Advances in the Preoperative Identification of Uterine Sarcoma
Abstract
:Simple Summary
Abstract
1. Introduction
Methods
2. Epidemiology and Clinical Manifestations
3. Laboratory Tests
4. Imaging Examinations
4.1. Color Doppler Ultrasonography
4.2. MRI
4.3. CT
4.4. PET-CT
4.5. Machine Learning and Radiomics
5. Preoperative Biopsy and Intraoperative Freezing
6. Integrated Model
7. Molecular Genetic Imaging Techniques
8. Discussion and Conclusions
Author Contributions
Funding
Conflicts of Interest
References
- Amant, F.; Coosemans, A.; Debiec-Rychter, M.; Timmerman, D.; Vergote, I. Clinical management of uterine sarcomas. Lancet Oncol. 2009, 10, 1188–1198. [Google Scholar] [CrossRef]
- Pavone, D.; Clemenza, S.; Sorbi, F.; Fambrini, M.; Petraglia, F. Epidemiology and Risk Factors of Uterine Fibroids. Best Pract. Res. Clin. Obstet. Gynaecol. 2018, 46, 3–11. [Google Scholar] [CrossRef] [PubMed]
- Raine-Bennett, T.; Tucker, L.Y.; Zaritsky, E.; Littell, R.D.; Palen, T.; Neugebauer, R.; Axtell, A.; Schultze, P.; Kronbach, D.; Embry-Schubert, J.; et al. Occult Uterine Sarcoma and Leiomyosarcoma: Incidence of and Survival Associated with Morcellation. Obstet. Gynecol. 2016, 127, 29–39. [Google Scholar] [CrossRef] [PubMed]
- George, S.; Barysauskas, C.; Serrano, C.; Oduyebo, T.; Rauh-Hain, J.A.; Del Carmen, M.G.; Demetri, G.D.; Muto, M.G. Retrospective cohort study evaluating the impact of intraperitoneal morcellation on outcomes of localized uterine leiomyosarcoma. Cancer 2014, 120, 3154–3158. [Google Scholar] [CrossRef] [PubMed]
- Yorgancı, A.; Meydanlı, M.M.; Kadıoğlu, N.; Taşkın, S.; Kayıkçıoğlu, F.; Altın, D.; Atasoy, L.; Haberal, A.N.; Kınay, T.; Akgül, M.A.; et al. Incidence and outcome of occult uterine sarcoma: A multi-centre study of 18604 operations performed for presumed uterine leiomyoma. J. Gynecol. Obstet. Hum. Reprod. 2020, 49, 101631. [Google Scholar] [CrossRef]
- Mallmann, P. Uterine Sarcoma-Difficult to Diagnose, Hard to Treat. Oncol. Res. Treat. 2018, 41, 674. [Google Scholar] [CrossRef]
- Prat, J. FIGO staging for uterine sarcomas. Int. J. Gynaecol. Obstet. 2009, 104, 177–178. [Google Scholar] [CrossRef]
- Brooks, S.E.; Zhan, M.; Cote, T.; Baquet, C.R. Surveillance, epidemiology, and end results analysis of 2677 cases of uterine sarcoma 1989–1999. Gynecol. Oncol. 2004, 93, 204–208. [Google Scholar] [CrossRef]
- Robinson, E.; Neugut, A.I.; Wylie, P. Clinical aspects of postirradiation sarcomas. J. Natl. Cancer Inst. 1988, 80, 233–240. [Google Scholar] [CrossRef]
- Botsis, D.; Koliopoulos, C.; Kondi-Pafitis, A.; Creatsas, G. Myxoid leiomyosarcoma of the uterus in a patient receiving tamoxifen therapy: A case report. Int. J. Gynecol. Pathol. 2006, 25, 173–175. [Google Scholar] [CrossRef]
- 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] [PubMed]
- Arend, R.; Doneza, J.A.; Wright, J.D. Uterine carcinosarcoma. Curr. Opin. Oncol. 2011, 23, 531–536. [Google Scholar] [CrossRef] [PubMed]
- Conklin, C.M.; Longacre, T.A. Endometrial stromal tumors: The new WHO classification. Adv. Anat. Pathol. 2014, 21, 383–393. [Google Scholar] [CrossRef]
- Lee, C.H.; Mariño-Enriquez, A.; Ou, W.; Zhu, M.; Ali, R.H.; Chiang, S.; Amant, F.; Gilks, C.B.; van de Rijn, M.; Oliva, E.; et al. The clinicopathologic features of YWHAE-FAM22 endometrial stromal sarcomas: A histologically high-grade and clinically aggressive tumor. Am. J. Surg. Pathol. 2012, 36, 641–653. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Cotzia, P.; Benayed, R.; Mullaney, K.; Oliva, E.; Felix, A.; Ferreira, J.; Soslow, R.A.; Antonescu, C.R.; Ladanyi, M.; Chiang, S. Undifferentiated Uterine Sarcomas Represent Under-Recognized High-grade Endometrial Stromal Sarcomas. Am. J. Surg. Pathol. 2019, 43, 662–669. [Google Scholar] [CrossRef]
- Nucci, M.R. Practical issues related to uterine pathology: Endometrial stromal tumors. Mod. Pathol. 2016, 29 (Suppl. 1), S92–S103. [Google Scholar] [CrossRef] [Green Version]
- Nathenson, M.J.; Ravi, V.; Fleming, N.; Wang, W.L.; Conley, A. Uterine Adenosarcoma: A Review. Curr. Oncol. Rep. 2016, 18, 68. [Google Scholar] [CrossRef]
- Juang, C.M.; Yen, M.S.; Horng, H.C.; Twu, N.F.; Yu, H.C.; Hsu, W.L. Potential role of preoperative serum CA125 for the differential diagnosis between uterine leiomyoma and uterine leiomyosarcoma. Eur. J. Gynaecol. Oncol. 2006, 27, 370–374. [Google Scholar]
- Yilmaz, N.; Sahin, I.; Kilic, S.; Özgü, E.; Gungor, T.; Bilge, U. Assessment of the predictivity of preoperative serum CA 125 in the differential diagnosis of uterine leiomyoma and uterine sarcoma in the Turkish female population. Eur. J. Gynaecol. Oncol. 2009, 30, 412–414. [Google Scholar]
- 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]
- Zhang, G.; Yu, X.; Zhu, L.; Fan, Q.; Shi, H.; Lang, J. Preoperative clinical characteristics scoring system for differentiating uterine leiomyosarcoma from fibroid. BMC Cancer 2020, 20, 514. [Google Scholar] [CrossRef] [PubMed]
- Goto, A.; Takeuchi, S.; Sugimura, K.; Maruo, T. Usefulness of Gd-DTPA contrast-enhanced dynamic MRI and serum determination 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]
- Mollo, A.; Raffone, A.; Travaglino, A.; Di Cello, A.; Saccone, G.; Zullo, F.; De Placido, G. Increased LDH5/LDH1 ratio in preoperative diagnosis of uterine sarcoma with inconclusive MRI and LDH total activity but suggestive CT scan: A case report. BMC Womens Health 2018, 18, 169. [Google Scholar] [CrossRef]
- Di Cello, A.; Borelli, M.; Marra, M.L.; Franzon, M.; D’Alessandro, P.; Di Carlo, C.; Venturella, R.; Zullo, F. A more accurate method to interpret lactate dehydrogenase (LDH) isoenzymes’ results in patients with uterine masses. Eur. J. Obstet. Gynecol. Reprod. Biol. 2019, 236, 143–147. [Google Scholar] [CrossRef] [PubMed]
- Spivack, L.E.; Glantz, J.C.; Lennon, C.; Bhagavath, B. Specificity of the lactate dehydrogenase isoenzyme index as a preoperative screen for uterine sarcoma before myomectomy. Fertil. Steril. 2021, 115, 174–179. [Google Scholar] [CrossRef]
- Umesaki, N.; Nagamatsu, A.; Li, L.; Tanaka, T. Use of 18F-fluorodeoxyglucose positron emission tomography for diagnosis of uterine sarcomas. Oncol. Rep. 2010, 23, 1069–1076. [Google Scholar] [CrossRef] [Green Version]
- Kusunoki, S.; Terao, Y.; Ujihira, T.; Fujino, K.; Kaneda, H.; Kimura, M.; Ota, T.; Takeda, S. Efficacy of PET/CT to exclude leiomyoma in patients with lesions suspicious for uterine sarcoma on MRI. Taiwan. J. Obstet. Gynecol. 2017, 56, 508–513. [Google Scholar] [CrossRef]
- Kim, H.; Han, K.; Chung, H.; Kim, J.; Park, N.; Song, Y.; Kang, S. Neutrophil to lymphocyte ratio for preoperative diagnosis of uterine sarcomas: A case-matched comparison. Eur. J. Surg. Oncol. 2010, 36, 691–698. [Google Scholar] [CrossRef]
- 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. 2016, 42, 313–318. [Google Scholar] [CrossRef]
- Trovik, J.; Salvesen, H.B.; Cuppens, T.; Amant, F.; Staff, A.C. Growth differentiation factor-15 as biomarker in uterine sarcomas. Int. J. Gynecol. Cancer 2014, 24, 252–259. [Google Scholar] [CrossRef]
- Maeno, M.; Mizutani, T.; Tsuyoshi, H.; Yamada, S.; Ishikane, S.; Kawabe, S.; Nishimura, K.; Yamada, M.; Miyamoto, K.; Yoshida, Y. Development of a novel and rapid measurement system for growth differentiation factor-15, progranulin, and osteopontin in uterine sarcoma. Endocr. J. 2020, 67, 91–94. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Yokoi, A.; Matsuzaki, J.; Yamamoto, Y.; Tate, K.; Yoneoka, Y.; Shimizu, H.; Uehara, T.; Ishikawa, M.; Takizawa, S.; Aoki, Y.; et al. Serum microRNA profile enables preoperative diagnosis of uterine leiomyosarcoma. Cancer Sci. 2019, 110, 3718–3726. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- De Bruyn, C.; Baert, T.; Van den Bosch, T.; Coosemans, A. Circulating Transcripts and Biomarkers in Uterine Tumors: Is There a Predictive Role? Curr. Oncol. Rep. 2020, 22, 12. [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. 2019, 45, 189–194. [Google Scholar] [CrossRef] [Green Version]
- Halaska, M.J.; Haidopoulos, D.; Guyon, F.; Morice, P.; Zapardiel, I.; Kesic, V.; Council, E.S.G.O. European Society of Gynecological Oncology Statement on Fibroid and Uterine Morcellation. Int. J. Gynecol. Cancer 2017, 27, 189–192. [Google Scholar] [CrossRef]
- Chen, I.; Firth, B.; Hopkins, L.; Bougie, O.; Xie, R.-H.; Singh, S. Clinical Characteristics Differentiating Uterine Sarcoma and Fibroids. JSLS J. Soc. Laparoendosc. Surg. 2018, 22, e2017.00066. [Google Scholar] [CrossRef] [Green Version]
- Kurjak, A.; Kupesic, S.; Shalan, H.; Jukic, S.; Kosuta, D.; Ilijas, M. Uterine sarcoma: A report of 10 cases studied by transvaginal color and pulsed Doppler sonography. Gynecol. Oncol. 1995, 59, 342–346. [Google Scholar] [CrossRef]
- Aviram, R.; Ochshorn, Y.; Markovitch, O.; Fishman, A.; Cohen, I.; Altaras, M.M.; Tepper, R. Uterine sarcomas versus leiomyomas: Gray-scale and Doppler sonographic findings. J. Clin. Ultrasound 2005, 33, 10–13. [Google Scholar] [CrossRef]
- Szabó, I.; Szánthó, A.; Csabay, L.; Csapó, Z.; Szirmai, K.; Papp, Z. Color Doppler ultrasonography in the differentiation of uterine sarcomas from uterine leiomyomas. Eur. J. Gynaecol. Oncol. 2002, 23, 29–34. [Google Scholar]
- 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]
- Exacoustos, C.; Romanini, M.E.; Amadio, A.; Amoroso, C.; Szabolcs, B.; Zupi, E.; Arduini, D. Can gray-scale and color Doppler sonography differentiate between uterine leiomyosarcoma and leiomyoma? J. Clin. Ultrasound 2007, 35, 449–457. [Google Scholar] [CrossRef] [PubMed]
- 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] [PubMed]
- Chiappa, V.; Interlenghi, M.; Salvatore, C.; Bertolina, F.; Bogani, G.; Ditto, A.; Martinelli, F.; Castiglioni, I.; Raspagliesi, F. Using rADioMIcs and machine learning with ultrasonography for the differential diagnosis of myometRiAL tumors (the ADMIRAL pilot study). Radiomics and differential diagnosis of myometrial tumors. Gynecol. Oncol. 2021, 161, 838–844. [Google Scholar] [CrossRef] [PubMed]
- 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]
- Tanaka, Y.O.; Nishida, M.; Tsunoda, H.; Okamoto, Y.; Yoshikawa, H. Smooth muscle tumors of uncertain malignant potential and leiomyosarcomas of the uterus: MR findings. J. Magn. Reson. Imaging 2004, 20, 998–1007. [Google Scholar] [CrossRef]
- 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]
- Ando, T.; Kato, H.; Furui, T.; Morishige, K.-I.; Goshima, S.; Matsuo, M. Uterine smooth muscle tumours with hyperintense area on T(1) weighted images: Differentiation between leiomyosarcomas and leiomyomas. Br. J. Radiol. 2018, 91, 20170767. [Google Scholar] [CrossRef]
- Sahdev, A.; Sohaib, S.A.; Jacobs, I.; Shepherd, J.H.; Oram, D.H.; Reznek, R.H. MR imaging of uterine sarcomas. AJR Am. J. Roentgenol. 2001, 177, 1307–1311. [Google Scholar] [CrossRef]
- Kim, T.H.; Kim, J.W.; Kim, S.Y.; Kim, S.H.; Cho, J.Y. What MRI Features Suspect Malig. pure mesenchymal uterine tumors rather than uterine leiomyoma with cystic degeneration? J. Gynecol. Oncol. 2018, 29, 1093896. [Google Scholar] [CrossRef] [Green Version]
- Santos, P.; Cunha, T.M. Uterine sarcomas: Clinical presentation and MRI features. Diagn. Interv. Radiol. 2015, 21, 4–9. [Google Scholar] [CrossRef] [Green Version]
- 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]
- Ueda, H.; Togashi, K.; Konishi, I.; Kataoka, M.L.; Koyama, T.; Fujiwara, T.; Kobayashi, H.; Fujii, S.; Konishi, J. Unusual appearances of uterine leiomyomas: MR imaging findings and their histopathologic backgrounds. Radiographics 1999, 19, S131–S145. [Google Scholar] [CrossRef] [PubMed]
- Namimoto, T.; Yamashita, Y.; Awai, K.; Nakaura, T.; Yanaga, Y.; Hirai, T.; Saito, T.; Katabuchi, H. Combined use of T2-weighted and diffusion-weighted 3-T MR imaging for differentiating uterine sarcomas from benign leiomyomas. Eur. Radiol. 2009, 19, 2756–2764. [Google Scholar] [CrossRef] [PubMed]
- 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]
- 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]
- Sato, K.; Yuasa, N.; Fujita, M.; Fukushima, Y. Clinical application of diffusion-weighted imaging for preoperative differentiation between uterine leiomyoma and leiomyosarcoma. Am. J. Obstet. Gynecol. 2014, 210, 368.e1–368.e8. [Google Scholar] [CrossRef]
- Malek, M.; Tabibian, E.; Dehgolan, M.R.; Rahmani, M.; Akhavan, S.; Hasani, S.S.; Nili, F.; Hashemi, H. A Diagnostic Algorithm using Multi-parametric MRI to Differentiate Benign from Malignant Myometrial Tumors: Machine-Learning Method. Sci. Rep. 2020, 10, 7404. [Google Scholar] [CrossRef]
- Wahab, C.A.; Jannot, A.-S.; Bonaffini, P.A.; Bourillon, C.; Cornou, C.; Lefrère-Belda, M.-A.; Bats, A.-S.; Thomassin-Naggara, I.; Bellucci, A.; Reinhold, C.; et al. Diagnostic Algorithm to Differentiate Benign Atypical Leiomyomas from Malignant Uterine Sarcomas with Diffusion-weighted MRI. Radiology 2020, 297, 361–371. [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. Reson. Imaging 2016, 43, 333–342. [Google Scholar] [CrossRef]
- Smith, J.; Zawaideh, J.P.; Sahin, H.; Freeman, S.; Bolton, H.; Addley, H.C. Differentiating uterine sarcoma from leiomyoma: BET(1)T(2)ER Check! Br. J. Radiol. 2021, 94, 20201332. [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]
- DeMulder, D.; Ascher, S.M. Uterine Leiomyosarcoma: Can MRI Differentiate Leiomyosarcoma from Benign Leiomyoma before Treatment? AJR Am. J. Roentgenol. 2018, 211, 1405–1415. [Google Scholar] [CrossRef] [PubMed]
- Malek, M.; Gity, M.; Alidoosti, A.; Oghabian, Z.; Rahimifar, P.; Ebrahimi, S.M.S.; Tabibian, E.; Oghabian, M.A. A machine learning approach for distinguishing uterine sarcoma from leiomyomas based on perfusion weighted MRI parameters. Eur. J. Radiol. 2019, 110, 203–211. [Google Scholar] [CrossRef] [PubMed]
- Faghihi, R.; Zeinali-Rafsanjani, B.; Mosleh-Shirazi, M.-A.; Saeedi-Moghadam, M.; Lotfi, M.; Jalli, R.; Iravani, V. Magnetic Resonance Spectroscopy and its Clinical Applications: A Review. J. Med. Imaging Radiat. Sci. 2017, 48, 233–253. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- 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]
- Takeuchi, M.; Matsuzaki, K.; Harada, M. Preliminary observations and clinical value of lipid peak in high-grade uterine sarcomas using in vivo proton MR spectroscopy. Eur. Radiol. 2013, 23, 2358–2363. [Google Scholar] [CrossRef]
- Takeuchi, M.; Matsuzaki, K.; Harada, M. Clinical utility of susceptibility-weighted MR sequence for the evaluation of uterine sarcomas. Clin. Imaging 2019, 53, 143–150. [Google Scholar] [CrossRef]
- Zeng, C.; Du, S.; Han, Y.; Fu, J.; Luo, Q.; Xiang, Y.; Chen, X.; Luo, T.; Li, Y.; Zheng, Y. Optic radiations are thinner and show signs of iron deposition in patients with long-standing remitting-relapsing multiple sclerosis: An enhanced T(2)(*)-weighted angiography imaging study. Eur. Radiol. 2018, 28, 4447–4454. [Google Scholar] [CrossRef]
- Tian, S.; Liu, A.; Chen, A.; Niu, M.; Wei, Q. Differential Diagnosis of Uterine Sarcoma and Degenerative Hysteromyoma by Using Multiple Quantitative Parameters of Enhanced T2 Star Weighted Angiography. Chin. J. Med. Imaging 2020, 28, 108–111. [Google Scholar]
- Ju, Y.; Nie, J.; Tian, S.; Liu, A.; Chen, L.; Wei, Q. The value of diffusional kurtosis imaging in differentiating uterine sarcoma from degenerative hysteromyoma. Chin. J. Magn. Reson. Imaging 2021, 12, 61–65. [Google Scholar]
- Yu, L.; Wang, G.; Li, Z. The CT and MRI Imaging Features of Endometrial Stromal Sarcomas. J. Clin. Radiol. 2020, 39, 715–719. [Google Scholar]
- Bang, J.; Kang, S. Diagnostic performance of F-18-FDG PET or PET/CT in differential diagnosis of uterine leiomyomas and uterine sarcomas: Systematic review and meta-analysis of the literature. Clin. Transl. Imaging 2022, 10, 301–309. [Google Scholar] [CrossRef]
- Tsujikawa, T.; Yamamoto, M.; Shono, K.; Yamada, S.; Tsuyoshi, H.; Kiyono, Y.; Kimura, H.; Okazawa, H.; Yoshida, Y. Assessment of intratumor heterogeneity in mesenchymal uterine tumor by an 18F-FDG PET/CT texture analysis. Ann. Nucl. Med. 2017, 31, 752–757. [Google Scholar] [CrossRef]
- Yamane, T.; Takaoka, A.; Kita, M.; Imai, Y.; Senda, M. 18F-FLT PET performs better than 18F-FDG PET in differentiating malignant uterine corpus tumors from benign leiomyoma. Ann. Nucl. Med. 2012, 26, 478–484. [Google Scholar] [CrossRef] [PubMed]
- Nakagawa, M.; Nakaura, T.; Namimoto, T.; Iyama, Y.; Kidoh, M.; Hirata, K.; Nagayama, Y.; Oda, S.; Sakamoto, F.; Shiraishi, S.; et al. A multiparametric MRI-based machine learning to distinguish between uterine sarcoma and benign leiomyoma: Comparison with (18)F-FDG PET/CT. Clin. Radiol. 2019, 74, 167.e1–167.e7. [Google Scholar] [CrossRef] [PubMed]
- Nakagawa, M.; Nakaura, T.; Namimoto, T.; Iyama, Y.; Kidoh, M.; Hirata, K.; Nagayama, Y.; Yuki, H.; Oda, S.; Utsunomiya, D.; et al. Machine Learning to Differentiate T2-Weighted Hyperintense Uterine Leiomyomas from Uterine Sarcomas by Utilizing Multiparametric Magnetic Resonance Quantitative Imaging Features. Acad. Radiol. 2019, 26, 1390–1399. [Google Scholar] [CrossRef]
- Wang, T.; Gong, J.; Li, Q.; Chu, C.; Shen, W.; Peng, W.; Gu, Y.; Li, W. A combined radiomics and clinical variables model for prediction of malignancy in T2 hyperintense uterine mesenchymal tumors on MRI. Eur. Radiol. 2021, 31, 6125–6135. [Google Scholar] [CrossRef]
- Xie, H.; Hu, J.; Zhang, X.; Ma, S.; Liu, Y.; Wang, X. Preliminary utilization of radiomics in differentiating uterine sarcoma from atypical leiomyoma: Comparison on diagnostic efficacy of MRI features and radiomic features. Eur. J. Radiol. 2019, 115, 39–45. [Google Scholar] [CrossRef]
- Niu, M.; Liu, A.; Zhang, Q. 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. Clin. Radiol. 2019, 38, 1895–1899. [Google Scholar]
- Peters, A.; Sadecky, A.M.; Winger, D.G.; Guido, R.S.; Lee, T.T.; Mansuria, S.M.; Donnellan, N.M. Characterization and Preoperative Risk Analysis of Leiomyosarcomas at a High-Volume Tertiary Care Center. Int. J. Gynecol. Cancer 2017, 27, 1183–1190. [Google Scholar] [CrossRef]
- 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. Radiol. 2017, 42, 1762–1772. [Google Scholar] [CrossRef] [PubMed]
- Tamura, R.; Kashima, K.; Asatani, M.; Nishino, K.; Nishikawa, N.; Sekine, M.; Serikawa, T.; Enomoto, T. Preoperative ultrasound-guided needle biopsy of 63 uterine tumors having high signal intensity upon T2-weighted magnetic resonance imaging. Int. J. Gynecol. Cancer 2014, 24, 1042–1047. [Google Scholar] [CrossRef] [PubMed]
- Mattos, L.C.; Alboni, C.; Malmusi, S.; Galassi, M.C.; Facchinetti, F.; Mabrouk, M. Ultrasound-guided Needle Biopsy for Preoperative Assessment of Uterine Fibroids: Our Experience and a Review of the Literature. Gynecol. Minim. Invasive Ther. 2022, 11, 47–50. [Google Scholar] [CrossRef] [PubMed]
- Petousis, S.; Croce, S.; Kind, M.; Margioula-Siarkou, C.; Babin, G.; Lalet, C.; Querleu, D.; Floquet, A.; Pulido, M.; Guyon, F. BIOPSAR study: Ultrasound-guided pre-operative biopsy to assess histology of sarcoma-suspicious uterine tumors: A new study protocol. Int. J. Gynecol. Cancer 2021, 31, 1476–1480. [Google Scholar] [CrossRef]
- Yang, X.; Stamp, M. Computer-aided diagnosis of low grade endometrial stromal sarcoma (LGESS). Comput. Biol. Med. 2021, 138, 104874. [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, 678. [Google Scholar] [CrossRef] [Green Version]
- Nagai, T.; Takai, Y.; Akahori, T.; Ishida, H.; Hanaoka, T.; Uotani, T.; Sato, S.; Matsunaga, S.; Baba, K.; Seki, H. Highly improved accuracy of the revised PREoperative sarcoma score (rPRESS) in the decision of performing surgery for patients presenting with a uterine mass. Springerplus 2015, 4, 520. [Google Scholar] [CrossRef] [Green Version]
- 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]
- Condic, M.; Egger, E.K.; Hohenberger, P.; Staerk, C.; Mayr, A.; Armbrust, R.; Roser, E.; Mustea, A.; Sehouli, J. Clinical value of pre-operative scoring systems to predict leiomyosarcoma: Results of a validation study in 177 patients from the NOGGO-REGSA Registry. Int. J. Gynecol. Cancer 2022, 32, 619–625. [Google Scholar] [CrossRef]
- Lentz, S.E.; Zaritsky, E.; Tucker, L.-Y.; Lee, C.; Lazo, I.M.; Niihara, A.; Yamamoto, M.; Raine-Bennett, T. Prediction of Occult Uterine Sarcoma before Hysterectomy for Women with Leiomyoma or Abnormal Bleeding. J. Minim. Invasive Gynecol. 2020, 27, 930–937.e1. [Google Scholar] [CrossRef]
- Chantasartrassamee, P.; Kongsawatvorakul, C.; Rermluk, N.; Charakorn, C.; Wattanayingcharoenchai, R.; Lertkhachonsuk, A.-A. Preoperative clinical characteristics between uterine sarcoma and leiomyoma in patients with uterine mass, a case-control study. Eur. J. Obstet. Gynecol. Reprod. Biol. 2022, 270, 176–180. [Google Scholar] [CrossRef] [PubMed]
- Shalaby, S.; Khater, M.; Laknaur, A.; Arbab, A.; Al-Hendy, A. Molecular Bio-Imaging Probe for Non-Invasive Differentiation between Human Leiomyoma Versus Leiomyosarcoma. Reprod. Sci. 2020, 27, 644–654. [Google Scholar] [CrossRef] [PubMed]
- Benson, C.; Miah, A.B. Uterine sarcoma-current perspectives. Int. J. Womens Health 2017, 9, 597–606. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Zhang, L.; Huang, J.; Liu, L. Improved Deep Learning Network Based in combination with Cost-sensitive Learning for Early Detection of Ovarian Cancer in Color Ultrasound Detecting System. J. Med. Syst. 2019, 43, 251. [Google Scholar] [CrossRef] [PubMed]
- Pergialiotis, V.; Pouliakis, A.; Parthenis, C.; Damaskou, V.; Chrelias, C.; Papantoniou, N.; Panayiotides, I. The utility of artificial neural networks and classification and regression trees for the prediction of endometrial cancer in postmenopausal women. Public Health 2018, 164, 1–6. [Google Scholar] [CrossRef] [PubMed]
Marker | Author | Year | Tumor Type (N) | Controls (N) | Results/Conclusions |
---|---|---|---|---|---|
CA125 | Juang [18] | 2006 | LMS(42) | UM (84) | Preoperative serum CA125 had a potential role in the differential diagnosis between early stage and advanced-stage uterine leiomyosarcoma. |
Yilmaz [19] | 2009 | USM * (26); | UM (2382) | In the differential diagnosis of myoma and uterine sarcoma, the preoperative serum CA 125 level did not have any predictivity. | |
LDH | Song [20] | 2018 | USM (50) | UM (26) | The positivity rates for LDH-A and LDH-D were significantly higher in patients with uterine sarcoma compared with those with uterine myoma. |
Zhang [21] | 2020 | LMS (45) | UM (180) | LDH ≥ 193 U/L was independent predictors of LMS. | |
Goto [22] | 2002 | LMS (10) | DLM (130) | The combined use of dynamic MRI and serum measurement of LDH seems to be useful in making a differentiated diagnosis of LMS from DLM. | |
Di Cello [24] | 2019 | USM (43) | UM (2211) | UMG, the accuracy of markers in discriminating between benign and suspicious malignant uterine masses was significantly enhanced, sensitivity at 100% and specificity at 99.6%. | |
Spivack [25] | 2021 | Null | UM (179) | Specificity of UMG index to exclude uterine sarcoma was 91.1% (163/179) and higher in non-obese (BMI < 30; 95.1%) than obese women (85.5%). | |
Nagamatsu [26] | 2010 | USM * (10) | UM (24) | The diagnostic accuracy of FDG-PET combined with serum LDH was 100%. | |
Kusunoki [27] | 2017 | USC (15) | UM (19) | PET/CT and LDH levels had a sensitivity of 86.6%, specificity of 100%, positive predictive value of 100%, and an NPV of 90.4%. | |
NLR | Zhang [21] | 2020 | LMS (45) | UM (180) | NLR ≥ 2.8 were independent predictors of LMS. |
Kim [28] | 2010 | USM * (55) | UM (330) | NLR was more powerful for the preoperative diagnosis of uterine sarcomas than serum CA-125 levels. | |
Cho [29] | 2016 | USM(31) | UM (93) | NLR > 2.1 was independent risk factors for uterine sarcoma. | |
GDF-15 | Trovik [30] | 2014 | USM (19) | UM (50) | The median circulating GDF-15 concentration was elevated in the uterine sarcoma group (943 ng/L) compared with the myoma uteri group (647 ng/L). |
MicroRNA | Yokoi [32] | 2019 | USM (10) | UM (18) | The optimal model consisted of two miRNAs (miR-1246 and miR-191-5p), with an area under the receiver operating characteristic curve (AUC) for identifying LMS of 0.97. |
CRP+D-dimer | Nishigaya [34] | 2019 | USM (36) | UM (97) | When LDH, D-dimer, and CRP were all positive, specificity and positive predictive value were 100% in differentiating leiomyosarcoma from uterine myoma. |
MRI | Author | Year | Tumor Type (N) | Controls (N) | Results/Conclusions |
---|---|---|---|---|---|
T1 | Tanaka [45] | 2004 | LMS (9)/ SMTUMP (3) | UM (12) | It was found that 9 of the 12 nonbenign characters had more than 50% of high-intensity areas on T2-weighted images (T2WI), and some hyperintense foci on T1-weighted images (T1WI). |
Malek [46] | 2019 | USM (21) | UM (84) | Intensity at T1-weighted sequences exhibited no significant difference between USM and UM (p = 0.201). | |
Ando [47] | 2018 | LMS (14) | LM (1118) | T1 HIA within LM showed more homogeneity, better demarcation, smaller occupying rate, and higher signal intensity than T1 HIA within LMS. | |
T2 | Sahdev [48] | 2001 | USM (22) | \ | On T2WI, the masses were characteristically of low or intermediate background signal intensity with pockets of very high T2 signal. The areas of high T2 signal corresponded to cystic necrosis in the tumor. |
Kim [49] | 2018 | ESS (18) LMS (15) | LCD (30) | ESS or LMS more frequently showed high T2 SI compared with LCD (OR = 4.396; p = 0.046). | |
Malek [46] | 2019 | USM (21) | UM (84) | T2-scaled ratio, tumor myometrium contrast ratio on T2 and tumor myometrium contrast-enhanced ratio achieved a sensitivity of 100% in predicting USM. | |
Namimoto [53] | 2009 | USM (8) | UM (95) | A combination of ADC and TCR achieved a significant improvement without any overlap between sarcomas and leiomyomas (sensitivity 100%, specificity 100%). | |
DWI&ADC | Thomassin-Naggara [54] | 2013 | USM (25) | UM (26) | The significant criteria for prediction of malignancy were high DWI signal intensity (OR = +∞), intermediate T2-weighted signal intensity (OR = +∞), mean ADC (OR = 25.1). |
Li [55] | 2017 | LMS (16) | DLM (26) | The mean ADC value in LMS was significantly lower than that in DLMs (p < 0.001). | |
Sato [56] | 2014 | LMS (10) | UM (83) | The LMS were readily apparent via DWI, presenting as an intermediate- to high-intensity area in the uterine wall. All low-intensity areas presented as leiomyoma nodules. | |
Wahab [58] | 2020 | USM (51) | UM (105) | Predictive MRI criteria for malignancy were enlarged lymph nodes or peritoneal implants, high DWI signal greater than that in the endometrium, and ADC less than or equal to 0.905 × 10−3 mm2/s. | |
CE-MRI | Goto [22] | 2002 | LMS (10) | DLM (130) | The contrast enhancement at 60s after administration of Gd-DTPA was detected in all LMS but absent in 28 of 32 DLM patients. |
Lin [59] | 2016 | LMS/SMTUMP (8) | UM (25) | For prospective differentiation between uterine LMS/STUMP and benign leiomyoma, CE-MRI can provide accurate information and is preferable to DWI. A combination of DWI and ADC values can achieve comparable diagnostic accuracy to CE-MRI. | |
Multi-MRI | Lakhman [61] | 2017 | LMS (10) | ALM (14) | Four qualitative MR features most strongly associated with LMS were nodular borders, hemorrhage, “T2 dark” area(s), and central unenhanced area(s). |
PWI | Malek [63] | 2019 | USM (10) | UM (50) | When 21 features extracted from ROIs were fed into the classifier an accuracy of 91.7%, sensitivity of 100%, and specificity of 90% were achieved in the optimal operating point of the classifier. |
MRS | Rahimifar [65] | 2019 | USM (21) | UM (84) | The percentage of malignant lesions for which choline and lipid peaks were present was significantly higher than that of benign lesions. By combining the ADC and MRS findings, an accuracy of 98.3 (95.1–100) was achieved. |
Takeuchi [66] | 2013 | USM (12) | UM (26) | The presence of a high lipid peak for the diagnosis of sarcoma had a sensitivity of 100%, specificity of 96%, positive predictive value of 92% and negative predictive value of 100%. | |
SWS | Takeuchi [67] | 2019 | USM (10) | UM (24) | The accuracy, sensitivity, and specificity for SWS were 97%, 100%, and 96%, respectively. |
ESWAN | Tian [69] | 2020 | USM (17) | DLM (33) | The AUC values of phase, R2* and T2* in USM group were 0.854, 0.900 and 0.961, respectively. |
DKI | Ju [70] | 2021 | USM (13) | DLM (26) | The AUC values of MK, Ka, Kr, FA, MD, Da and Dr were 0.93, 0.99, 0.80, 0.73, 0.94, 0.97 and 0.90. The diagnostic threshold of the parameters were as follows: MK ≥ 0.80, Ka ≥ 0.73, Kr ≥ 0.75, FA ≥ 0.22, MD ≤ 1.47, Da ≤ 1.95, Dr ≤ 1.23. |
Methods | Author | Year | Tumor Type(N) | Controls (N) | Results/Conclusions |
---|---|---|---|---|---|
machine learning | Nakagawa [75] | 2018 | USM (11) | UM (56) | The AUCs of the univariate models using MRI parameters (0.68-0.8) were inferior to that of the maximum standardized uptake value (SUVmax) of PET (0.85); however, the AUC of the multivariate LR model (0.92) was superior to that of SUVmax, and comparable to that of the board-certified radiologists (0.97 and 0.89). |
Malek [57] | 2020 | USM (21) | UM (84) | The simple decision tree and a complex one were proposed using the most accurate models. Our final simple decision tree obtained accuracy = 96.2%, sensitivity = 100% and specificity = 95%, while the complex tree yielded accuracy, sensitivity and specificity of 100%. | |
Radiomics | Lakhman [61] | 2017 | LMS (10) | ALM (14) | Sixteen texture features differed significantly between LMS and ALM (p-values: < 0.001–0.036). Unsupervised clustering achieved accuracy of 0.75 (sensitivity: 0.70; specificity: 0.79). |
Nakagawa [76] | 2019 | USM (30) | UM (50) | The AUC for the eXtreme Gradient Boosting was significantly higher than those for both radiologists (0.93 vs. 0.80 and 0.68, p = 0.03 and p < 0.001, respectively) in the differentiation of uterine sarcomas from leiomyomas with high signal intensity on T2WI. | |
Wang [77] | 2021 | USM (53) | UM (81) | Comparing with the T2WI-based radiomics model (AUC: 0.76 ± 0.09) and the clinical model (AUC: 0.79 ± 0.09), the combined model significantly improved the AUC value to 0.91 ± 0.05 (p < 0.05). The clinical-radiomics combined model yielded equivalent or higher performance than two radiologists (AUC: 0.78 vs. 0.91, p = 0.03; 0.90 vs.0.91, p = 0.13). | |
Xie [78] | 2019 | USM (29) | UM (49) | Diagnosis efficacy of radiologists based on MRI reached an AUC of 0.752, sensitivity of 58.6%, specificity of 91.8%, and accuracy of 79.5%. The optimal radiomic model reached an AUC of 0.830, sensitivity of 76.0%, average specificity of 73.2%, and accuracy of 73.9%. | |
Texture analysis | Niu [79] | 2019 | USM (16) | DUF (31) | The maximum, mean, standard deviation, 50th, 75th, 90th, 95th, skewness and entropy of USM were less than DUF. The energy value and consistency were greater than DUF, and the differences were statistically significant (p < 0.05). The area under the curve (AUC) of the entropy value is the largest, and the diagnostic efficiency is the best (AUC = 0.94). |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 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
Liu, J.; Wang, Z. Advances in the Preoperative Identification of Uterine Sarcoma. Cancers 2022, 14, 3517. https://doi.org/10.3390/cancers14143517
Liu J, Wang Z. Advances in the Preoperative Identification of Uterine Sarcoma. Cancers. 2022; 14(14):3517. https://doi.org/10.3390/cancers14143517
Chicago/Turabian StyleLiu, Junxiu, and Zijie Wang. 2022. "Advances in the Preoperative Identification of Uterine Sarcoma" Cancers 14, no. 14: 3517. https://doi.org/10.3390/cancers14143517