Sonographic and Magnetic Resonance Characteristics of Gynecological Sarcoma
Abstract
:1. Introduction
2. Materials and Methods
3. Results
3.1. Ultrasound
3.1.1. General Characteristics of Sarcoma
3.1.2. Characteristics of Sarcoma Subtypes
3.1.3. Accuracy
Author | Year | Study Type | Number of Patients | Sarcoma-Subtypes (Number of Patients) | Objective |
---|---|---|---|---|---|
Kohler et al. [20] | 2019 | Prospective study | 293 | LMS | Developing a preoperative leiomyoma score |
Gaetke-Udager et al. [29] | 2016 | Retrospective study | 10 | LMS | Diagnostic accuracy of ultrasound for LMS vs. LM |
Cho et al. [9] | 2016 | Retrospective study | 31 | 14 ESS, 11 LMS, 6 US | Identify preoperative diagnostic findings suggestive of uterine sarcoma |
Ludovisi et al. [7] | 2019 | Retrospective multicenter study | 195 | 116 LMS, 48 ESS, 31 UES | Clinical and ultrasound characteristics of uterine sarcomas |
Gao et al. [21] | 2014 | Retrospective study | 80 | 38 ESS, 22 LMS, 18 CS, 2 US | Characteristics of uterine sarcoma (in China) |
Li et al. [27] | 2020 | Retrospective study | 114 | 50 LG-ESS, 34 LMS, 13 HG-ESS, 9 UUS, 8 AS | The accuracy of preoperative diagnosis with US |
Alcazar et al. [24] | 2012 | Retrospective study | 9 | 4 CS, 5 others | Gray-scale and color Doppler ultrasound features of uncommon primary malignant ovarian tumors |
Cheng et al. [22] | 2020 | Retrospective study | 72 | 27 ESS, 20 LMS, 15 AS | Common imaging findings of uterine sarcoma |
Najibi et al. [28] | 2021 | Cross-sectional study | 37 | not specified | Diagnostic accuracy of ultrasound in benign vs. malignant myometrial tumors |
Ciccarone et al. [25] | 2021 | Retrospective multicenter study | 91 | CS (Ovaries) | Clinical and ultrasound characteristics of ovarian carcinosarcoma |
Park et al. [26] | 2016 | Retrospective analysis | 10 | LG-ESS | US findings associated with LG-ESS |
Bonneau et al. [23] | 2013 | Retrospective cohort study | 23 | 7 UES, 6 CS, 4 STUMP, 3 LMS, 2 LG-ESS | US performance for differentiating LM vs. MMT |
3.2. MRI
3.2.1. Characteristics for All Subtypes
3.2.2. Characteristics Only LMS
3.2.3. Characteristics Other Subtypes of Sarcoma
3.3. Special Imaging Techniques
Accuracy
Author | Year | Study-Type | Number of Patients | Sarcoma-Subtypes (Number of Patients) | Objective |
---|---|---|---|---|---|
Li et al. [27] | 2020 | Retrospective study | 34 | 15 LG-ESS, 10 LMS, 5 HG-ESS, 3 UUS, 1 AS | The accuracy of preoperative diagnosis with MRI |
Sumi et al. [37] | 2015 | Retrospective study | 25 | 11 CS, 8 LMS, 6 ESS | Distinguish major histological types of uterine sarcomas |
Saida et al. [43] | 2021 | Retrospective case-control study | 12 | CS (ovary) | Imaging and clinical characteristics of ovarian carcinosarcoma (CS) compared with high-grade serous carcinoma. |
Takeuchi et al. [35] | 2019 | Retrospective case-control study | 10 | 6 CS, 3 LMS, 1 ESS | Susceptibility-weighted MR sequences (SWS) for diagnosis of sarcoma |
Lin et al. [50] | 2015 | Prospective study | 8 | 6 LMS, 2 STUMP | Diagnostic accuracy of CE-MRI vs. DWI for LMS/STUMP vs. LM |
Sahin et al. [39] | 2021 | Retrospective case-control study | 16 | LMS | Non-contrast MRI features of LMS and atypical LM |
Rahimifar et al. [49] | 2019 | Prospective study | 14 | Not specified | DWI and MR-Spectroscopy for differentiation; combining ADC and MRS for better accuracy |
Lakhman et al. [8] | 2017 | Retrospective study | 19 | LMS | Qualitative MR features to distinguish LMS from ALM, feasibility of texture analysis |
Li et al. [40] | 2017 | Retrospective study | 16 | LMS | DWI for differentiation LMS and degenerated LM |
Li et al. [44] | 2017 | Retrospective study | 15 | 13 LG-ESS, 2 HG-ESS | Conventional MRI and DWI features of ESS and correlation of ADC-value and Ki-67 expression |
Gerges et al. [48] | 2018 | Retrospective study | 17 | LMS | Texture analysis of multiple MRI sequences for differentiation of LMS and LM |
Thomassin-Naggara et al. [33] | 2013 | Retrospective study | 25 | 9 UES, 4 CS, 3 LMS, 2 LG-ESS, 1 RMS, 6 STUMP | MRI for differentiation malignant vs. benign |
Malek et al. [38] | 2019 | Prospective study | 14 | Not specified | Diagnostic accuracy of preoperative quantitative metrics based on T2WI and CE-MRI |
Zhang et al. [47] | 2014 | Prospective study | 22 | 7 LMS, 9 ESS+AS, 6 CS | MRI and DWI for categorization of uterine sarcoma (compared to pathology) |
Rio et al. [41] | 2019 | Retrospective study | 20 | LMS | MRI features differentiating atypical and degenerated LM with hyperintensity on T2WI from LMS |
Bi et al. [46] | 2020 | Observational study | 71 | 29 ESS, 27 CS, 15 LMS | MRI features incl. ADC for preoperative identification of sarcoma subtypes |
Ando et al. [42] | 2018 | Retrospective study | 19 | 14 LMS, 5 STUMP | Differences of LMS vs. LM with T1WI hyperintense areas (T1HIAs) |
Bi et al. [34] | 2018 | Retrospective study | 36 | 24 ESS, 12 LMS | Qualitative and quantitative MRI features of sarcoma vs. ALM |
Huang et al. [45] | 2019 | Retrospective study | 20 | 11 HG-ESS, 9 LG-ESS | Diagnostic accuracy of MRI in diagnosing and differentiating HG- vs. LG-ESS |
Najibi et al. [28] | 2021 | Cross-sectional study | 63 | Not specified | Diagnostic accuracy CE/DWI-MRI for differentiating malignant vs. benign myometrial tumors |
Gaetke-Udager et al. [29] | 2016 | Retrospective study | 7 | LMS | Diagnostic accuracy MRI without DWI for LMS vs. LM |
4. Discussion
4.1. Ultrasound
Key Ultrasound Features of Sarcoma | |
---|---|
Tumor size | Large, diameter >8 cm |
Type of tumor | Solid |
Borders | Poorly defined |
Echogenicity | Heterogeneous |
Shadowing | No acoustic shadowing |
Vascularization | Moderate to rich vascularization |
Degenerations | Cystic changes or degenerations common |
4.2. MRI
Key MRI Features of Sarcoma | |
---|---|
Borders | Irregular or ill-defined |
SI on T2WI | Heterogeneous and intermediate to high SI |
Degeneration | Hemorrhage, necrosis, cystic degenerations |
SI on DWI | Hyperintense SI |
ADC value | Low/below cutoff |
Enhancement | Heterogeneous enhancement on CE-MRI |
SI on T1WI | Low SI with areas of high SI |
4.3. Other Imagings
4.4. Laboratory Testing
5. Conclusions and Limitations
6. Patents
Declaration of Authorship
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
LG-ESS | low grade endometrial stromal sarcoma |
HG-ESS | high grade endometrial stromal sarcoma |
LMS | leiomyosarcoma |
CS | carcinosarcoma |
UUS | undifferentiated uterine sarcoma |
CT | computed tomography |
MRI | magnetic resonance imaging |
MMT | malignant mesenchymal tumor |
AS | adenosarcoma |
ALM | atypical leiomyoma |
MMMT | malignant mullerian mixed tumor |
STUMP | smooth muscle tumors of uncertain malignant potential |
PET/CT | positron emission tomography/computed tomography |
FDG-PET | fluorine-18-fluorodeoxyglucose positron emission tomography |
SUV | standardized uptake value |
LDH | serum lactate dehydrogenase |
DWI | diffusion-weighted imaging |
ADC | apparent diffusion coefficient |
ROC | receiver operating characteristic |
T1WI | T1-weighted imaging |
T2WI | T2-weighted imaging |
Gd-DTPA | gadolinium diethylenetriaminepentaacetic acid |
CE-MRI | contrast-enhanced MRI |
SI | signal intensity |
SWAN | T2 star-weighted MR angiography |
SWI | susceptibility-weighted MR |
CR | contrast ratio |
CER | contrast-enhanced ratio |
T1 HIA | hyperintense areas on T1WI |
MRS | magnetic resonance spectroscopy |
NPV | negative predictive value |
PPV | positive predictive value |
BET1T2ER Check! | border enhancement, T1WISI, T2WISI, endometrial thickening, restricted diffusion |
PRESS | PREoperative sarcoma score |
References
- Uterine sarcoma. Guideline of the DGGG and OEGGG (S2k-Level, AWMF Registry No.015/074, April 2021). Available online: https://www.awmf.org/uploads/tx_szleitlinien/015-074l_S2k_Uterine_Sakrome_2021-04.pdf (accessed on 30 December 2021).
- Barral, M.; Placé, V.; Dautry, R.; Bendavid, S.; Cornelis, F.; Foucher, R.; Guerrache, Y.; Soyer, P. Magnetic resonance imaging features of uterine sarcoma and mimickers. Abdom. Imaging 2017, 42, 1762–1772. [Google Scholar] [CrossRef]
- Oh, J.; Bin Park, S.; Park, H.J.; Lee, E.S. Ultrasound Features of Uterine Sarcomas. Ultrasound Q. 2019, 35, 376–384. [Google Scholar] [CrossRef]
- Sun, S.; Bonaffini, P.A.; Nougaret, S.; Fournier, L.; Dohan, A.; Chong, J.; Smith, J.; Addley, H.; Reinhold, C. How to differentiate uterine leiomyosarcoma from leiomyoma with imaging. Diagn. Interv. Imaging 2019, 100, 619–634. [Google Scholar] [CrossRef] [PubMed]
- Roberts, M.E.; Aynardi, J.T.; Chu, C.S. Uterine leiomyosarcoma: A review of the literature and update on management options. Gynecol. Oncol. 2018, 151, 562–572. [Google Scholar] [CrossRef]
- Kostov, S.; Kornovski, Y.; Ivanova, V.; Dzhenkov, D. New Aspects of Sarcomas of Uterine Corpus—A Brief Narrative Review. Clin. Pract. 2021, 11, 878–900. [Google Scholar] [CrossRef]
- Ludovisi, M.; Moro, F.; Pasciuto, T.; Di Noi, S.; Giunchi, S.; Savelli, L.; Pascual, M.A.; Sladkevicius, P.; Alcazar, J.L.; Franchi, D.; et al. Imaging in gynecological disease (15): Clinical and ultrasound characteristics of uterine sarcoma. Ultrasound Obstet. Gynecol. 2019, 54, 676–687. [Google Scholar] [CrossRef]
- Lakhman, Y.; Veeraraghavan, H.; Chaim, J.; Feier, D.; Goldman, D.A.; Moskowitz, C.S.; Nougaret, S.; Sosa, R.E.; Vargas, H.A.; Soslow, R.A.; et al. Differentiation of Uterine Leiomyosarcoma from Atypical Leiomyoma: Diagnostic Accuracy of Qualitative MR Imaging Features and Feasibility of Texture Analysis. Eur. Radiol. 2017, 27, 2903–2915. [Google Scholar] [CrossRef] [PubMed]
- Cho, H.-Y.; Kim, K.; Kim, Y.-B.; No, J.H. Differential diagnosis between uterine sarcoma and leiomyoma using preoperative clinical characteristics. J. Obstet. Gynaecol. Res. 2015, 42, 313–318. [Google Scholar] [CrossRef] [PubMed]
- Wu, T.-I.; Yen, T.-C.; Lai, C.-H. Clinical presentation and diagnosis of uterine sarcoma, including imaging. Best Pract. Res. Clin. Obstet. Gynaecol. 2011, 25, 681–689. [Google Scholar] [CrossRef] [PubMed]
- DeMulder, D.; Ascher, S.M. Uterine Leiomyosarcoma: Can MRI Differentiate Leiomyosarcoma from Benign Leiomyoma Before Treatment? Am. J. Roentgenol. 2018, 211, 1405–1415. [Google Scholar] [CrossRef]
- Song, K.-J.; Yu, X.-N.; Lv, T.; Chen, Y.-L.; Diao, Y.-C.; Liu, S.-L.; Wang, Y.-K.; Yao, Q. Expression and prognostic value of lactate dehydrogenase-A and -D subunits in human uterine myoma and uterine sarcoma. Medicine 2018, 97, e0268. [Google Scholar] [CrossRef]
- Nagai, T.; Takai, Y.; Akahori, T.; Ishida, H.; Hanaoka, T.; Uotani, T.; Sato, S.; Matsunaga, S.; Baba, K.; Seki, H. Novel uterine sarcoma preoperative diagnosis score predicts the need for surgery in patients presenting with a uterine mass. Springerplus 2014, 3, 1–7. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Goto, A.; Takeuchi, S.; Sugimura, K.; Maruo, T. Usefulness of Gd-DTPA contrast-enhanced dynamic MRI and serum determi-nation of LDH and its isozymes in the differential diagnosis of leiomyosarcoma from degenerated leiomyoma of the uterus. Int. J. Gynecol. Cancer 2002, 12, 354–361. [Google Scholar] [CrossRef] [PubMed]
- Nishigaya, Y.; Kobayashi, Y.; Matsuzawa, Y.; Hasegawa, K.; Fukasawa, I.; Watanabe, Y.; Tokunaga, H.; Yaegashi, N.; Iwashita, M. Diagnostic value of combination serum assay of lactate dehydrogenase, D-dimer, and C-reactive protein for uterine leiomyosarcoma. J. Obstet. Gynaecol. Res. 2018, 45, 189–194. [Google Scholar] [CrossRef] [Green Version]
- Bansal, N.; Herzog, T.J.; Burke, W.; Cohen, C.J.; Wright, J.D. The utility of preoperative endometrial sampling for the detection of uterine sarcomas. Gynecol. Oncol. 2008, 110, 43–48. [Google Scholar] [CrossRef] [PubMed]
- Ricci, S.; Stone, R.L.; Fader, A.N. Uterine leiomyosarcoma: Epidemiology, contemporary treatment strategies and the impact of uterine morcellation. Gynecol. Oncol. 2017, 145, 208–216. [Google Scholar] [CrossRef] [PubMed]
- Gockley, A.A.; Rauh-Hain, J.A.; del Carmen, M.G. Uterine leiomyosarcoma: A review article. Int. J. Gynecol. Cancer 2014, 24, 1538–1542. [Google Scholar] [CrossRef]
- Stukan, M.; Rutkowski, P.; Smadja, J.; Bonvalot, S. Ultrasound-Guided Trans-Uterine Cavity Core Needle Biopsy of Uterine Myometrial Tumors to Differentiate Sarcoma from a Benign Lesion—Description of the Method and Review of the Literature. Diagnostics 2022, 12, 1348. [Google Scholar] [CrossRef]
- Köhler, G.; Vollmer, M.; Nath, N.; Hessler, P.-A.; Dennis, K.; Lehr, A.; Köller, M.; Riechmann, C.; Bralo, H.; Trojnarska, D.; et al. Benign uterine mass—Discrimination from leiomyosarcoma by a preoperative risk score: A multicenter cohort study. Arch. Gynecol. Obstet. 2019, 300, 1719–1727. [Google Scholar] [CrossRef]
- Gao, Y.; Meng, H.; Zhang, Y.; Jiao, T.; Hui, N. Retrospective analysis of 80 cases with uterine carcinosarcoma, leiomyosarcoma and endometrial stromal sarcoma in China, 1988–2007. Int. J. Clin. Exp. Pathol. 2014, 7, 1616–1624. [Google Scholar]
- Cheng, G.; Li, Y.; Qu, P. Clinicopathological characteristics of patients with uterine sarcoma: Clinical presentation, treatment, and survival outcomes. J. Clin. Exp. Med. 2020, 13, 7794–7804. [Google Scholar]
- Bonneau, C.; Thomassin-Naggara, I.; Dechoux, S.; Cortez, A.; Darai, E.; Rouzier, R. Value of ultrasonography and magnetic resonance imaging for the characterization of uterine mesenchymal tumors. Acta Obstet. Gynecol. Scand. 2013, 93, 261–268. [Google Scholar] [CrossRef]
- Alcázar, J.L.; Guerriero, S.; Pascual, M.; Ajossa, S.; Olartecoechea, B.; Hereter, L. Clinical and sonographic features of uncommon primary ovarian malignancies. J. Clin. Ultrasound 2011, 40, 323–329. [Google Scholar] [CrossRef] [PubMed]
- Ciccarone, F.; Biscione, A.; Moro, F.; Fischerova, D.; Savelli, L.; Munaretto, M.; Jokubkiene, L.; Sladkevicius, P.; Chiappa, V.; Fruscio, R.; et al. Imaging in gynecological disease (23): Clinical and ultrasound characteristics of ovarian carcinosarcoma. Ultrasound Obstet. Gynecol. 2021, 59, 241–247. [Google Scholar] [CrossRef] [PubMed]
- Park, G.E.; Rha, S.E.; Oh, S.N.; Lee, A.; Lee, K.H.; Kim, M.-R. Ultrasonographic findings of low-grade endometrial stromal sarcoma of the uterus with a focus on cystic degeneration. Ultrasonography 2016, 35, 124–130. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Li, D.; Yin, N.; Du, G.; Wang, S.; Xiao, Z.; Chen, J.; Chen, W. A Real-World Study on Diagnosis and Treatment of Uterine Sarcoma in Western China. Int. J. Biol. Sci. 2020, 16, 388–395. [Google Scholar] [CrossRef] [Green Version]
- Najibi, S.; Gilani, M.M.; Zamani, F.; Akhavan, S.; Zamani, N. Comparison of the diagnostic accuracy of contrast-enhanced/DWI MRI and ultrasonography in the differentiation between benign and malignant myometrial tumors. Ann. Med. Surg. 2021, 70, 102813. [Google Scholar] [CrossRef]
- Gaetke-Udager, K.; McLean, K.; Sciallis, A.P.; Alves, T.; Maturen, K.E.; Mervak, B.M.; Moore, A.G.; Wasnik, A.P.; Erba, J.; Davenport, M.S. Diagnostic Accuracy of Ultrasound, Contrast-enhanced CT, and Conventional MRI for Differentiating Leiomyoma from Leiomyosarcoma. Acad. Radiol. 2016, 23, 1290–1297. [Google Scholar] [CrossRef]
- Huang, Y.-T.; Huang, Y.-L.; Ng, K.-K.; Lin, G. Current Status of Magnetic Resonance Imaging in Patients with Malignant Uterine Neoplasms: A Review. J. Radiol. 2019, 20, 18–33. [Google Scholar] [CrossRef]
- Smith, J.; Zawaideh, J.P.; Sahin, H.; Freeman, S.; Bolton, H.; Addley, H.C. Differentiating uterine sarcoma from leiomyoma: BET1T2ER Check! J. Radiol. 2021, 94, 20201332. [Google Scholar] [CrossRef]
- Sousa, F.A.; Ferreira, J.; Cunha, T.M. MR Imaging of uterine sarcomas: A comprehensive review with radiologic-pathologic correlation. Abdom. Radiol. 2021, 46, 5687–5706. [Google Scholar] [CrossRef]
- Thomassin-Naggara, I.; Dechoux, S.; Bonneau, C.; Morel, A.; Rouzier, R.; Carette, M.-F.; Darai, E.; Bazot, M. How to differentiate benign from malignant myometrial tumours using MR imaging. Eur. Radiol. 2013, 23, 2306–2314. [Google Scholar] [CrossRef] [PubMed]
- Bi, Q.; Xiao, Z.; Lv, F.; Liu, Y.; Zou, C.; Shen, Y. Utility of Clinical Parameters and Multiparametric MRI as Predictive Factors for Differentiating Uterine Sarcoma from Atypical Leiomyoma. Acad. Radiol. 2018, 25, 993–1002. [Google Scholar] [CrossRef] [PubMed]
- Takeuchi, M.; Matsuzaki, K.; Harada, M. Clinical utility of susceptibility-weighted MR sequence for the evaluation of uterine sarcomas. Clin. Imaging 2018, 53, 143–150. [Google Scholar] [CrossRef] [PubMed]
- Haacke, E.M.; Mittal, S.; Wu, Z.; Neelavalli, J.; Cheng, Y.-C. Susceptibility-Weighted Imaging: Technical Aspects and Clinical Applications, Part 1. Am. J. Neuroradiol. 2009, 30, 19–30. [Google Scholar] [CrossRef] [Green Version]
- Sumi, A.; Terasaki, H.; Sanada, S.; Uchida, M.; Tomioka, Y.; Kamura, T.; Yano, H.; Abe, T. Assessment of MR Imaging as a Tool to Differentiate between the Major Histological Types of Uterine Sarcomas. Magn. Reson. Med. Sci. 2015, 14, 295–304. [Google Scholar] [CrossRef] [Green Version]
- Malek, M.; Rahmani, M.; Ebrahimi, S.M.S.; Tabibian, E.; Alidoosti, A.; Rahimifar, P.; Akhavan, S.; Gandomkar, Z. 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. Cancer Imaging 2019, 19, 20. [Google Scholar] [CrossRef] [Green Version]
- Sahin, H.; Smith, J.; Zawaideh, J.P.; Shakur, A.; Carmisciano, L.; Caglic, I.; Bruining, A.; Jimenez-Linan, M.; Freeman, S.; Addley, H. Diagnostic interpretation of non-contrast qualitative MR imaging features for charac-terisation of uterine leiomyosarcoma. J. Radiol. 2021, 94, 20210115. [Google Scholar] [CrossRef]
- Li, H.M.; Liu, J.; Qiang, J.W.; Zhang, H.; Zhang, G.F.; Ma, F. Diffusion-Weighted Imaging for Differentiating Uterine Leiomyosarcoma from Degenerated Leiomyoma. J. Comput. Assist. Tomogr. 2017, 41, 599–606. [Google Scholar] [CrossRef]
- Rio, G.; Lima, M.; Gil, R.; Horta, M.; Cunha, T.M. T2 hyperintense myometrial tumors: Can MRI features differentiate leiomyomas from leiomyosarcomas? Abdom. Radiol. 2019, 44, 3388–3397. [Google Scholar] [CrossRef]
- Ando, T.; Kato, H.; Furui, T.; Morishige, K.-I.; Goshima, S.; Matsuo, M. Uterine smooth muscle tumours with hyperintense area on T1 weighted images: Differentiation between leiomyosarcomas and leiomyomas. Br. J. Radiol. 2018, 91, 20170767. [Google Scholar] [CrossRef] [PubMed]
- Saida, T.; Mori, K.; Tanaka, Y.O.; Sakai, M.; Amano, T.; Kikuchi, S.; Masuoka, S.; Yoshida, M.; Masumoto, T.; Satoh, T.; et al. Carcinosarcoma of the ovary: MR and clinical findings compared with high-grade serous carcinoma. Jpn. J. Radiol. 2020, 39, 357–366. [Google Scholar] [CrossRef] [PubMed]
- Li, H.M.; Liu, J.; Qiang, J.W.; Gu, W.Y.; Zhang, G.F.; Ma, F.H. Endometrial Stromal Sarcoma of the Uterus: Magnetic Resonance Imaging Findings Including Apparent Diffusion Coefficient Value and Its Correlation with Ki-67 Expression. Int. J. Gynecol. Cancer 2017, 27, 1877–1887. [Google Scholar] [CrossRef]
- Huang, Y.-L.; Ueng, S.-H.; Chen, K.; Huang, Y.-T.; Lu, H.-Y.; Ng, K.-K.; Chang, T.-C.; Lai, C.-H.; Lin, G. 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 2019, 19, 1–12. [Google Scholar] [CrossRef] [PubMed]
- Bi, Q.; Wu, K.; Lv, F.; Xiao, Z.; Xiong, Y.; Shen, Y. The value of clinical parameters combined with magnetic resonance imaging (MRI) features for preoperatively distinguishing different subtypes of uterine sarcomas: An observational study (STROBE com-pliant). Medicine 2020, 99, e19787. [Google Scholar] [CrossRef]
- Zhang, G.-F.; Zhang, H.; Tian, X.-M.; Zhang, H. Magnetic resonance and diffusion-weighted imaging in categorization of uterine sarcomas: Correlation with pathological findings. Clin. Imaging 2014, 38, 836–844. [Google Scholar] [CrossRef]
- Gerges, L.; Popiolek, D.; Rosenkrantz, A.B. Explorative Investigation of Whole-Lesion Histogram MRI Metrics for Differentiating Uterine Leiomyomas and Leiomyosarcomas. AJR Am. J. Roentgenol. 2018, 210, 1172–1177. [Google Scholar] [CrossRef]
- Rahimifar, P.; Hashemi, H.; Malek, M.; Ebrahimi, S.; Tabibian, E.; Alidoosti, A.; Mousavi, A.; Yarandi, F. Diagnostic value of 3 T MR spectroscopy, diffusion-weighted MRI, and apparent diffusion coefficient value for distinguishing benign from malignant myometrial tumours. Clin. Radiol. 2019, 74, 571.e9–571.e18. [Google Scholar] [CrossRef]
- Lin, G.; Yang, L.-Y.; Huang, Y.-T.; Ng, K.-K.; Ng, S.-H.; Ueng, S.-H.; Chao, A.; Yen, T.-C.; Chang, T.-C.; Lai, C.-H. 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. Reason. Imaging 2016, 43, 333–342. [Google Scholar] [CrossRef]
- Dueholm, M.; Hjorth, I.M.D. Structured imaging technique in the gynecologic office for the diagnosis of abnormal uterine bleeding. Best Pract. Res. Clin. Obstet. Gynaecol. 2017, 40, 23–43. [Google Scholar] [CrossRef]
- Early, H.M.; McGahan, J.P.; Scoutt, L.M.; Revzin, M.; Lamba, R.; Corwin, M.; Fananapazir, G.; Sekhon, S. Pitfalls of Sonographic Imaging of Uterine Leiomyoma. Ultrasound Q. 2016, 32, 164–174. [Google Scholar] [CrossRef] [PubMed]
- Virarkar, M.; Diab, R.; Palmquist, S.; Bassett, J.R.; Bhosale, P. Diagnostic Performance of MRI to Differentiate Uterine Leiomyosarcoma from Benign Leiomyoma: A Meta-Analysis. J. Belg. Soc. Radiol. 2020, 104, 69. [Google Scholar] [CrossRef] [PubMed]
- Nagamatsu, A.; Umesaki, N.; Li, L.; Tanaka, T. Use of 18 F-fluorodeoxyglucose positron emission tomography for diagnosis of uterine sarcomas. Oncol. Rep. 2010, 23, 1069–1076. [Google Scholar] [PubMed] [Green Version]
- Oda, K.; Okada, S.; Nei, T.; Shirai, T.; Takahashi, M.; Sano, Y.; Shiromizu, K. Cytodiagnostic Problems in Uterine Sarcoma. Analysis according to a novel classification of tumor growth types. Acta Cytol. 2004, 48, 181–186. [Google Scholar] [CrossRef] [PubMed]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Camponovo, C.; Neumann, S.; Zosso, L.; Mueller, M.D.; Raio, L. Sonographic and Magnetic Resonance Characteristics of Gynecological Sarcoma. Diagnostics 2023, 13, 1223. https://doi.org/10.3390/diagnostics13071223
Camponovo C, Neumann S, Zosso L, Mueller MD, Raio L. Sonographic and Magnetic Resonance Characteristics of Gynecological Sarcoma. Diagnostics. 2023; 13(7):1223. https://doi.org/10.3390/diagnostics13071223
Chicago/Turabian StyleCamponovo, Carolina, Stephanie Neumann, Livia Zosso, Michael D. Mueller, and Luigi Raio. 2023. "Sonographic and Magnetic Resonance Characteristics of Gynecological Sarcoma" Diagnostics 13, no. 7: 1223. https://doi.org/10.3390/diagnostics13071223