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Progress of MRI in the preoperative diagnosis of uterine sarcoma
LI Chenrong  BI Guoli  BI Qiu  LIU Xiulan  ZHANG Yingcong  WANG Xianhong  CHENG Changxin 

Cite this article as: LI C R, BI G L, BI Q, et al. Progress of MRI in the preoperative diagnosis of uterine sarcoma[J]. Chin J Magn Reson Imaging, 2024, 15(9): 218-223. DOI:10.12015/issn.1674-8034.2024.09.038.


[Abstract] Uterine sarcomas (US) are highly malignant mesenchymal tumors of the female reproductive system with low incidence and poor prognosis. With the development of minimally invasive and noninvasive treatments and precision medicine, accurate preoperative identification of US will be helpful for the clinical development of personalized treatment plans. In this paper, we will review the research progress of MRI conventional sequences, functional imaging techniques, and MRI-based artificial intelligence analysis in US diagnosis. It aims to help clinicians and imaging physicians to understand the current research status related to US diagnostic imaging and lay the foundation for realizing precision medicine.
[Keywords] uterine sarcomas；magnetic resonance imaging；functional magnetic resonance imaging；artificial intelligence

LI Chenrong1   BI Guoli1, 2*   BI Qiu2   LIU Xiulan1   ZHANG Yingcong1   WANG Xianhong1   CHENG Changxin1  

1 College of Medicine, Kunming University of Science and Technology, Kunming 650000, China

2 Department of MRI,the First People's Hospital of Yunnan Province (the Affiliated Hospital of Kunming University of Science and Technology), Kunming 650032, China

Corresponding author: BI G L, E-mail: guolibi76@163.com

Conflicts of interest   None.

Received  2024-03-03
Accepted  2024-09-10
DOI: 10.12015/issn.1674-8034.2024.09.038
Cite this article as: LI C R, BI G L, BI Q, et al. Progress of MRI in the preoperative diagnosis of uterine sarcoma[J]. Chin J Magn Reson Imaging, 2024, 15(9): 218-223. DOI:10.12015/issn.1674-8034.2024.09.038.

[1]
Collaborative Group of Female Reproductive System Disease of Chinese Society of Pathology. Standardization for pathological diagnosis of primary uterine sarcoma[J]. Chin J Pathol, 2021, 50(9): 987-993. DOI: 10.3760/cma.j.cn112151-20210607-00415.
[2]
YANG L, CAI Y, WANG Y J, et al. Fibroblast growth factor 23 is a potential prognostic biomarker in uterine sarcoma[J/OL]. Technol Cancer Res Treat, 2024, 23: 15330338241245924 [2024-04-17]. https://journals.sagepub.com/doi/10.1177/15330338241245924. DOI: 10.1177/15330338241245924.
[3]
HAYASHI T, SANO K, YAEGASHI N, et al. Characteristic of endometrial stromal sarcoma by algorithm of potential biomarkers for uterine mesenchymal tumor[J]. Curr Issues Mol Biol, 2023, 45(8): 6190-6201. DOI: 10.3390/cimb45080390.
[4]
LIU J X, WANG Z J. Advances in the preoperative identification of uterine sarcoma[J/OL]. Cancers, 2022, 14(14): 3517 [2023-06-20]. https://www.mdpi.com/2072-6694/14/14/3517. DOI: 10.3390/cancers14143517.
[5]
FERRON G, BATAILLON G, MARTINEZ A, et al. Gynecological sarcomas, surgical management: primary, metastatic, and recurrent disease[J]. Int J Gynecol Cancer, 2024, 34(3): 393-402. DOI: 10.1136/ijgc-2023-004582.
[6]
FENNESSY F M, GARGIULO A R. Invited commentary: pelvic MRI is now the basis of personalized fibroid care[J/OL]. Radiographics, 2023, 43(6): e220212 [2023-12-13]. https://pubmed.ncbi.nlm.nih.gov/37261963/. DOI: 10.1148/rg.220212.
[7]
PETROCELLI R, HINDMAN N, REINHOLD C. Current concepts in the imaging of uterine sarcomas[J]. Radiol Clin North Am, 2023, 61(4): 627-638. DOI: 10.1016/j.rcl.2023.02.008.
[8]
ŻAK K, ZAREMBA B, RAJTAK A, et al. Preoperative differentiation of uterine leiomyomas and leiomyosarcomas: current possibilities and future directions[J/OL]. Cancers, 2022, 14(8): 1966 [2023-06-28]. https://www.mdpi.com/2072-6694/14/8/1966. DOI: 10.3390/cancers14081966.
[9]
RAFFONE A, RAIMONDO D, NEOLA D, et al. Diagnostic accuracy of MRI in the differential diagnosis between uterine leiomyomas and sarcomas: a systematic review and meta-analysis[J]. Int J Gynaecol Obstet, 2024, 165(1): 22-33. DOI: 10.1002/ijgo.15136.
[10]
VALLETTA R, CORATO V, LOMBARDO F, et al. Leiomyoma or sarcoma? MRI performance in the differential diagnosis of sonographically suspicious uterine masses[J/OL]. Eur J Radiol, 2024, 170: 111217 [2023-12-11]. https://pubmed.ncbi.nlm.nih.gov/38042020/. DOI: 10.1016/j.ejrad.2023.111217.
[11]
CAMPONOVO C, NEUMANN S, ZOSSO L, et al. Sonographic and magnetic resonance characteristics of gynecological sarcoma[J/OL]. Diagnostics, 2023, 13(7): 1223 [2023-07-10]. https://www.mdpi.com/2075-4418/13/7/1223. DOI: 10.3390/diagnostics13071223.
[12]
HINDMAN N, KANG S, FOURNIER L, et al. MRI evaluation of uterine masses for risk of leiomyosarcoma: a consensus statement[J/OL]. Radiology, 2023, 306(2): e211658 [2023-06-18]. http://pubs.rsna.org/doi/10.1148/radiol.211658. DOI: 10.1148/radiol.211658.
[13]
SOUSA F A E, FERREIRA J, CUNHA T M. MR Imaging of uterine sarcomas: a comprehensive review with radiologic-pathologic correlation[J]. Abdom Radiol (NY), 2021, 46(12): 5687-5706. DOI: 10.1007/s00261-021-03263-w.
[14]
SMITH J, ZAWAIDEH J P, SAHIN H, et al. Differentiating uterine sarcoma from leiomyoma: BET1T2ER Check![J/OL]. Br J Radiol, 2021, 94(1125): 20201332 [2023-12-11]. https://www.birpublications.org/doi/10.1259/bjr.20201332. DOI: 10.1259/bjr.20201332.
[15]
LIN Y, WU R C, HUANG Y L, et al. Uterine fibroid-like tumors: spectrum of MR imaging findings and their differential diagnosis[J]. Abdom Radiol, 2022, 47(6): 2197-2208. DOI: 10.1007/s00261-022-03431-6.
[16]
MATSUURA K, INOUE K, HOSHINO E, et al. Utility of magnetic resonance imaging for differentiating malignant mesenchymal tumors of the uterus from T2-weighted hyperintense leiomyomas[J]. Jpn J Radiol, 2022, 40(4): 385-395. DOI: 10.1007/s11604-021-01217-2.
[17]
FUJII S, GONDA T, YUNAGA H. Clinical utility of diffusion-weighted imaging in gynecological imaging: revisited[J]. Invest Radiol, 2024, 59(1): 78-91. DOI: 10.1097/RLI.0000000000001004.
[18]
HUANG Y T, HUANG Y L, NG K K, et al. Current status of magnetic resonance imaging in patients with malignant uterine neoplasms: a review[J]. Korean J Radiol, 2019, 20(1): 18-33. DOI: 10.3348/kjr.2018.0090.
[19]
VIRARKAR M, DIAB R, PALMQUIST S, et al. Diagnostic performance of MRI to differentiate uterine leiomyosarcoma from benign leiomyoma: a meta-analysis[J/OL]. J Belg Soc Radiol, 2020, 104(1): 69 [2023-06-28]. http://jbsr.be/articles/10.5334/jbsr.2275/. DOI: 10.5334/jbsr.2275.
[20]
ABDEL WAHAB C, JANNOT A S, BONAFFINI P A, et al. Diagnostic algorithm to differentiate benign atypical leiomyomas from malignant uterine sarcomas with diffusion-weighted MRI[J]. Radiology, 2020, 297(2): 361-371. DOI: 10.1148/radiol.2020191658.
[21]
FENG C H, CONLIN C C, BATRA K, et al. Voxel-level classification of prostate cancer on magnetic resonance imaging: improving accuracy using four-compartment restriction spectrum imaging[J]. J Magn Reson Imaging, 2021, 54(3): 975-984. DOI: 10.1002/jmri.27623.
[22]
KIM H, RHA S E, SHIN Y R, et al. Differentiating uterine sarcoma from atypical leiomyoma on preoperative magnetic resonance imaging using logistic regression classifier: added value of diffusion-weighted imaging-based quantitative parameters[J]. Korean J Radiol, 2024, 25(1): 43-54. DOI: 10.3348/kjr.2023.0760.
[23]
LÜ J B, TIAN S F, LIU A L, et al. The value of DKI quantitative parameters in the differential diagnosis of uterine sarcoma and cellular uterine leiomyoma[J]. J Med Imag, 2023, 33(5): 818-821.
[24]
ZHANG Y F, CHEN J J, YANG C, et al. Preoperative prediction of microvascular invasion in hepatocellular carcinoma using diffusion-weighted imaging-based habitat imaging[J]. Eur Radiol, 2024, 34(5): 3215-3225. DOI: 10.1007/s00330-023-10339-2.
[25]
TIAN S, NIU M, XIE L, et al. Diffusion-tensor imaging for differentiating uterine sarcoma from degenerative uterine fibroids[J/OL]. Clin Radiol, 2021, 76(4): 313.e27-313.e32 [2023-06-28]. https://pubmed.ncbi.nlm.nih.gov/33358441/. DOI: 10.1016/j.crad.2020.11.115.
[26]
MA C J, TIAN S F, YU H X, et al. The value of DTI quantitative parameters in the differential diagnosis of uterine sarcoma and cellular uterine leiomyoma[J]. J Clin Radiol, 2022, 41(5): 908-912.
[27]
IIMA M. Perfusion-driven intravoxel incoherent motion (IVIM) MRI in oncology: applications, challenges, and future trends[J]. Magn Reson Med Sci, 2021, 20(2): 125-138. DOI: 10.2463/mrms.rev.2019-0124.
[28]
SATTA S, DOLCIAMI M, CELLI V, et al. Quantitative diffusion and perfusion MRI in the evaluation of endometrial cancer: validation with histopathological parameters[J/OL]. Br J Radiol, 2021, 94(1125): 20210054 [2024-02-28]. https://academic.oup.com/bjr/article/7451643. DOI: 10.1259/bjr.20210054.
[29]
WANG C, ZHENG X Y, ZHOU Z F, et al. Differentiating cellular leiomyoma from uterine sarcoma and atypical leiomyoma using multi-parametric MRI[J/OL]. Front Oncol, 2022, 12: 1005191 [2023-10-07]. https://www.frontiersin.org/articles/10.3389/fonc.2022.1005191/full. DOI: 10.3389/fonc.2022.1005191.
[30]
MALEK M, TABIBIAN E, RAHIMI DEHGOLAN M, et al. A Diagnostic Algorithm using Multi-parametric MRI to Differentiate Benign from Malignant Myometrial Tumors: machine-Learning Method[J/OL]. Sci Rep, 2020, 10(1): 7404 [2023-06-28]. https://www.nature.com/articles/s41598-020-64285-w. DOI: 10.1038/s41598-020-64285-w.
[31]
MUZIO F D, FUSCO R, SIMONETTI I, et al. Functional assessment in endometrial and cervical cancer: diffusion and perfusion, two captivating tools for radiologists[J]. Eur Rev Med Pharmacol Sci, 2023, 27(16): 7793-7810. DOI: 10.26355/eurrev_202308_33435.
[32]
HÉLAGE S, VANDEVENTER S, BUY J N, et al. Uterine sarcomas: are there MRI signs predictive of histopathological diagnosis? A 50-patient case series with pathological correlation[J/OL]. Sarcoma, 2021, 2021: 8880080 [2023-09-11]. https://www.hindawi.com/journals/sarcoma/2021/8880080/. DOI: 10.1155/2021/8880080.
[33]
KIM M, PARK J E, KIM H S, et al. Spatiotemporal habitats from multiparametric physiologic MRI distinguish tumor progression from treatment-related change in post-treatment glioblastoma[J]. Eur Radiol, 2021, 31(8): 6374-6383. DOI: 10.1007/s00330-021-07718-y.
[34]
CAUSA ANDRIEU P, WOO S, KIM T H, et al. New imaging modalities to distinguish rare uterine mesenchymal cancers from benign uterine lesions[J]. Curr Opin Oncol, 2021, 33(5): 464-475. DOI: 10.1097/CO.0000000000000758.
[35]
SOTOMAYOR C G, PARRA C, MIRANDA M, et al. Hyaline and cystic degeneration of uterine leiomyomas: CT and MR imaging with histopathological sample analyses[J/OL]. Diagnostics, 2023, 13(20): 3230 [2023-12-11]. https://www.mdpi.com/2075-4418/13/20/3230. DOI: 10.3390/diagnostics13203230.
[36]
MALEK M, GITY M, ALIDOOSTI A, et al. A machine learning approach for distinguishing uterine sarcoma from leiomyomas based on perfusion weighted MRI parameters[J]. Eur J Radiol, 2019, 110: 203-211. DOI: 10.1016/j.ejrad.2018.11.009.
[37]
BI Q, XIAO Z B, LV F J, et al. Utility of clinical parameters and multiparametric MRI as predictive factors for differentiating uterine sarcoma from atypical leiomyoma[J]. Acad Radiol, 2018, 25(8): 993-1002. DOI: 10.1016/j.acra.2018.01.002.
[38]
TAKEUCHI M, MATSUZAKI K, HARADA M. Clinical utility of susceptibility-weighted MR sequence for the evaluation of uterine sarcomas[J]. Clin Imaging, 2019, 53: 143-150. DOI: 10.1016/j.clinimag.2018.10.015.
[39]
TAKEUCHI M, MATSUZAKI K, BANDO Y, et al. Evaluation of red degeneration of uterine leiomyoma with susceptibility-weighted MR imaging[J]. Magn Reson Med Sci, 2019, 18(2): 158-162. DOI: 10.2463/mrms.mp.2018-0074.
[40]
HONG H, WANG S Y, YU X F, et al. White matter tract injury by MRI in CADASIL patients is associated with iron accumulation[J]. J Magn Reson Imaging, 2023, 57(1): 238-245. DOI: 10.1002/jmri.28301.
[41]
TIAN S F, LIU A L, CHEN A L, et al. Differential diagnosis of uterine sarcoma and degenerative hysteromyoma by using multiple quantitative parameters of enhanced T2 star weighted angiography[J]. Chin J Med Imag, 2020, 28(2): 108-111. DOI: 10.3969/j.issn.1005-5185.2020.02.007.
[42]
ZHANG S, LIU Z, NGUYEN T D, et al. Clinical feasibility of brain quantitative susceptibility mapping[J]. Magn Reson Imaging, 2019, 60: 44-51. DOI: 10.1016/j.mri.2019.04.003.
[43]
DAI M Y, LIU Y, HU Y, et al. Combining multiparametric MRI features-based transfer learning and clinical parameters: application of machine learning for the differentiation of uterine sarcomas from atypical leiomyomas[J]. Eur Radiol, 2022, 32(11): 7988-7997. DOI: 10.1007/s00330-022-08783-7.
[44]
RAHIMIFAR P, HASHEMI H, MALEK M, et al. Diagnostic value of 3 T MR spectroscopy, diffusion-weighted MRI, and apparent diffusion coefficient value for distinguishing benign from malignant myometrial tumours[J/OL]. Clin Radiol, 2019, 74(7): 571.e9-571.e18 [2023-09-11]. https://pubmed.ncbi.nlm.nih.gov/31010584/. DOI: 10.1016/j.crad.2019.03.011.
[45]
ROSA F, MARTINETTI C, MAGNALDI S, et al. Uterine mesenchymal tumors: development and preliminary results of a magnetic resonance imaging (MRI) diagnostic algorithm[J]. Radiol Med, 2023, 128(7): 853-868. DOI: 10.1007/s11547-023-01654-1.
[46]
NAKAGAWA M, NAKAURA T, NAMIMOTO T, et al. A multiparametric MRI-based machine learning to distinguish between uterine sarcoma and benign leiomyoma: comparison with 18F-FDG PET/CT[J/OL]. Clin Radiol, 2019, 74(2): 167.e1-167.e7 [2023-09-11]. https://pubmed.ncbi.nlm.nih.gov/30471748/. DOI: 10.1016/j.crad.2018.10.010.
[47]
MALEK M, RAHMANI M, SEYYED EBRAHIMI S M, et al. Investigating the diagnostic value of quantitative parameters based on T2-weighted and contrast-enhanced MRI with psoas muscle and outer myometrium as internal references for differentiating uterine sarcomas from leiomyomas at 3T MRI[J/OL]. Cancer Imaging, 2019, 19(1): 20 [2023-08-14]. https://cancerimagingjournal.biomedcentral.com/articles/10.1186/s40644-019-0206-8. DOI: 10.1186/s40644-019-0206-8.
[48]
SUZUKI A, KIDO A, MATSUKI M, et al. Development of an algorithm to differentiate uterine sarcoma from fibroids using MRI and LDH levels[J/OL]. Diagnostics, 2023, 13(8): 1404 [2023-06-28]. https:// www.mdpi.com/2075-4418/13/8/1404. DOI: 10.3390/diagnostics13081404.
[49]
SUN S, BONAFFINI P A, NOUGARET S, et al. How to differentiate uterine leiomyosarcoma from leiomyoma with imaging[J]. Diagn Interv Imaging, 2019, 100(10): 619-634. DOI: 10.1016/j.diii.2019.07.007.
[50]
TAMEHISA T, SATO S, SAKAI T, et al. Establishment of noninvasive prediction models for the diagnosis of uterine leiomyoma subtypes[J]. Obstet Gynecol, 2024, 143(3): 358-365. DOI: 10.1097/AOG.0000000000005475.
[51]
FRIZZELL T O, GLASHUTTER M, LIU C C, et al. Artificial intelligence in brain MRI analysis of Alzheimer's disease over the past 12 years: a systematic review[J/OL]. Ageing Res Rev, 2022, 77: 101614 [2023-11-15]. https://linkinghub.elsevier.com/retrieve/pii/S1568163722000563. DOI: 10.1016/j.arr.2022.101614.
[52]
LU H M, XUE H, WANG Y L, et al. Review of application of machine learning in radiomics analysis[J]. Comput Eng Appl, 2023, 59(17): 22-34. DOI: 10.3778/j.issn.1002-8331.2210-0435.
[53]
LAKHMAN Y, VEERARAGHAVAN H, CHAIM J, et al. Differentiation of uterine leiomyosarcoma from atypical leiomyoma: diagnostic accuracy of qualitative MR imaging features and feasibility of texture analysis[J]. Eur Radiol, 2017, 27(7): 2903-2915. DOI: 10.1007/s00330-016-4623-9.
[54]
ZHANG X N, BAI M, MA K R, et al. The value of magnetic resonance imaging histograms in the preoperative differential diagnosis of endometrial stromal sarcoma and degenerative hysteromyoma[J/OL]. Front Surg, 2021, 8: 726067 [2023-06-28]. https://www.frontiersin.org/articles/10.3389/fsurg.2021.726067/full. DOI: 10.3389/fsurg.2021.726067.
[55]
NIU M, LIU A L, ZHANG Q H, et al. The differential diagnosis value of histogram and texture analysis parameters in apparent diffusion coefficient of diffusion weighted image for uterine sarcoma and degenerative uterine fibroids[J]. J Clin Radiol, 2019, 38(10): 1895-1899. DOI: 10.13437/j.cnki.jcr.2019.10.022.
[56]
GERGES L, POPIOLEK D, ROSENKRANTZ A B. Explorative investigation of whole-lesion histogram MRI metrics for differentiating uterine leiomyomas and leiomyosarcomas[J]. AJR Am J Roentgenol, 2018, 210(5): 1172-1177. DOI: 10.2214/AJR.17.18605.
[57]
XIE H H, ZHANG X D, MA S, et al. Preoperative differentiation of uterine sarcoma from leiomyoma: comparison of three models based on different segmentation volumes using radiomics[J]. Mol Imaging Biol, 2019, 21(6): 1157-1164. DOI: 10.1007/s11307-019-01332-7.
[58]
XIE H H, HU J, ZHANG X D, et al. Preliminary utilization of radiomics in differentiating uterine sarcoma from atypical leiomyoma: comparison on diagnostic efficacy of MRI features and radiomic features[J]. Eur J Radiol, 2019, 115: 39-45. DOI: 10.1016/j.ejrad.2019.04.004.
[59]
TOYOHARA Y, SONE K, NODA K, et al. Development of a deep learning method for improving diagnostic accuracy for uterine sarcoma cases[J/OL]. Sci Rep, 2022, 12(1): 19612 [2023-06-28]. https://www.nature.com/articles/s41598-022-23064-5. DOI: 10.1038/s41598-022-23064-5.
[60]
TOYOHARA Y, SONE K, NODA K, et al. The automatic diagnosis artificial intelligence system for preoperative magnetic resonance imaging of uterine sarcoma[J/OL]. J Gynecol Oncol, 2024, 35(3): e24 [2024-01-16]. https://ejgo.org/DOIx.php?id=10.3802/jgo.2024.35.e24. DOI: 10.3802/jgo.2024.35.e24.
[61]
LV T X, HONG X Y, LIU Y, et al. AI-powered interpretable imaging phenotypes noninvasively characterize tumor microenvironment associated with diverse molecular signatures and survival in breast cancer[J/OL]. Comput Methods Programs Biomed, 2024, 243: 107857 [2023-10-26]. https://linkinghub.elsevier.com/retrieve/pii/S0169260723005230. DOI: 10.1016/j.cmpb.2023.107857.
[62]
NAKAGAWA M, NAKAURA T, NAMIMOTO T, et al. Machine learning to differentiate T2-weighted hyperintense uterine leiomyomas from uterine sarcomas by utilizing multiparametric magnetic resonance quantitative imaging features[J]. Acad Radiol, 2019, 26(10): 1390-1399. DOI: 10.1016/j.acra.2018.11.014.
[63]
ROLLER L A, WAN Q, LIU X Y, et al. MRI, clinical, and radiomic models for differentiation of uterine leiomyosarcoma and leiomyoma[J]. Abdom Radiol, 2024, 49(5): 1522-1533. DOI: 10.1007/s00261-024-04198-8.
[64]
RAVEGNINI G, FERIOLI M, MORGANTI A G, et al. Radiomics and artificial intelligence in uterine sarcomas: a systematic review[J/OL]. J Pers Med, 2021, 11(11): 1179 [2023-06-20]. https://www.mdpi.com/2075-4426/11/11/1179. DOI: 10.3390/jpm11111179.

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