Korean Journal of Radiology

The Korean Society of Radiology (대한영상의학회)
권호 표지
DOI QR코드

DOI QR Code

Differentiating Uterine Sarcoma From Atypical Leiomyoma on Preoperative Magnetic Resonance Imaging Using Logistic Regression Classifier: Added Value of Diffusion-Weighted Imaging-Based Quantitative Parameters

  • Hokun Kim (Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea) ;
  • Sung Eun Rha (Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea) ;
  • Yu Ri Shin (Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea) ;
  • Eu Hyun Kim (Department of Radiology, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea) ;
  • Soo Youn Park (Department of Radiology, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea) ;
  • Su-Lim Lee (Department of Radiology, Uijeongbu St. Mary's Hospital, College of Medicine, The Catholic University of Korea) ;
  • Ahwon Lee (Department of Hospital Pathology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea) ;
  • Mee-Ran Kim (Department of Obstetrics and Gynecology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea)
  • Received : 2023.08.19
  • Accepted : 2023.10.24
  • Published : 2024.01.01
Download PDF
⟨ Previous Next ⟩

Abstract

Objective: To evaluate the added value of diffusion-weighted imaging (DWI)-based quantitative parameters to distinguish uterine sarcomas from atypical leiomyomas on preoperative magnetic resonance imaging (MRI). Materials and Methods: A total of 138 patients (age, 43.7 ± 10.3 years) with uterine sarcoma (n = 44) and atypical leiomyoma (n = 94) were retrospectively collected from four institutions. The cohort was randomly divided into training (84/138, 60.0%) and validation (54/138, 40.0%) sets. Two independent readers evaluated six qualitative MRI features and two DWI-based quantitative parameters for each index tumor. Multivariable logistic regression was used to identify the relevant qualitative MRI features. Diagnostic classifiers based on qualitative MRI features alone and in combination with DWI-based quantitative parameters were developed using a logistic regression algorithm. The diagnostic performance of the classifiers was evaluated using a cross-table analysis and calculation of the area under the receiver operating characteristic curve (AUC). Results: Mean apparent diffusion coefficient value of uterine sarcoma was lower than that of atypical leiomyoma (mean ± standard deviation, 0.94 ± 0.30 10-3 mm2/s vs. 1.23 ± 0.25 10-3 mm2/s; P < 0.001), and the relative contrast ratio was higher in the uterine sarcoma (8.16 ± 2.94 vs. 4.19 ± 2.66; P < 0.001). Selected qualitative MRI features included ill-defined margin (adjusted odds ratio [aOR], 17.9; 95% confidence interval [CI], 1.41-503, P = 0.040), intratumoral hemorrhage (aOR, 27.3; 95% CI, 3.74-596, P = 0.006), and absence of T2 dark area (aOR, 83.5; 95% CI, 12.4-1916, P < 0.001). The classifier that combined qualitative MRI features and DWI-based quantitative parameters showed significantly better performance than without DWI-based parameters in the validation set (AUC, 0.92 vs. 0.78; P < 0.001). Conclusion: The addition of DWI-based quantitative parameters to qualitative MRI features improved the diagnostic performance of the logistic regression classifier in differentiating uterine sarcomas from atypical leiomyomas on preoperative MRI.

Keywords

References

  1. Stewart EA. Uterine fibroids. Lancet 2001;357:293-298 
  2. Wallach EE, Vlahos NF. Uterine myomas: an overview of development, clinical features, and management. Obstet Gynecol 2004;104:393-406 
  3. Ryan GL, Syrop CH, Van Voorhis BJ. Role, epidemiology, and natural history of benign uterine mass lesions. Clin Obstet Gynecol 2005;48:312-324 
  4. Amant F, Coosemans A, Debiec-Rychter M, Timmerman D, Vergote I. Clinical management of uterine sarcomas. Lancet Oncol 2009;10:1188-1198 
  5. Mallmann P. Uterine sarcoma - difficult to diagnose, hard to treat. Oncol Res Treat 2018;41:674 
  6. Mbatani N, Olawaiye AB, Prat J. Uterine sarcomas. Int J Gynaecol Obstet 2018;143 Suppl 2:51-58 
  7. Major FJ, Blessing JA, Silverberg SG, Morrow CP, Creasman WT, Currie JL, et al. Prognostic factors in early-stage uterine sarcoma. A Gynecologic Oncology Group study. Cancer 1993;71(4 Suppl):1702-1709 
  8. Yorganci A, Meydanli MM, Kadiog˘lu N, Tas,kin S, Kayikcioglu F, Altin D, 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 
  9. Hricak H, Tscholakoff D, Heinrichs L, Fisher MR, Dooms GC, Reinhold C, et al. Uterine leiomyomas: correlation of MR, histopathologic findings, and symptoms. Radiology 1986;158:385-391 
  10. Murase E, Siegelman ES, Outwater EK, Perez-Jaffe LA, Tureck RW. Uterine leiomyomas: histopathologic features, MR imaging findings, differential diagnosis, and treatment. Radiographics 1999;19:1179-1197 
  11. Abdel Wahab C, Jannot AS, Bonaffini PA, Bourillon C, Cornou C, Lefrere-Belda MA, et al. Diagnostic algorithm to differentiate benign atypical leiomyomas from malignant uterine sarcomas with diffusion-weighted MRI. Radiology 2020;297:361-371 
  12. Lakhman Y, Veeraraghavan H, Chaim J, Feier D, Goldman DA, Moskowitz CS, 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 
  13. Tamai K, Koyama T, Saga T, Morisawa N, Fujimoto K, Mikami Y, et al. The utility of diffusion-weighted MR imaging for differentiating uterine sarcomas from benign leiomyomas. Eur Radiol 2008;18:723-730 
  14. Namimoto T, Yamashita Y, Awai K, Nakaura T, Yanaga Y, Hirai T, et al. 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 
  15. Takeuchi M, Matsuzaki K, Nishitani H. Hyperintense uterine myometrial masses on T2-weighted magnetic resonance imaging: differentiation with diffusion-weighted magnetic resonance imaging. J Comput Assist Tomogr 2009;33:834-837 
  16. Cornfeld D, Israel G, Martel M, Weinreb J, Schwartz P, McCarthy S. MRI appearance of mesenchymal tumors of the uterus. Eur J Radiol 2010;74:241-249 
  17. Thomassin-Naggara I, Dechoux S, Bonneau C, Morel A, Rouzier R, Carette MF, et al. How to differentiate benign from malignant myometrial tumours using MR imaging. Eur Radiol 2013;23:2306-2314 
  18. 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 2014;93:261-268 
  19. 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-e8 
  20. Zhang GF, Zhang H, Tian XM, Zhang H. Magnetic resonance and diffusion-weighted imaging in categorization of uterine sarcomas: correlation with pathological findings. Clin Imaging 2014;38:836-844 
  21. Tasaki A, Asatani MO, Umezu H, Kashima K, Enomoto T, Yoshimura N, et al. Differential diagnosis of uterine smooth muscle tumors using diffusion-weighted imaging: correlations with the apparent diffusion coefficient and cell density. Abdom Imaging 2015;40:1742-1752 
  22. Lin G, Yang LY, Huang YT, Ng KK, Ng SH, Ueng SH, et al. 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 
  23. Li HM, Liu J, Qiang JW, Zhang H, Zhang GF, Ma F. Diffusion-weighted imaging for differentiating uterine leiomyosarcoma from degenerated leiomyoma. J Comput Assist Tomogr 2017;41:599-606 
  24. Kim TH, Kim JW, Kim SY, Kim SH, Cho JY. What MRI features suspect malignant pure mesenchymal uterine tumors rather than uterine leiomyoma with cystic degeneration? J Gynecol Oncol 2018;29:e26 
  25. Valdes-Devesa V, Jimenez MDM, Sanz-Rosa D, Espada Vaquero M, Alvarez Moreno E, Sainz de la Cuesta Abbad R. Preoperative diagnosis of atypical pelvic leiomyoma and sarcoma: the potential role of diffusion-weighted imaging. J Obstet Gynaecol 2019;39:98-104 
  26. Xie H, Zhang X, Ma S, Liu Y, Wang X. Preoperative differentiation of uterine sarcoma from leiomyoma: comparison of three models based on different segmentation volumes using radiomics. Mol Imaging Biol 2019;21:1157-1164 
  27. Jagannathan JP, Steiner A, Bay C, Eisenhauer E, Muto MG, George S, et al. Differentiating leiomyosarcoma from leiomyoma: in support of an MR imaging predictive scoring system. Abdom Radiol (NY) 2021;46:4927-4935 
  28. Ogihara Y, Kitazume Y, Iwasa Y, Taura S, Himeno Y, Kimura T, et al. Prediction of histological grade of hepatocellular carcinoma using quantitative diffusion-weighted MRI: a retrospective multivendor study. Br J Radiol 2018;91:20170728 
  29. Helage S, Vandeventer S, Buy JN, Bordonne C, Just PA, Jacob D, et al. Uterine sarcomas: are there MRI signs predictive of histopathological diagnosis? A 50-patient case series with pathological correlation. Sarcoma 2021;2021:8880080 
  30. Barral M, Place V, Dautry R, Bendavid S, Cornelis F, Foucher R, et al. Magnetic resonance imaging features of uterine sarcoma and mimickers. Abdom Radiol (NY) 2017;42:1762-1772 
  31. Hindman N, Kang S, Fournier L, Lakhman Y, Nougaret S, Reinhold C, et al. MRI evaluation of uterine masses for risk of leiomyosarcoma: a consensus statement. Radiology 2023;306:e211658 
  32. Zhu J, Zhang J, Gao JY, Li JN, Yang DW, Chen M, et al. Apparent diffusion coefficient normalization of normal liver: will it improve the reproducibility of diffusion-weighted imaging at different MR scanners as a new biomarker? Medicine (Baltimore) 2017;96:e5910 
  33. Schmeel FC. Variability in quantitative diffusion-weighted MR imaging (DWI) across different scanners and imaging sites: is there a potential consensus that can help reducing the limits of expected bias? Eur Radiol 2019;29:2243-2245