Browse > Article
http://dx.doi.org/10.12701/yujm.2019.00234

Assessment of solid components of borderline ovarian tumor and stage I carcinoma: added value of combined diffusion- and perfusion-weighted magnetic resonance imaging  

Kim, See Hyung (Department of Radiology, School of Medicine, Kyungpook National University)
Publication Information
Journal of Yeungnam Medical Science / v.36, no.3, 2019 , pp. 231-240 More about this Journal
Abstract
Background: We sought to determine the value of combining diffusion-weighted (DW) and perfusion-weighted (PW) sequences with a conventional magnetic resonance (MR) sequence to assess solid components of borderline ovarian tumors (BOTs) and stage I carcinomas. Methods: Conventional, DW, and PW sequences in the tumor imaging studies of 70 patients (BOTs, n=38; stage I carcinomas, n=32) who underwent surgery with pathologic correlation were assessed. Two independent radiologists calculated the parameters apparent diffusion coefficient (ADC), $K^{trans}$ (vessel permeability), and $V_e$ (cell density) for the solid components. The distribution on conventional MR sequence and mean, standard deviation, and 95% confidence interval of each DW and PW parameter were calculated. The inter-observer agreement among the two radiologists was assessed. Area under the receiver operating characteristic curve (AUC) and multivariate logistic regression were performed to compare the effectiveness of DW and PW sequences for average values and to characterize the diagnostic performance of combined DW and PW sequences. Results: There were excellent agreements for DW and PW parameters between radiologists. The distributions of ADC, $K^{trans}$, and $V_e$ values were significantly different between BOTs and stage I carcinomas, yielding AUCs of 0.58 and 0.68, 0.78 and 0.82, and 0.70 and 0.72, respectively, with ADC yielding the lowest diagnostic performance. The AUCs of the DW, PW, and combined PW and DW sequences were $0.71{\pm}0.05$, $0.80{\pm}0.05$, and $0.85{\pm}0.05$, respectively. Conclusion: Combining PW and DW sequences to a conventional sequence potentially improves the diagnostic accuracy in the differentiation of BOTs and stage I carcinomas.
Keywords
Borderline ovarian tumor; Diagnosis; Magnetic resonance imaging; Stage I ovarian carcinoma;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Yankeelov TE, Lepage M, Chakravarthy A, Broome EE, Niermann KJ, Kelley MC, et al. Integration of quantitative DCE-MRI and ADC mapping to monitor treatment response in human breast cancer: initial results. Magn Reson Imaging 2007;25:1-13.   DOI
2 Aref M, Handbury JD, Xiuquan Ji J, Aref S, Wiener EC. Spatial and temporal resolution effects on dynamic contrast-enhanced magnetic resonance mammography. Magn Reson Imaging 2007;25:14-34.   DOI
3 Cuenod CA, Fournier L, Balvay D, Guinebretiere JM. Tumor angiogenesis: pathophysiology and implications for contrast-enhanced MRI and CT assessment. Abdom Imaging 2006;31:188-93.   DOI
4 deSouza NM, O'Neill R, McIndoe GA, Dina R, Soutter WP. Borderline tumors of the ovary: CT and MRI features and tumor markers in differentiation from stage I disease. AJR Am J Roentgenol 2005;184:999-1003.   DOI
5 Kozlowski P, Chang SD, Jones EC, Berean KW, Chen H, Goldenberg SL. Combined diffusion-weighted and dynamic contrast-enhanced MRI for prostate cancer diagnosis--correlation with biopsy and histopathology. J Magn Reson Imaging 2006;24:108-13.   DOI
6 Emoto M, Iwasaki H, Mimura K, Kawarabayashi T, Kikuchi M. Differences in the angiogenesis of benign and malignant ovarian tumors, demonstrated by analyses of color Doppler ultrasound, immunohistochemistry, and microvessel density. Cancer 1997;80:899-907.   DOI
7 Fujii S, Kakite S, Nishihara K, Kanasaki Y, Harada T, Kigawa J, et al. Diagnostic accuracy of diffusion-weighted imaging in differentiating benign from malignant ovarian lesions. J Magn Reson Imaging 2008;28:1149-56.   DOI
8 Hricak H, Chen M, Coakley FV, Kinkel K, Yu KK, Sica G, et al. Complex adnexal masses: detection and characterization with MR imaging--multivariate analysis. Radiology 2000;214:39-46.   DOI
9 Landis JR, Koch GG. An application of hierarchical kappa-type statistics in the assessment of majority agreement among multiple observers. Biometrics 1977;33:363-74.   DOI
10 Lee EJ, Kim SH, Kim YH, Lee HJ. Is CA-125 an additional help to radiologic findings for differentiation borderline ovarian tumor from stage I carcinoma? Acta Radiol 2011;52:458-62.   DOI
11 Metz CE, Shen JH. Gains in accuracy from replicated readings of diagnostic images: prediction and assessment in terms of ROC analysis. Med Decis Making 1992;12:60-75.   DOI
12 Metz CE, Pan X. "Proper" binormal ROC curves: theory and maximum-likelihood estimation. J Math Psychol 1999;43:1-33.   DOI
13 Sohaib SA, Sahdev A, Van Trappen P, Jacobs IJ, Reznek RH. Characterization of adnexal mass lesions on MR imaging. AJR Am J Roentgenol 2003;180:1297-304.   DOI
14 Moore RG, Bast RC Jr. How do you distinguish a malignant pelvic mass from a benign pelvic mass? Imaging, biomarkers, or none of the above. J Clin Oncol 2007;25:4159-61.   DOI
15 Nakayama T, Yoshimitsu K, Irie H, Aibe H, Tajima T, Nishie A, et al. Diffusion-weighted echo-planar MR imaging and ADC mapping in the differential diagnosis of ovarian cystic masses: usefulness of detecting keratinoid substances in mature cystic teratomas. J Magn Reson Imaging 2005;22:271-8.   DOI
16 Timmerman D, Valentin L, Bourne TH, Collins WP, Verrelst H, Vergote I, et al. Terms, definitions and measurements to describe the sonographic features of adnexal tumors: a consensus opinion from the International Ovarian Tumor Analysis (IOTA) Group. Ultrasound Obstet Gynecol 2000;16:500-5.   DOI
17 Thomassin-Naggara I, DaraiE, Cuenod CA, Rouzier R, Callard P, Bazot M. Dynamic contrast-enhanced magnetic resonance imaging: a useful tool for characterizing ovarian epithelial tumors. J Magn Reson Imaging 2008;28:111-20.   DOI
18 Thomassin-Naggara I, Bazot M, DaraiE, Callard P, Thomassin J, Cuenod CA. Epithelial ovarian tumors: value of dynamic contrast-enhanced MR imaging and correlation with tumor angiogenesis. Radiology 2008;248:148-59.   DOI
19 Thomassin-Naggara I, DaraiE, Cuenod CA, Fournier L, Toussaint I, Marsault C, et al. Contribution of diffusion-weighted MR imaging for predicting benignity of complex adnexal masses. Eur Radiol 2009;19:1544-52.   DOI
20 Nishida N, Yano H, Komai K, Nishida T, Kamura T, Kojiro M. Vascular endothelial growth factor C and vascular endothelial growth factor receptor 2 are related closely to the prognosis of patients with ovarian carcinoma. Cancer 2004;101:1364-74.   DOI
21 Song T, Laine AF, Chen Q, Rusinek H, Bokacheva L, Lim RP, et al. Optimal k-space sampling for dynamic contrast-enhanced MRI with an application to MR renography. Magn Reson Med 2009;61:1242-8.   DOI
22 Orre M, Lotfi-Miri M, Mamers P, Rogers PA. Increased microvessel density in mucinous compared with malignant serous and benign tumours of the ovary. Br J Cancer 1998;77:2204-9.   DOI
23 Oto A, Kayhan A, Jiang Y, Tretiakova M, Yang C, Antic T, et al. Prostate cancer: differentiation of central gland cancer from benign prostatic hyperplasia by using diffusion-weighted and dynamic contrast-enhanced MR imaging. Radiology 2010;257:715-23.   DOI
24 Padhani AR, Dzik-Jurasz A. Perfusion MR imaging of extracranial tumor angiogenesis. Top Magn Reson Imaging 2004;15:41-57.   DOI
25 Takemori M, Nishimura R, Hasegawa K. Clinical evaluation of MRI in the diagnosis of borderline ovarian tumors. Acta Obstet Gynecol Scand 2002;81:157-61.   DOI
26 Takeuchi M, Matsuzaki K, Nishitani H. Diffusion-weighted magnetic resonance imaging of ovarian tumors: differentiation of benign and malignant solid components of ovarian masses. J Comput Assist Tomogr 2010;34:173-6.   DOI