Skip to main content
SpringerLink
Log in

Uterine fibroid-like tumors: spectrum of MR imaging findings and their differential diagnosis

  • Review
  • Published:
Abdominal Radiology Aims and scope Submit manuscript

Abstract

Uterine leiomyoma, also known as uterine fibroid, is the most common gynecological tumor, affecting almost 80% of women at some point during their lives. In the same time, other fibroid-like tumors have similar clinical presentations and about 0.5% of resected tumors of which were presumed benign fibroids in the preoperative diagnosis revealed as malignant sarcomas in the final histopathological examination. Amid the emergence of nonsurgical or minimally invasive procedures for symptomatic benign uterine fibroids, such as uterine artery embolization, high-intensity-focused ultrasound, or laparoscopic myomectomy, the preoperative diagnosis of uterine tumors through imaging becomes all the more relevant. Preoperative tissue sampling is challenging because of the variable location of the myometrial mass; thus, the preoperative evaluation of size and location is increasingly performed through magnetic resonance imaging. Features in images might also be useful for examining the full spectrum of such growths, from benign fibroids to neoplasms of uncertain behavior and malignant sarcomas. Benign fibroids include usual-type leiomyomas, myomas with degeneration, and mitotically active leiomyomas. Neoplasms of uncertain behavior include smooth muscle tumors of uncertain malignant potential, leiomyomas with bizarre nuclei, and cellular leiomyomas. Malignant sarcomas comprise leiomyosarcomas, endometrial stromal sarcomas, adenosarcomas, and carcinosarcomas. The purpose of this article is to review the spectrum of MRI findings of uterine fibroid-like tumors, from benign variants, uncertain behavior to malignant sarcomas, and update the advanced imaging modalities, including diffusion-weighted imaging, positron emission tomography/computed tomography, combining texture analysis and radiomics, to tackle this important issue.

Graphical abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data availability

Not applicable.

References

  1. Seidman JD, Thomas RM (1993) Multiple plexiform tumorlets of the uterus. Arch Pathol Lab Med 117:1255-1256.

    CAS  PubMed  Google Scholar 

  2. Giuliani E, As-Sanie S, Marsh EE (2020) Epidemiology and management of uterine fibroids. Int J Gynaecol Obstet 149:3-9. https://doi.org/10.1002/ijgo.13102

    Article  PubMed  Google Scholar 

  3. Amant F, Coosemans A, Debiec-Rychter M, Timmerman D, Vergote I (2009) Clinical management of uterine sarcomas. Lancet Oncol 10:1188-1198. https://doi.org/10.1016/S1470-2045(09)70226-8

    Article  PubMed  Google Scholar 

  4. Lin G, Yang LY, Huang YT, Ng KK, Ng SH, Ueng SH, Chao A, Yen TC, Chang TC, Lai CH (2016) Comparison of the diagnostic accuracy of contrast-enhanced MRI and diffusion-weighted MRI in the differentiation between uterine leiomyosarcoma / smooth muscle tumor with uncertain malignant potential and benign leiomyoma. J Magn Reson Imaging 43:333-342. https://doi.org/10.1002/jmri.24998

    Article  PubMed  Google Scholar 

  5. Munro MG, Critchley HO, Broder MS, Fraser IS, Disorders FWGoM (2011) FIGO classification system (PALM-COEIN) for causes of abnormal uterine bleeding in nongravid women of reproductive age. Int J Gynaecol Obstet 113:3-13. https://doi.org/10.1016/j.ijgo.2010.11.011

    Article  PubMed  Google Scholar 

  6. Quade BJ, Robboy SJ (2009) Uterine smooth muscle tumors. In: Robboy SJ, Mutter GL, Prat J, Bentley RC, Russell P, Anderson MC (ed) Robboy's pathology of the female reproductive tract, 2nd edn. Churchill Livingstone Elsevier, Oxford, pp 474

    Google Scholar 

  7. WHO Classification of Tumors Editorial Board (2020) Tumours of the uterine corpus. In: WHO Classification of Tumours Female Genital Tumours, 5th edn., vol 4. International Agency for Research on Cancer, Lyon, pp 272–276

    Google Scholar 

  8. Mittl RL, Jr., Yeh IT, Kressel HY (1991) High-signal-intensity rim surrounding uterine leiomyomas on MR images: pathologic correlation. Radiology 180:81-83. https://doi.org/10.1148/radiology.180.1.2052728

    Article  PubMed  Google Scholar 

  9. Tamai K, Koyama T, Saga T, Morisawa N, Fujimoto K, Mikami Y, Togashi K (2008) The utility of diffusion-weighted MR imaging for differentiating uterine sarcomas from benign leiomyomas. Eur Radiol 18:723-730. https://doi.org/10.1007/s00330-007-0787-7

    Article  PubMed  Google Scholar 

  10. Ip PP, Tse KY, Tam KF (2010) Uterine smooth muscle tumors other than the ordinary leiomyomas and leiomyosarcomas: a review of selected variants with emphasis on recent advances and unusual morphology that may cause concern for malignancy. Adv Anat Pathol 17:91-112. https://doi.org/10.1097/PAP.0b013e3181cfb901

    Article  PubMed  Google Scholar 

  11. Arleo EK, Schwartz PE, Hui P, McCarthy S (2015) Review of Leiomyoma Variants. AJR Am J Roentgenol 205:912-921. https://doi.org/10.2214/AJR.14.13946

    Article  PubMed  Google Scholar 

  12. DeMulder D, Ascher SM (2018) Uterine Leiomyosarcoma: Can MRI Differentiate Leiomyosarcoma From Benign Leiomyoma Before Treatment? AJR Am J Roentgenol 211:1405-1415. https://doi.org/10.2214/AJR.17.19234

    Article  PubMed  Google Scholar 

  13. Murase E, Siegelman ES, Outwater EK, Perez-Jaffe LA, Tureck RW (1999) Uterine leiomyomas: histopathologic features, MR imaging findings, differential diagnosis, and treatment. Radiographics 19:1179-1197. https://doi.org/10.1148/radiographics.19.5.g99se131179

    Article  CAS  PubMed  Google Scholar 

  14. Nakai G, Yamada T, Hamada T, Atsukawa N, Tanaka Y, Yamamoto K, Higashiyama A, Juri H, Nakamoto A, Yamamoto K, Hirose Y, Ohmichi M, Narumi Y (2017) Pathological findings of uterine tumors preoperatively diagnosed as red degeneration of leiomyoma by MRI. Abdom Radiol (NY) 42:1825-1831. https://doi.org/10.1007/s00261-017-1126-3

    Article  Google Scholar 

  15. Kubik-Huch RA, Weston M, Nougaret S, Leonhardt H, Thomassin-Naggara I, Horta M, Cunha TM, Maciel C, Rockall A, Forstner R (2018) European Society of Urogenital Radiology (ESUR) Guidelines: MR Imaging of Leiomyomas. Eur Radiol 28:3125-3137. https://doi.org/10.1007/s00330-017-5157-5

    Article  PubMed  PubMed Central  Google Scholar 

  16. Causa Andrieu P, Woo S, Kim TH, Kertowidjojo E, Hodgson A, Sun S (2021) New imaging modalities to distinguish rare uterine mesenchymal cancers from benign uterine lesions. Curr Opin Oncol 33:464-475. https://doi.org/10.1097/CCO.0000000000000758

    Article  CAS  PubMed  Google Scholar 

  17. Tasaki A, Asatani MO, Umezu H, Kashima K, Enomoto T, Yoshimura N, Aoyama H (2015) Differential diagnosis of uterine smooth muscle tumors using diffusion-weighted imaging: correlations with the apparent diffusion coefficient and cell density. Abdom Imaging 40:1742-1752. https://doi.org/10.1007/s00261-014-0324-5

    Article  PubMed  Google Scholar 

  18. Li HM, Liu J, Qiang JW, Zhang H, Zhang GF, Ma F (2017) Diffusion-Weighted Imaging for Differentiating Uterine Leiomyosarcoma From Degenerated Leiomyoma. J Comput Assist Tomogr 41:599-606. https://doi.org/10.1097/RCT.0000000000000565

    Article  CAS  PubMed  Google Scholar 

  19. Thomassin-Naggara I, Dechoux S, Bonneau C, Morel A, Rouzier R, Carette MF, Darai E, Bazot M (2013) How to differentiate benign from malignant myometrial tumours using MR imaging. Eur Radiol 23:2306-2314. https://doi.org/10.1007/s00330-013-2819-9

    Article  PubMed  Google Scholar 

  20. Abdel Wahab C, Jannot AS, Bonaffini PA, Bourillon C, Cornou C, Lefrere-Belda MA, Bats AS, Thomassin-Naggara I, Bellucci A, Reinhold C, Fournier LS (2020) Diagnostic Algorithm to Differentiate Benign Atypical Leiomyomas from Malignant Uterine Sarcomas with Diffusion-weighted MRI. Radiology 297:361-371. https://doi.org/10.1148/radiol.2020191658

    Article  PubMed  Google Scholar 

  21. Helage S, Vandeventer S, Buy JN, Bordonne C, Just PA, Jacob D, Ghossain M, Rousset P, Dion E (2021) Uterine Sarcomas: Are There MRI Signs Predictive of Histopathological Diagnosis? A 50-Patient Case Series with Pathological Correlation. Sarcoma 2021:8880080. https://doi.org/10.1155/2021/8880080

    Article  PubMed  PubMed Central  Google Scholar 

  22. Virarkar M, Diab R, Palmquist S, Bassett R, Jr., Bhosale P (2020) Diagnostic Performance of MRI to Differentiate Uterine Leiomyosarcoma from Benign Leiomyoma: A Meta-Analysis. J Belg Soc Radiol 104:69. https://doi.org/10.5334/jbsr.2275

    Article  PubMed  PubMed Central  Google Scholar 

  23. O'Connor DM, Norris HJ (1990) Mitotically active leiomyomas of the uterus. Hum Pathol 21:223-227. https://doi.org/10.1016/0046-8177(90)90133-p

    Article  CAS  PubMed  Google Scholar 

  24. Perrone T, Dehner LP (1988) Prognostically favorable "mitotically active" smooth-muscle tumors of the uterus. A clinicopathologic study of ten cases. Am J Surg Pathol 12:1-8. https://doi.org/10.1097/00000478-198801000-00001

    Article  CAS  PubMed  Google Scholar 

  25. Yamashita Y, Torashima M, Takahashi M, Tanaka N, Katabuchi H, Miyazaki K, Ito M, Okamura H (1993) Hyperintense uterine leiomyoma at T2-weighted MR imaging: differentiation with dynamic enhanced MR imaging and clinical implications. Radiology 189:721-725. https://doi.org/10.1148/radiology.189.3.8234695

    Article  CAS  PubMed  Google Scholar 

  26. Sato K, Yuasa N, Fujita M, Fukushima Y (2014) Clinical application of diffusion-weighted imaging for preoperative differentiation between uterine leiomyoma and leiomyosarcoma. Am J Obstet Gynecol 210:368 e361-368 e368. https://doi.org/10.1016/j.ajog.2013.12.028

    Article  Google Scholar 

  27. Ly A, Mills AM, McKenney JK, Balzer BL, Kempson RL, Hendrickson MR, Longacre TA (2013) Atypical leiomyomas of the uterus: a clinicopathologic study of 51 cases. Am J Surg Pathol 37:643-649. https://doi.org/10.1097/PAS.0b013e3182893f36

    Article  PubMed  Google Scholar 

  28. Bell SW, Kempson RL, Hendrickson MR (1994) Problematic uterine smooth muscle neoplasms. A clinicopathologic study of 213 cases. Am J Surg Pathol 18:535-558.

    Article  CAS  Google Scholar 

  29. Evans HL, Chawla SP, Simpson C, Finn KP (1988) Smooth muscle neoplasms of the uterus other than ordinary leiomyoma. A study of 46 cases, with emphasis on diagnostic criteria and prognostic factors. Cancer 62:2239-2247. https://doi.org/10.1002/1097-0142(19881115)62:10<2239::aid-cncr2820621028>3.0.co;2-y

    Article  CAS  PubMed  Google Scholar 

  30. Gupta M, Laury AL, Nucci MR, Quade BJ (2018) Predictors of adverse outcome in uterine smooth muscle tumours of uncertain malignant potential (STUMP): a clinicopathological analysis of 22 cases with a proposal for the inclusion of additional histological parameters. Histopathology 73:284-298. https://doi.org/10.1111/his.13515

    Article  PubMed  Google Scholar 

  31. Cornfeld D, Israel G, Martel M, Weinreb J, Schwartz P, McCarthy S (2010) MRI appearance of mesenchymal tumors of the uterus. Eur J Radiol 74:241-249. https://doi.org/10.1016/j.ejrad.2009.03.005

    Article  PubMed  Google Scholar 

  32. Clarke MA, Devesa SS, Harvey SV, Wentzensen N (2019) Hysterectomy-Corrected Uterine Corpus Cancer Incidence Trends and Differences in Relative Survival Reveal Racial Disparities and Rising Rates of Nonendometrioid Cancers. J Clin Oncol 37:1895-1908. https://doi.org/10.1200/JCO.19.00151

    Article  PubMed  PubMed Central  Google Scholar 

  33. Mittal KR, Chen F, Wei JJ, Rijhvani K, Kurvathi R, Streck D, Dermody J, Toruner GA (2009) Molecular and immunohistochemical evidence for the origin of uterine leiomyosarcomas from associated leiomyoma and symplastic leiomyoma-like areas. Mod Pathol 22:1303-1311. https://doi.org/10.1038/modpathol.2009.96

    Article  CAS  PubMed  Google Scholar 

  34. Aminzadeh P, Alibrahim E, Dobrotwir A, Paul E, Goergen S (2021) Multiparametric MR evaluation of uterine leiomyosarcoma and STUMP versus leiomyoma in symptomatic women planned for high frequency focussed ultrasound: accuracy of imaging parameters and interobserver agreement for identification of malignancy. Br J Radiol 94:20200483. https://doi.org/10.1259/bjr.20200483

    Article  PubMed  PubMed Central  Google Scholar 

  35. Matsuura K, Inoue K, Hoshino E, Yasuda M, Hasegawa K, Okada Y, Baba Y, Kozawa E (2021) Utility of magnetic resonance imaging for differentiating malignant mesenchymal tumors of the uterus from T2-weighted hyperintense leiomyomas. Jpn J Radiol. https://doi.org/10.1007/s11604-021-01217-2

    Article  PubMed  PubMed Central  Google Scholar 

  36. Ando T, Kato H, Furui T, Morishige KI, Goshima S, Matsuo M (2018) Uterine smooth muscle tumours with hyperintense area on T1 weighted images: differentiation between leiomyosarcomas and leiomyomas. Br J Radiol 91:20170767. https://doi.org/10.1259/bjr.20170767

    Article  PubMed  PubMed Central  Google Scholar 

  37. Lakhman Y, Veeraraghavan H, Chaim J, Feier D, Goldman DA, Moskowitz CS, Nougaret S, Sosa RE, Vargas HA, Soslow RA, Abu-Rustum NR, Hricak H, Sala E (2017) Differentiation of Uterine Leiomyosarcoma from Atypical Leiomyoma: Diagnostic Accuracy of Qualitative MR Imaging Features and Feasibility of Texture Analysis. Eur Radiol 27:2903-2915. https://doi.org/10.1007/s00330-016-4623-9

    Article  PubMed  Google Scholar 

  38. Rio G, Lima M, Gil R, Horta M, Cunha TM (2019) T2 hyperintense myometrial tumors: can MRI features differentiate leiomyomas from leiomyosarcomas? Abdom Radiol (NY) 44:3388-3397. https://doi.org/10.1007/s00261-019-02097-x

    Article  Google Scholar 

  39. Sahin H, Smith J, Zawaideh JP, Shakur A, Carmisciano L, Caglic I, Bruining A, Jimenez-Linan M, Freeman S, Addley H (2021) Diagnostic interpretation of non-contrast qualitative MR imaging features for characterisation of uterine leiomyosarcoma. Br J Radiol 94:20210115. https://doi.org/10.1259/bjr.20210115

    Article  PubMed  Google Scholar 

  40. Seagle BL, Shilpi A, Buchanan S, Goodman C, Shahabi S (2017) Low-grade and high-grade endometrial stromal sarcoma: A National Cancer Database study. Gynecol Oncol 146:254-262. https://doi.org/10.1016/j.ygyno.2017.05.036

    Article  PubMed  Google Scholar 

  41. Kim TH, Kim JW, Kim SY, Kim SH, Cho JY (2018) What MRI features suspect malignant pure mesenchymal uterine tumors rather than uterine leiomyoma with cystic degeneration? J Gynecol Oncol 29:e26. https://doi.org/10.3802/jgo.2018.29.e26

    Article  PubMed  PubMed Central  Google Scholar 

  42. Tirumani SH, Ojili V, Shanbhogue AK, Fasih N, Ryan JG, Reinhold C (2013) Current concepts in the imaging of uterine sarcoma. Abdom Imaging 38:397-411. https://doi.org/10.1007/s00261-012-9919-x

    Article  PubMed  Google Scholar 

  43. Huang YL, Ueng SH, Chen K, Huang YT, Lu HY, Ng KK, Chang TC, Lai CH, Lin G (2019) Utility of diffusion-weighted and contrast-enhanced magnetic resonance imaging in diagnosing and differentiating between high- and low-grade uterine endometrial stromal sarcoma. Cancer Imaging 19:63. https://doi.org/10.1186/s40644-019-0247-z

    Article  PubMed  PubMed Central  Google Scholar 

  44. Takeuchi M, Matsuzaki K, Yoshida S, Kudo E, Bando Y, Hasebe H, Kamada M, Nishitani H (2009) Adenosarcoma of the uterus: magnetic resonance imaging characteristics. Clin Imaging 33:244-247. https://doi.org/10.1016/j.clinimag.2008.11.003

    Article  PubMed  Google Scholar 

  45. Menczer J (2015) Review of Recommended Treatment of Uterine Carcinosarcoma. Curr Treat Options Oncol 16:53. https://doi.org/10.1007/s11864-015-0370-4

    Article  PubMed  Google Scholar 

  46. Bharwani N, Newland A, Tunariu N, Babar S, Sahdev A, Rockall AG, Reznek RH (2010) MRI appearances of uterine malignant mixed mullerian tumors. AJR Am J Roentgenol 195:1268-1275. https://doi.org/10.2214/AJR.10.4419

    Article  PubMed  Google Scholar 

  47. Takahashi M, Kozawa E, Tanisaka M, Hasegawa K, Yasuda M, Sakai F (2016) Utility of histogram analysis of apparent diffusion coefficient maps obtained using 3.0T MRI for distinguishing uterine carcinosarcoma from endometrial carcinoma. J Magn Reson Imaging 43:1301-1307. https://doi.org/10.1002/jmri.25103

    Article  PubMed  Google Scholar 

  48. Kapoor V, McCook BM, Torok FS (2004) An introduction to PET-CT imaging. Radiographics 24:523-543. https://doi.org/10.1148/rg.242025724

    Article  PubMed  Google Scholar 

  49. Kusunoki S, Terao Y, Ujihira T, Fujino K, Kaneda H, Kimura M, Ota T, Takeda S (2017) Efficacy of PET/CT to exclude leiomyoma in patients with lesions suspicious for uterine sarcoma on MRI. Taiwan J Obstet Gynecol 56:508-513. https://doi.org/10.1016/j.tjog.2017.05.003

    Article  PubMed  Google Scholar 

  50. Sun S, Bonaffini PA, Nougaret S, Fournier L, Dohan A, Chong J, Smith J, Addley H, Reinhold C (2019) How to differentiate uterine leiomyosarcoma from leiomyoma with imaging. Diagn Interv Imaging 100:619-634. https://doi.org/10.1016/j.diii.2019.07.007

    Article  CAS  PubMed  Google Scholar 

  51. Ho KC, Dean Fang YH, Lin G, Ueng SH, Wu TI, Lai CH, Chueh HY, Chao A, Chang TC, Yen TC (2018) Presurgical Identification of Uterine Smooth Muscle Malignancies through the Characteristic FDG Uptake Pattern on PET Scans. Contrast Media Mol Imaging 2018:7890241. https://doi.org/10.1155/2018/7890241

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Gerges L, Popiolek D, Rosenkrantz AB (2018) Explorative Investigation of Whole-Lesion Histogram MRI Metrics for Differentiating Uterine Leiomyomas and Leiomyosarcomas. AJR Am J Roentgenol 210:1172-1177. https://doi.org/10.2214/AJR.17.18605

    Article  PubMed  Google Scholar 

  53. Jagannathan JP, Steiner A, Bay C, Eisenhauer E, Muto MG, George S, Fennessy FM (2021) Differentiating leiomyosarcoma from leiomyoma: in support of an MR imaging predictive scoring system. Abdom Radiol (NY) 46:4927-4935. https://doi.org/10.1007/s00261-021-03132-6

    Article  Google Scholar 

  54. Xie H, Hu J, Zhang X, Ma S, Liu Y, Wang X (2019) Preliminary utilization of radiomics in differentiating uterine sarcoma from atypical leiomyoma: Comparison on diagnostic efficacy of MRI features and radiomic features. Eur J Radiol 115:39-45. https://doi.org/10.1016/j.ejrad.2019.04.004

    Article  PubMed  Google Scholar 

  55. Nakagawa M, Nakaura T, Namimoto T, Iyama Y, Kidoh M, Hirata K, Nagayama Y, Yuki H, Oda S, Utsunomiya D, Yamashita Y (2019) Machine Learning to Differentiate T2-Weighted Hyperintense Uterine Leiomyomas from Uterine Sarcomas by Utilizing Multiparametric Magnetic Resonance Quantitative Imaging Features. Acad Radiol 26:1390-1399. https://doi.org/10.1016/j.acra.2018.11.014

    Article  PubMed  Google Scholar 

  56. Wang T, Gong J, Li Q, Chu C, Shen W, Peng W, Gu Y, Li W (2021) A combined radiomics and clinical variables model for prediction of malignancy in T2 hyperintense uterine mesenchymal tumors on MRI. Eur Radiol 31:6125-6135. https://doi.org/10.1007/s00330-020-07678-9

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

We appreciate the help from Kuan-Ying Lu, Dr. Yu-Ting Huang, and Dr. Ting-Chang Chang for assistance in article preparation. The authors thank all the members of the Cancer Center, Chang Gung Memorial Hospital, for their invaluable help. This manuscript was edited by Wallace Academic Editing.

Funding

This work was supported by Chang Gung Medical Foundation Contract Grant Nos. CLRPG3K0023 and CIRPG3H0011; Contract grant sponsor: Ministry of Science and Technology, Taiwan; and Contract Grant Nos. MOST104-2314-B-182A-095-MY3, MOST 109–2628-B-182A-007, and MOST 110–2628-B-182A-018. Chang Gung IRB No. 202100690B0.

Author information

Authors and Affiliations

  1. Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan, 33382, Taiwan

    Yenpo Lin, Yen-Ling Huang, Kueian Chen, Shu-Chi Tseng & Gigin Lin

  2. Imaging Core Laboratory, Institute for Radiological Research, Chang Gung Memorial Hospital at Linkou and Chang Gung University, 5 Fuhsing St., Guishan, Taoyuan, 33382, Taiwan

    Yenpo Lin, Yen-Ling Huang, Kueian Chen, Shu-Chi Tseng & Gigin Lin

  3. Gynecologic Cancer Research Center, Chang Gung Memorial Hospital at Linkou and Chang Gung University, 5 Fuhsing St., Guishan, Taoyuan, 33382, Taiwan

    Ren-Chin Wu, Yen-Ling Huang, Kueian Chen, Chin-Jung Wang, Angel Chao, Chyong-Huey Lai & Gigin Lin

  4. Clinical Metabolomics Core Laboratory, Chang Gung Memorial Hospital at Linkou, 5 Fuhsing St., Guishan, Taoyuan, 33382, Taiwan

    Gigin Lin

  5. Department of Pathology, Chang Gung Memorial Hospital at Linkou and Chang Gung University, 5 Fuhsing St., Guishan, Taoyuan, 33382, Taiwan

    Ren-Chin Wu

  6. Department of Obstetrics and Gynecology, Chang Gung Memorial Hospital at Linkou and Chang Gung University, 5 Fuhsing St., Guishan, Taoyuan, 33382, Taiwan

    Chin-Jung Wang, Angel Chao & Chyong-Huey Lai

Authors
  1. Yenpo Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ren-Chin Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yen-Ling Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kueian Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shu-Chi Tseng
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Chin-Jung Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Angel Chao
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Chyong-Huey Lai
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Gigin Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

YL—Data acquisition, literature research, and manuscript preparation. YLH and KC—data acquisition and review. YL—data analysis. RCW: pathology review. HJH, HHC, AC, and CHL—study conception and design and manuscript editing. GL—study conception and design, manuscript review, and guarantor of integrity of the entire study. All authors had substantial contribution in drafting and approving the final manuscript and agreed to be accountable.

Corresponding author

Correspondence to Gigin Lin.

Ethics declarations

Conflict of interest

The authors declare that they have no competing interests.

Ethical approval

This retrospective study was conducted in accordance with the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of the Chang Gung Memorial Hospital.

Consent to participate

The need for written informed consent was waived by the Ethics Committee.

Consent for publication

Not applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lin, Y., Wu, RC., Huang, YL. et al. Uterine fibroid-like tumors: spectrum of MR imaging findings and their differential diagnosis. Abdom Radiol 47, 2197–2208 (2022). https://doi.org/10.1007/s00261-022-03431-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00261-022-03431-6

Keywords

Search

Navigation