Top

European Radiology

Published in:

01-02-2020 | Magnetic Resonance Imaging | Musculoskeletal

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

Authors: P. A. Gondim Teixeira, H. Biouichi, W. Abou Arab, M. Rios, F. Sirveaux, G. Hossu, A. Blum

Published in: European Radiology | Issue 2/2020

Abstract

Objectives

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

Methods

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.

Results

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.

Conclusion

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.

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, Lyon

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 https://doi.org/10.1200/JCO.2010.33.5489 CrossRef

Shields CJ, Winter DC, Kirwan WO, Redmond HP (2001) Desmoid tumours. Eur J Surg Oncol 27:701–706 https://doi.org/10.1053/ejso.2001.1169 CrossRef

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 https://doi.org/10.1093/annonc/mdt485 CrossRef

Bonvalot S, Desai A, Coppola S et al (2012) The treatment of desmoid tumors: a stepwise clinical approach. Ann Oncol 23:x158–x166 https://doi.org/10.1093/annonc/mds298 CrossRef

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 https://doi.org/10.1245/s10434-009-0733-9 CrossRef

Shinagare AB, Ramaiya NH, Jagannathan JP et al (2011) A to Z of desmoid tumors. AJR Am J Roentgenol 197:W1008–W1014 https://doi.org/10.2214/AJR.11.6657

Kujak JL, Liu PT, Johnson GB, Callstrom MR (2010) Early experience with percutaneous cryoablation of extra-abdominal desmoid tumors. Skeletal Radiol 39:175–182 https://doi.org/10.1007/s00256-009-0801-z CrossRef

Grimer R, Judson I, Peake D, Seddon B (2010) Guidelines for the management of soft tissue sarcomas. Sarcoma 2010:1–15 https://doi.org/10.1155/2010/506182

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 https://doi.org/10.1016/j.ejca.2014.11.005 CrossRef

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 https://doi.org/10.1016/j.ejso.2014.08.095

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 https://doi.org/10.1016/j.diii.2018.06.007

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 https://doi.org/10.1016/j.ejrad.2008.10.012 CrossRef

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 https://doi.org/10.1002/cncr.25089

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 https://doi.org/10.1148/radiol.2016151301 CrossRef

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 https://doi.org/10.1148/radiol.2512081403 CrossRef

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 https://doi.org/10.1097/SLA.0000000000003073

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 https://doi.org/10.1593/tlo.11232 CrossRef

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 https://doi.org/10.1002/cncr.28493

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. https://doi.org/10.1016/j.ejca.2016.03.081

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 https://doi.org/10.1007/s00256-016-2439-y CrossRef

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 https://doi.org/10.1200/JCO.2006.10.4257 CrossRef

23.

Bissler JJ, McCormack FX, Young LR et al (2008) Sirolimus for angiomyolipoma in tuberous sclerosis complex or lymphangioleiomyomatosis. N Engl J Med 358:140–151

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. https://doi.org/10.1186/s13569-016-0060-4

25.

Havaei M, Davy A, Warde-Farley D et al (2017) Brain tumor segmentation with deep neural networks. Med Image Anal 35:18–31 https://doi.org/10.1016/j.media.2016.05.004 CrossRef

Title: Evidence-based MR imaging follow-up strategy for desmoid-type fibromatosis
Authors: P. A. Gondim Teixeira
H. Biouichi
W. Abou Arab
M. Rios
F. Sirveaux
G. Hossu
A. Blum
Publication date: 01-02-2020
Publisher: Springer Berlin Heidelberg
Keywords: Magnetic Resonance Imaging
Magnetic Resonance Imaging
Fibromatosis
Published in: European Radiology / Issue 2/2020
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-019-06404-4

Keynote webinar | Spotlight on medication adherence

Springer Medicine

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

Abstract

Objectives

Methods

Results

Conclusion

Key Points

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Objectives

Methods

Results

Conclusion

Key Points

Please log in to get access to this content

Other articles of this Issue 2/2020

Long-term experience and analysis of data on diagnostic reference levels: the good, the bad, and the ugly

Gadoxetic acid–enhanced MRI as a predictor of recurrence of HCC after liver transplantation

Radiomics of small renal masses on multiphasic CT: accuracy of machine learning–based classification models for the differentiation of renal cell carcinoma and angiomyolipoma without visible fat

Second-look PET-CT following an initial incomplete PET-CT response to (chemo)radiotherapy for head and neck squamous cell carcinoma

Deep learning with ultrasonography: automated classification of liver fibrosis using a deep convolutional neural network

How intrahepatic cholestasis affects liver stiffness in patients with chronic hepatitis B: a study of 1197 patients with liver biopsy