Effect of MRI-based semiautomatic size-assessment in cerebral metastases on the RANO-BM classification

  • Hans-Christian BauknechtEmail author
  • Randolf Klingebiel
  • Patrick Hein
  • Claudia Wolf
  • Lars Bornemann
  • Eberhard Siebert
  • Georg Bohner
Original Article



Evaluation of a semiautomatic software algorithm for magnetic resonance imaging (MRI)-based assessment of cerebral metastases in cancer patients.

Material and Methods

Brain metastases (n = 131) in 38 patients, assessed by contrast-enhanced MRI, were retrospectively evaluated at two timepoints (baseline, follow-up) by two experienced neuroradiologists in a blinded manner. The response assessment in neuro-oncology (RANO) criteria for brain metastases (RANO-BM) were applied by means of a software (autoRANO-BM) as well as manually (manRANO-BM) at an interval of 3 weeks.


The average diameter of metastases was 12.03 mm (SD ± 6.66 mm) for manRANO-BM and 13.97 mm (SD ± 7.76 mm) for autoRANO-BM. Diameter figures were higher when using semiautomatic measurements (median = 11.8 mm) as compared to the manual ones (median = 10.2 mm; p = 0.000). Correlation coefficients for intra-observer variability were 0.993 (autoRANO-BM) and 0.979 (manRANO-BM). The interobserver variability (R1/R2) was 0.936/0.965 for manRANO-BM and 0.989/0.998 for autoRANO-BM. A total of 19 lesions (15%) were classified differently when using semiautomatic measurements. In 14 cases with suspected disease progression by manRANO-BM a stable course was found according to autoRANO-BM.


Computerized measuring techniques can aid in the assessment of cerebral metastases by reducing examiner-dependent effects and may consequently result in a different classification according to RANO-BM criteria.


Cerebral metastases Semiautomatic measurement RECIST Response group Radiooncological imaging 


Compliance with ethical guidelines

Conflict of interest

H.-C. Bauknecht, R. Klingebiel, P. Hein, C. Wolf, L. Bornemann, E. Siebert and G. Bohner declare that they have no competing interests.

Ethical standards

This study has been approved by the ethics committee and has therefore been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments.


  1. 1.
    Lin NU, Lee EQ, Aoyama H, Barani IJ, Barboriak DP, Baumert BG, Bendszus M, Brown PD, Camidge DR, Chang SM, Dancey J, de Vries EG, Gaspar LE, Harris GJ, Hodi FS, Kalkanis SN, Linskey ME, Macdonald DR, Margolin K, Mehta MP, Schiff D, Soffietti R, Suh JH, van den Bent MJ, Vogelbaum MA, Wen PY; Response Assessment in Neuro-Oncology (RANO) group. Response assessment criteria for brain metastases: Proposal from the RANO Group. Lancet Oncol. 2015;16:e270–8.CrossRefGoogle Scholar
  2. 2.
    Radbruch A, Bendszus M. Advanced MR imaging in neuro-oncology. Clin Neuroradiol. 2015;25(Suppl 2):143–9.CrossRefGoogle Scholar
  3. 3.
    Eisenhauer EA, Therasse P, Bogaerts J, Schwartz LH, Sargent D, Ford R, Dancey J, Arbuck S, Gwyther S, Mooney M, Rubinstein L, Shankar L, Dodd L, Kaplan R, Lacombe D, Verweij J. New response evaluation criteria in solid tumors: Revised RECIST guideline (version 1.1). Eur J Cancer. 2009;45:228–47.CrossRefGoogle Scholar
  4. 4.
    Suh JH. Stereotactic radiosurgery for the management of brain metastases. N Engl J Med. 2010;362:1119–27.CrossRefGoogle Scholar
  5. 5.
    Hein PA, Romano VC, Rogalla P, Klessen C, Lembcke A, Dicken V, Bornemann L, Bauknecht HC. Linear and volume measurements of pulmonary nodules at different CT dose levels—Intra scan and inter-scan analysis. Rofo. 2009;181:24–31.CrossRefGoogle Scholar
  6. 6.
    Hein PA, Romano VC, Rogalla P, Klessen C, Lembcke A, Bornemann L, Dicken V, Hamm B, Bauknecht HC. Variability of semi automated lung nodule volumetry on ultralow-dose CT: Comparison with nodule volumetry on standard-dose CT. J Digit Imaging. 2010;23:8–17.CrossRefGoogle Scholar
  7. 7.
    Bornemann L, Kuhnigk JM, Dicken V, Zidowitz S, Kuemmerlen B, Krass S, Peitgen HO, Wein BB, Schubert H, Shin HO, Wormanns D. Informatics in radiology (infoRAD): new tools for computer assistance in thoracic CT part 2. Therapy monitoring of pulmonary metastases. Radiographics. 2005;25:841–8.CrossRefGoogle Scholar
  8. 8.
    Kuhnigk JM, Dicken V, Bornemann L et al. Morphological segmentation and partial volume analysis for volumetry of solid pulmonary lesions in thoracic CT scans. IEEE Trans Med Imaging 2006;25(4):417–434CrossRefGoogle Scholar
  9. 9.
    Cohen J. A power primer. Psychological Bulletin. 1992;112:155–9.CrossRefGoogle Scholar
  10. 10.
    O JH, Lodge MA, Wahl RL. Practical PERCIST: A simplified guide to PET response criteria in solid tumors 1.0. Radiology. 2016;280:576–84.CrossRefGoogle Scholar
  11. 11.
    Takimoto CH. Commentary: Tumor growth, patient survival, and the search for the optimal phase II efficacy endpoint. Oncologist. 2008;13:1043–5.CrossRefGoogle Scholar
  12. 12.
    Shah GD, Kesari S, Xu R, Batchelor TT, O’Neill AM, Hochberg FH, Levy B, Bradshaw J, Wen PY. Comparison of linear and volumetric criteria in assessing tumor response in adult high-grade gliomas. Neuro Oncol. 2006;8:38-46.CrossRefGoogle Scholar
  13. 13.
    Klingelhöfer L, Mucha D, Geiger K, Koch R, von Kummer R. Prognostic value of conventional magnetic resonance imaging for adult patients with brain tumors. Clin Neuroradiol. 2015;25:281–9.CrossRefGoogle Scholar
  14. 14.
    Lee GS, Cho SJ, Kim JH, Park HK, Park SQ, Kim RS, Jang JC. Comparative analysis of efficacy and safety of multisession radiosurgery to single dose radiosurgery for metastatic brain tumors. Brain Tumor Res Treat. 2015;3:95–102.CrossRefGoogle Scholar
  15. 15.
    Da Silva AN, Nagayama K, Schlesinger D, Sheehan JP. Early brain tumor metastasis reduction following gamma knife surgery. J Neurosurg. 2009;110:547–52.CrossRefGoogle Scholar
  16. 16.
    Bauknecht HC, Romano VC, Rogalla P, Klingebiel R, Wolf C, Bornemann L, Hamm B, Hein PA. Intra- and interobserver variability of linear and volumetric measurements of brain metastases using contrast-enhanced magnetic resonance imaging. Invest Radiol. 2010;45:49–56.CrossRefGoogle Scholar
  17. 17.
    Vogel MN, Vonthein R, Schmücker S, Maksimovich O, Bethge W, Dicken V, Claussen CD, Horger M. Automated pulmonary nodule volumetry with an optimized algorithm. Accuracy at different slice thicknesses compared to unidimensional and bidimensional measurements. Rofo. 2008;180:791–7.CrossRefGoogle Scholar
  18. 18.
    Gahrmann R, van den Bent M, van der Holt B, Vernhout RM, Taal W, Vos M, de Groot JC, Beerepoot LV, Buter J, Flach ZH, Hanse M, Jasperse B, Smits M. Comparison of 2D (RANO) and volumetric methods for assessment of recurrent glioblastoma treated with bevacizumab—A report from the BELOB trial. Neuro Oncol. 2017;19:853-61.CrossRefGoogle Scholar
  19. 19.
    Fabel M, Wulff A, Heckel F, Bornemann L, Freitag-Wolf S, Heller M, Biederer J, Bolte H. Clinical lymph node staging—Influence of slice thickness and reconstruction kernel on volumetry and RECIST measurements. Eur J Radiol. 2012;81:3124–30.CrossRefGoogle Scholar
  20. 20.
    Huber T, Alber G, Bette S, Boeckh-Behrens T, Gempt J, Ringel F, Alberts E, Zimmer C, Bauer JS. Reliability of semi-automated segmentations in glioblastoma. Clin Neuroradiol. 2017;27:153–61.CrossRefGoogle Scholar
  21. 21.
    Marten K, Auer F, Schmidt S, Kohl G, Rummeny EJ, Engelke C. Inadequacy of manual measurements compared to automated CT volumetry in assessment of treatment response of pulmonary metastases using RECIST criteria. Eur Radiol. 2006;16:781–90.CrossRefGoogle Scholar
  22. 22.
    He L, Teng Y, Jin B, Zhao M, Yu P, Hu X, Zhang J, Li S, Gao Y, Liu Y. Initial partial response and stable disease according to RECIST indicate similar survival for chemotherapeutical patients with advanced non-small cell lung cancer. BMC Cancer. 2010;10:681.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of NeuroradiologyCharité—University Medicine BerlinBerlinGermany
  2. 2.Department of diagnostic and interventional NeuroradiologyProtestant Hospital BethelBielefeldGermany
  3. 3.MunichGermany
  4. 4.Pediatric practice in the medical centerFalkenseeGermany
  5. 5.Fraunhofer Institute for Medical Image Computing MeVisBremenGermany

Personalised recommendations