European Radiology

, Volume 30, Issue 2, pp 895–902 | Cite as

Evidence-based MR imaging follow-up strategy for desmoid-type fibromatosis

  • P. A. Gondim TeixeiraEmail author
  • H. Biouichi
  • W. Abou Arab
  • M. Rios
  • F. Sirveaux
  • G. Hossu
  • A. Blum



To propose a follow-up strategy for desmoid-type fibromatosis (DF) based on tumor growth behavior and the signal on T2-weighted MRI.


We retrospectively reviewed 296 MRI studies of 34 patients with histologically proven DF. In each study, tumor volume and T2 signal relatively normal striated muscle were assessed. Volume variation and monthly growth rates were analyzed to determine lesion growth behavior (progressing versus stable/regressing lesions). Growth behavior was correlated with T2 signal, tumor location, β-catenin status, treatment strategy, and follow-up duration. Interobserver variability of volume measurements and interobserver measurement variation ratio were assessed.


There were 25 women and 9 men with a mean age of 39.9 ± 19 (4–73) years. Mean follow-up time in the patients included was 55 ± 41 (12–148) months. In progressing lesions, the mean average monthly growth ratio was 10.9 ± 9.2 (1.1–42.5) %. Interobserver variability of volume measurements was excellent (ICC = 0.96). Mean interobserver measurement variation ratio was 20.4 ± 23.6%. The only factor correlated with tumor growth behavior was T2 signal ratio (p < 0.0001). Seventeen out of 34 (50%) patients presented a signal change over the threshold of 1 during follow-up. There were five occurrences of secondary growth after a period of stability with a mean delay until growth of 38.2 ± 44.2 (17–116) months.


DF growth rate was quantitatively assessed. A threshold for volume variation detection was established. DF growth behavior was significantly related to T2 signal. An evidence-based follow-up strategy is proposed.

Key Points

In progressing desmoid fibromatosis, the mean average monthly growth ratio was 10.9 ± 9.2%.

Lesions with muscle/tumor T2 signal ratios lower than 1 tended to be stable or regress over time.

Given the interobserver measurement variability and MRI in-plane spatial resolution, a variation higher than 42.6% in tumor volume is required to confirm punctual progression.


Aggressive fibromatosis Follow-up studies Magnetic resonance imaging Evidence-based practice Interobserver variability 



Average monthly growth rate


Confidence intervals


Desmoid-type fibromatosis


Echo train length




Intraclass correlation coefficients


Magnetic resonance imaging


Number of excitations


Echo time


Repetition time



We are indebted to Dr. Jean-Luc Verhaeghe for the support in the preparation of this work.


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

Compliance with ethical standards


The scientific guarantor of this publication is Professor Alain Blum.

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

One of the authors has significant statistical expertise.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was not required because retrospective studies with fully anonymized patient data do not require ethics committee approval.


• retrospective

• observational

• performed at one institution


  1. 1.
    Fletcher CDM, Unni KK, Mertens F (2002) Pathology and genetics of tumours of soft tissue and bone. IARC Press, Lyon, France, April 24 - 28, 2002. IARC Press, LyonGoogle Scholar
  2. 2.
    Salas S, Dufresne A, Bui B et al (2011) Prognostic factors influencing progression-free survival determined from a series of sporadic desmoid tumors: a wait-and-see policy according to tumor presentation. J Clin Oncol 29:3553–3558 CrossRefGoogle Scholar
  3. 3.
    Shields CJ, Winter DC, Kirwan WO, Redmond HP (2001) Desmoid tumours. Eur J Surg Oncol 27:701–706 CrossRefGoogle Scholar
  4. 4.
    Gronchi A, Colombo C, Le Péchoux C et al (2014) Sporadic desmoid-type fibromatosis: a stepwise approach to a non-metastasising neoplasm—a position paper from the Italian and the French Sarcoma Group. Ann Oncol 25:578–583 CrossRefGoogle Scholar
  5. 5.
    Bonvalot S, Desai A, Coppola S et al (2012) The treatment of desmoid tumors: a stepwise clinical approach. Ann Oncol 23:x158–x166 CrossRefGoogle Scholar
  6. 6.
    Bonvalot S, Rimareix F, Causeret S et al (2009) Hyperthermic isolated limb perfusion in locally advanced soft tissue sarcoma and progressive desmoid-type fibromatosis with TNF 1 mg and melphalan (T1-M HILP) is safe and efficient. Ann Surg Oncol 16:3350–3357 CrossRefGoogle Scholar
  7. 7.
    Shinagare AB, Ramaiya NH, Jagannathan JP et al (2011) A to Z of desmoid tumors. AJR Am J Roentgenol 197:W1008–W1014
  8. 8.
    Kujak JL, Liu PT, Johnson GB, Callstrom MR (2010) Early experience with percutaneous cryoablation of extra-abdominal desmoid tumors. Skeletal Radiol 39:175–182 CrossRefGoogle Scholar
  9. 9.
    Grimer R, Judson I, Peake D, Seddon B (2010) Guidelines for the management of soft tissue sarcomas. Sarcoma 2010:1–15 Google Scholar
  10. 10.
    Kasper B, Baumgarten C, Bonvalot S et al (2015) Management of sporadic desmoid-type fibromatosis: a European consensus approach based on patients’ and professionals’ expertise – a Sarcoma Patients EuroNet and European Organisation for Research and Treatment of Cancer/Soft Tissue and Bone Sarcoma Group initiative. Eur J Cancer 51:127–136 CrossRefGoogle Scholar
  11. 11.
    Eastley N, Hennig IM, Esler CP, Ashford RU (2014) 99. Nationwide trends in the current management of desmoid (aggressive) fibromatosis. Eur J Surg Oncol 40:S47
  12. 12.
    Gondim Teixeira PA, Chanson A, Verhaeghe JL et al (2019) Correlation between tumor growth and hormonal therapy with MR signal characteristics of desmoid-type fibromatosis: a preliminary study. Diagn Interv Imaging 100:47–55
  13. 13.
    Castellazzi G, Vanel D, Le Cesne A et al (2009) Can the MRI signal of aggressive fibromatosis be used to predict its behavior? Eur J Radiol 69:222–229 CrossRefGoogle Scholar
  14. 14.
    de Camargo VP, Keohan ML, D’Adamo DR, et al (2010) Clinical outcomes of systemic therapy for patients with deep fibromatosis (desmoid tumor). Cancer 116:2258–2265
  15. 15.
    Aghighi M, Boe J, Rosenberg J et al (2016) Three-dimensional radiologic assessment of chemotherapy response in Ewing sarcoma can be used to predict clinical outcome. Radiology 280:905–915 CrossRefGoogle Scholar
  16. 16.
    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 251:447–456 CrossRefGoogle Scholar
  17. 17.
    Cassidy MR, Lefkowitz RA, Long N et al (2018) Association of MRI T2 signal intensity with desmoid tumor progression during active observation: a retrospective cohort study. Ann Surg:1
  18. 18.
    Mozley PD, Bendtsen C, Zhao B et al (2012) Measurement of tumor volumes improves RECIST-based response assessments in advanced lung cancer. Transl Oncol 5:19–25 CrossRefGoogle Scholar
  19. 19.
    Krajewski KM, Nishino M, Franchetti Y, Ramaiya NH, Van den Abbeele AD, Choueiri TK (2014) Intraobserver and interobserver variability in computed tomography size and attenuation measurements in patients with renal cell carcinoma receiving antiangiogenic therapy. Cancer 120:711–721
  20. 20.
    Schwartz LH, Litière S, de Vries E, et al (2016) RECIST 1.1 – update and clarification: from the RECIST committee. Eur J Cancer 62:132–137.
  21. 21.
    Sheth PJ, del Moral S, Wilky BA et al (2016) Desmoid fibromatosis: MRI features of response to systemic therapy. Skeletal Radiol 45:1365–1373 CrossRefGoogle Scholar
  22. 22.
    Burke M, Anderson JR, Kao SC et al (2007) Assessment of response to induction therapy and its influence on 5-year failure-free survival in group III rhabdomyosarcoma: the Intergroup Rhabdomyosarcoma Study-IV experience—a report from the Soft Tissue Sarcoma Committee of the Children’s Oncology Group. J Clin Oncol 25:4909–4913 CrossRefGoogle Scholar
  23. 23.
    Bissler JJ, McCormack FX, Young LR et al (2008) Sirolimus for angiomyolipoma in tuberous sclerosis complex or lymphangioleiomyomatosis. N Engl J Med 358:140–151Google Scholar
  24. 24.
    Dangoor A, Seddon B, Gerrand C, Grimer R, Whelan J, Judson I (2016) UK guidelines for the management of soft tissue sarcomas. Clin Sarcoma Res 6.
  25. 25.
    Havaei M, Davy A, Warde-Farley D et al (2017) Brain tumor segmentation with deep neural networks. Med Image Anal 35:18–31 CrossRefGoogle Scholar

Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  1. 1.Guilloz Imaging Department, Central HospitalRegional University Hospital Center of Nancy (CHRU-Nancy)NancyFrance
  2. 2.Lorraine Cancer InstituteVandoeuvre-lès-NancyFrance
  3. 3.Emile Gallé Surgical CenterRegional University Hospital Center of NancyNancyFrance
  4. 4.Inserm, IADIUniversité de LorraineNancyFrance

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