (2003) The STARD statement for reporting studies of diagnostic accuracy: explanation and elaboration. Ann Intern Med 138:W1–W12CrossRefPubMedGoogle Scholar
  17. 17.
    Lee H, Chapiro J, Schernthaner R, Duran R, Wang Z, Gorodetski B, Geschwind J-F, Lin M (2015) How I do it: a practical database management system to assist clinical research teams with data collecting, organization, and reporting. Acad Radiol 22:527–533Google Scholar
  18. 18.
    Bruix J, Sherman M, Llovet JM et al (2001) Clinical management of hepatocellular carcinoma. Conclusions of the Barcelona-2000 EASL conference. European association for the study of the liver. J Hepatol 35:421–430CrossRefPubMedGoogle Scholar
  19. 19.
    Lencioni R, Llovet JM (2010) Modified RECIST (mRECIST) assessment for hepatocellular carcinoma. Semin Liver Dis 30:52–60CrossRefPubMedGoogle Scholar
  20. 20.
    Mitsufuji T, Shinagawa Y, Fujimitsu R et al (2013) Measurement consistency of MR elastography at 3.0 T: comparison among three different region-of-interest placement methods. Jpn J Radiol 31:336–341CrossRefPubMedGoogle Scholar
  21. 21.
    Reeder SB, Cruite I, Hamilton G, Sirlin CB (2011) Quantitative assessment of liver Fat with magnetic resonance imaging and spectroscopy. J Magn Reson Imaging 34:729–749CrossRefPubMedGoogle Scholar
  22. 22.
    Heye T, Merkle EM, Reiner CS et al (2013) Reproducibility of dynamic contrast-enhanced MR imaging Part II. Comparison of intra- and interobserver variability with manual region of interest placement versus semiautomatic lesion segmentation and histogram analysis. Radiology 266:812–821CrossRefPubMedGoogle Scholar
  23. 23.
    Tomaru Y, Inoue T, Oriuchi N, Takahashi K, Endo K (1998) Semi-automated renal region of interest selection method using the double-threshold technique: inter-operator variability in quantitating 99mTc-MAG3 renal uptake. Eur J Nucl Med 25:55–59CrossRefPubMedGoogle Scholar
  24. 24.
    Sattarivand M, Caldwell C, Poon I, Soliman H, Mah K (2013) Effects of ROI placement on PET-based assessment of tumor response to therapy. Int J Mol Imaging 2013:132804PubMedCentralPubMedGoogle Scholar
  25. 25.
    Habte F, Budhiraja S, Keren S, Doyle TC, Levin CS, Paik DS (2013) In situ study of the impact of inter- and intra-reader variability on region of interest (ROI) analysis in preclinical molecular imaging. Am J Nucl Med Mol Imaging 3:175–181PubMedCentralPubMedGoogle Scholar
  26. 26.
    Cutajar M, Mendichovszky IA, Tofts PS, Gordon I (2010) The importance of AIF ROI selection in DCE-MRI renography: reproducibility and variability of renal perfusion and filtration. Eur J Radiol 74:e154–e160CrossRefPubMedGoogle Scholar
  27. 27.
    Levman J, Warner E, Causer P, Martel A (2014) Semi-automatic region-of-interest segmentation based computer-aided diagnosis of mass lesions from dynamic contrast-enhanced magnetic resonance imaging based breast cancer screening. J Digit Imaging 27:670–678PubMedCentralCrossRefPubMedGoogle Scholar
  28. 28.
    Vouche M, Kulik L, Atassi R et al (2013) Radiological-pathological analysis of WHO, RECIST, EASL, mRECIST and DWI: Imaging analysis from a prospective randomized trial of Y90 +/- sorafenib. Hepatology 58:1655–1666CrossRefPubMedGoogle Scholar
  29. 29.
    Shinozaki K, Yoshimitsu K, Irie H et al (2004) Comparison of test-injection method and fixed-time method for depiction of hepatocellular carcinoma using dynamic steady-state free precession magnetic resonance imaging. J Comput Assist Tomogr 28:628–634CrossRefPubMedGoogle Scholar
  30. 30.
    American College of Radiology. Liver Imaging Reporting and Data System version 2014. Accessed March 2015, from

Copyright information

© European Society of Radiology 2015

Authors and Affiliations

  • Arun Chockalingam
    • 1
  • Rafael Duran
    • 1
  • Jae Ho Sohn
    • 1
  • Rüdiger Schernthaner
    • 1
  • Julius Chapiro
    • 1
  • Howard Lee
    • 1
  • Sonia Sahu
    • 1
  • Sonny Nguyen
    • 1
  • Jean-François Geschwind
    • 1
    Email author
  • MingDe Lin
    • 2
  1. 1.Russell H. Morgan Department of Radiology and Radiological Science, Division of Vascular and Interventional RadiologyThe Johns Hopkins HospitalBaltimoreUSA
  2. 2.U/S Imaging and Interventions (UII)Philips Research North AmericaBriarcliff ManorUSA

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