European Radiology

, Volume 29, Issue 2, pp 545–555 | Cite as

High-grade soft-tissue sarcoma: optimizing injection improves MRI evaluation of tumor response

  • Amandine CrombéEmail author
  • François Le Loarer
  • François Cornelis
  • Eberhardt Stoeckle
  • Xavier Buy
  • Sophie Cousin
  • Antoine Italiano
  • Michèle Kind



To determine the acquisition delay after gadolinium-chelate injection that optimizes the prediction of the histological response during anthracycline-based neoadjuvant chemotherapy (NAC) for locally advanced high-grade soft-tissue sarcomas (STS).


Thirty patients (mean age 62 years) were included in this IRB-approved study. All patients received 5-6 cycles of NAC followed by surgery. A good response was defined as ≤ 10% viable cells on histological analysis of the surgical specimen. DCE-MRI was performed before treatment (MRI0) and after two cycles (MRI1). Images were obtained every 8 s. Change in contrast enhancement (CE) between MRI0 and MRI1 was calculated for each acquisition delay ‘t’ on the whole tumor volume. Area under the receiver-operating characteristics curves (AUROC) for change in CE was calculated at each acquisition delay, as well as the accuracy of the Choi criteria.


There were 22 (73.3%) poor responders. Acquisition delay had a significant effect on change in CE and on the response status according to Choi (p = 0.0014 and 0.0270, respectively). The highest AUROC was obtained at t = 58 s (0.792) with an optimal threshold of a -30.5% decrease in CE. At t = 58 s, accuracy to predict a poor response was 82.8% above this threshold, while it was 72.4% and 70% with no objective response according to the Choi criteria and RECIST1.1, respectively.


Optimization of acquisition delay after injection to estimate change in CE improves the prediction of histological response. For STS undergoing NAC, a 60-s delay can be recommended with MRI.

Key points

• Accuracy of response criteria based on contrast enhancement, like the Choi criteria, is dependent on the acquisition delay after gadolinium-chelate injection.

• DCE-MRI helps determine the optimal acquisition delay after gadolinium-chelate injection for improving evaluation of tumor response.

• In soft tissue sarcoma, an acquisition delay at 60 s optimizes the evaluation of the response and accuracy of the Choi criteria.


Response evaluation criteria in solid tumors Sarcoma Magnetic resonance imaging Chemotherapy 



Apparent diffusion coefficient


Area under the ROC curve


Contrast enhancement


95% confidence interval


Diffusion-weighted imaging


European Organization for Research and Treatment of Cancer


Fédération Nationale des Centres de Lutte contre le Cancer


Good histological responder


Gradient-recalled echo


Longest diameter


Magnetic resonance imaging


Neoadjuvant chemotherapy


Negative predictive value


Odds ratio


Poor histological responder


Predictive positive value


Response evaluation criteria in solid tumors




Signal intensity


Signal-to-noise ratio




Soft-tissue sarcoma


Turbo spin echo



The authors state that this work has not received any funding.

Compliance with ethical standards


The scientific guarantor of this publication is Dr. Xavier Buy (interventional radiologist, head of the Department of Radiology of Institut Bergonié, comprehensive cancer center of Bordeaux, France,

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 method was necessary for this paper. Statistical analysis was performed by A. Crombe, a PhD student in applied mathematics at the Institut de Mathématiques de Bordeaux (MOnc Team, INRIA Bordeaux Sud-Ouest CNRS UMR 5251).

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.


• retrospective

• diagnostic or prognostic study

• performed at one institution

Supplementary material

330_2018_5635_MOESM1_ESM.docx (29 kb)
ESM 1 (DOCX 28 kb)


  1. 1.
    Issels RD, Lindner LH, Verweij J et al (2010) Neo-adjuvant chemotherapy alone or with regional hyperthermia for localised high-risk soft-tissue sarcoma: a randomised phase 3 multicentre study. Lancet Oncol 11:561–570. CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Saponara M, Stacchiotti S, Casali PG, Gronchi A (2017) (Neo)adjuvant treatment in localised soft tissue sarcoma: The unsolved affair. Eur J Cancer 70:1–11. CrossRefGoogle Scholar
  3. 3.
    Pasquali S, Gronchi A (2017) Neoadjuvant chemotherapy in soft tissue sarcomas: latest evidence and clinical implications. Ther Adv Med Oncol 9:415–429CrossRefGoogle Scholar
  4. 4.
    Gronchi A, Ferrari S, Quagliuolo V et al (2017) Histotype-tailored neoadjuvant chemotherapy versus standard chemotherapy in patients with high-risk soft-tissue sarcomas (ISG-STS 1001): an international, open-label, randomised, controlled, phase 3, multicentre trial. Lancet Oncol 18:812–822. CrossRefGoogle Scholar
  5. 5.
    Wardelmann E, Haas RL, Bovée JV et al (2016) Evaluation of response after neoadjuvant treatment in soft tissue sarcomas; the European Organization for Research and Treatment of Cancer–Soft Tissue and Bone Sarcoma Group (EORTC–STBSG) recommendations for pathological examination and reporting. Eur J Cancer 53:84–95.
  6. 6.
    Cousin S, Crombé A, Italiano A et al (2017) Clinical, radiological and genetic features associated with the histopathologic response to neoadjuvant chemotherapy (NAC) and outcomes in locally advanced soft tissue sarcoma (STS) patients. J Clin Oncol 35(15_suppl):11014CrossRefGoogle Scholar
  7. 7.
    Mo Z, Zhang T, Zhang F et al (2018) Feasibility and clinical value of CT-guided 125I brachytherapy for metastatic soft tissue sarcoma after first-line chemotherapy failure. Eur Radiol 28:1194–1203. CrossRefGoogle Scholar
  8. 8.
    Pollack SM, Ingham M, Spraker MB, Schwartz GK (2018) Emerging targeted and immune-based therapies in sarcoma. J Clin Oncol 36:125–135. CrossRefGoogle Scholar
  9. 9.
    Eisenhauer EA, Therasse P, Bogaerts J et al (2009) New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1). Eur J Cancer 45:228–247. CrossRefGoogle Scholar
  10. 10.
    Benz MR, Czernin J, Eilber FC et al (2009) FDG-PET/CT imaging predicts histopathologic treatment responses after the initial cycle of neoadjuvant chemotherapy in high-grade soft-tissue sarcomas. Clin Cancer Res 15:2856–2863. CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Dudeck O, Zeile M, Hamm B et al (2008) Diffusion-weighted magnetic resonance imaging allows monitoring of anticancer treatment effects in patients with soft-tissue sarcomas. J Magn Reson Imaging 27:1109–1113. CrossRefGoogle Scholar
  12. 12.
    van Rijswijk CS, Geirnaerdt MJ, Hogendoorn PC et al (2003) Dynamic contrast-enhanced MR imaging in monitoring response to isolated limb perfusion in high-grade soft tissue sarcoma: initial results. Eur Radiol 13:1849–1858.
  13. 13.
    Meyer JM, Perlewitz KS, Ryan CW et al (2013) Phase I trial of preoperative chemoradiation plus sorafenib for high-risk extremity soft tissue sarcomas with dynamic contrast-enhanced MRI correlates. Clin Cancer Res 19:6902–6911. CrossRefGoogle Scholar
  14. 14.
    Huang W, Beckett BR, Ryan CW et al (2016) Evaluation of soft tissue sarcoma response to preoperative chemoradiotherapy using dynamic contrast-enhanced magnetic resonance imaging. Tomography 2:308–316CrossRefGoogle Scholar
  15. 15.
    Soldatos T, Ahlawat S, Montgomery E, Chalian M, Jacobs MA, Fayad LM (2015) Multiparametric assessment of treatment response in high-grade soft-tissue sarcomas with anatomic and functional MR imaging sequences. Radiology 278:831–840Google Scholar
  16. 16.
    Xia W, Yan Z, Gao X (2017) Volume fractions of DCE-MRI parameter as early predictor of histologic response in soft tissue sarcoma: A feasibility study. Eur J Radiol 95:228–235. CrossRefGoogle Scholar
  17. 17.
    Stacchiotti S, Collini P, Messina A et al (2009) High-grade soft-tissue sarcomas: tumor response assessment—pilot study to assess the correlation between radiologic and pathologic response by using RECIST and Choi criteria. Radiology 251:447–456CrossRefGoogle Scholar
  18. 18.
    Stacchiotti S, Verderio P, Messina A et al (2012) Tumor response assessment by modified Choi criteria in localized high-risk soft tissue sarcoma treated with chemotherapy. Cancer 118:5857–5866. CrossRefGoogle Scholar
  19. 19.
    Trojani M, Contesso G, Coindre JM et al (1984) Soft-tissue sarcomas of adults; study of pathological prognostic variables and definition of a histopathological grading system. Int J Cancer 33:37–42CrossRefGoogle Scholar
  20. 20.
    Perkins NJ, Schisterman EF (2006) The inconsistency of “optimal” cutpoints obtained using two criteria based on the receiver operating characteristic curve. Am J Epidemiol 163:670–675. CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Taieb S, Saada-Bouzid E, Tresch E et al (1990) (2015) Comparison of response evaluation criteria in solid tumours and Choi criteria for response evaluation in patients with advanced soft tissue sarcoma treated with trabectedin: a retrospective analysis. Eur J Cancer 51:202–209.
  22. 22.
    Hargreaves BA (2012) Rapid gradient-echo imaging. J Magn Reson Imaging 36:1300–1313.
  23. 23.
    Zur Y, Wood ML, Neuringer LJ (1991) Spoiling of transverse magnetization in steady-state sequences. Magn Reson Med 21:251–263CrossRefGoogle Scholar
  24. 24.
    Gruber L, Loizides A, Ostermann L, Glodny B, Plaikner M, Gruber H (2016) Does size reliably predict malignancy in soft tissue tumours? Eur Radiol 26:4640–4648Google Scholar
  25. 25.
    Sagiyama K, Watanabe Y, Honda H et al (2017) Multiparametric voxel-based analyses of standardized uptake values and apparent diffusion coefficients of soft-tissue tumours with a positron emission tomography/magnetic resonance system: Preliminary results. Eur Radiol 27:5024–5033. CrossRefGoogle Scholar
  26. 26.
    Liang J, Sammet S, Yang X, Jia G, Takayama Y, Knopp MV (2010) Intraindividual in vivo comparison of gadolinium contrast agents for pharmacokinetic analysis using dynamic contrast enhanced magnetic resonance imaging. Invest Radiol 45:233–244Google Scholar

Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  • Amandine Crombé
    • 1
    • 2
    • 3
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  • François Le Loarer
    • 3
    • 4
  • François Cornelis
    • 5
  • Eberhardt Stoeckle
    • 6
  • Xavier Buy
    • 1
  • Sophie Cousin
    • 7
  • Antoine Italiano
    • 3
    • 7
  • Michèle Kind
    • 1
  1. 1.Department of Diagnostic and Interventional RadiologyInstitut BergonieBordeauxFrance
  2. 2.Modelisation in Oncology (MOnc) TeamINRIA Bordeaux-Sud-OuestTalenceFrance
  3. 3.University of BordeauxBordeauxFrance
  4. 4.Department of PathologyInstitut BergonieBordeauxFrance
  5. 5.Department of Radiology, Tenon HospitalSorbonne UniversityParisFrance
  6. 6.Department of SurgeryInstitut BergonieBordeauxFrance
  7. 7.Department of Medical OncologyInstitut BergonieBordeauxFrance

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