

ORIGINAL ARTICLE 

Year : 2016  Volume
: 12
 Issue : 1  Page : 401405 

Application of apparent diffusion coefficient and exponent apparent diffusion coefficient values in magnetic resonance imaging diffusionweighted imaging to differentiate benign and malignant ovarian epithelial tumors
YuXing Wang, MingZhi Yuan, ZhaoXia Wen
Department of Radiology, Linyi People's Hospital, Linyi 276003, China
Date of Web Publication  13Apr2016 
Correspondence Address: ZhaoXia Wen Department of Radiology, Linyi People's Hospital, No. 27 Jiefang Road, Linyi 276003 China
Source of Support: None, Conflict of Interest: None  Check 
DOI: 10.4103/09731482.163667
Objective: The aim of this study was to investigate the value of two quantitative indicators, the apparent diffusion coefficient (ADC) and the exponent apparent diffusion coefficient (EADC), of magnetic resonance imaging (MRI) diffusionweighted imaging (DWI) in the differential diagnosis of ovarian epithelial tumors. Materials and Methods: Clinical and MRI data from ovarian epithelial tumors were analyzed after pathology confirmation of 85 lesions from 76 cases (47 lesions from 41 benign cases; 38 lesions from 35 malignant cases). Patients underwent routine MRI examination and DWI before surgery. The average ADC and EADC values of the solid sections of the tumors were measured when the b value was 1000 s/mm ^{2}. Results: The mean ADC value of the solid sections in the benign group was 1.28 ± 0.23 × 10^{−3} mm ^{2}/s; the average EADC value was 27.96 ± 5.78 × 10^{−2}. In the malignant group, the mean ADC value of the solid sections was 0.86 ± 0.17 × 10^{−3} mm ^{2}/s; the average EADC value was 42.37 ± 5.96 × 10^{−2}. When the b value was 1000 s/mm ^{2}, there was a statistically significant difference in ADC and EADC values between benign and malignant ovarian tumors (P < 0.05). Conclusion: ADC and EADC values of DWI can be used to differentiate benign and malignant ovarian epithelial tumors. Keywords: Apparent diffusion coefficient, diffusionweighted imaging, epithelial ovarian tumor, exponent apparent diffusion coefficient
How to cite this article: Wang YX, Yuan MZ, Wen ZX. Application of apparent diffusion coefficient and exponent apparent diffusion coefficient values in magnetic resonance imaging diffusionweighted imaging to differentiate benign and malignant ovarian epithelial tumors. J Can Res Ther 2016;12:4015 
How to cite this URL: Wang YX, Yuan MZ, Wen ZX. Application of apparent diffusion coefficient and exponent apparent diffusion coefficient values in magnetic resonance imaging diffusionweighted imaging to differentiate benign and malignant ovarian epithelial tumors. J Can Res Ther [serial online] 2016 [cited 2020 Jul 16];12:4015. Available from: http://www.cancerjournal.net/text.asp?2016/12/1/401/163667 
> Introduction   
The most common ovarian tumors are of epithelial origin and account for 50–70% of primary ovarian tumors, and 85–90% of malignant ovarian tumors. The number of deaths from malignant ovarian tumors exceeds all other gynecological cancers.^{[1]} Malignant ovarian tumors are the fourth leading cause of tumorassociated deaths in women worldwide.^{[2]} The early diagnosis and treatment of ovarian cancer can improve the 5year patient survival rate,^{[3],[4]} and the differential diagnosis of benign and malignant ovarian tumors has been the focus of clinical research. Ultrasound and magnetic resonance imaging (MRI) examination are commonly used methods to diagnose ovarian tumors. The sensitivity of ultrasound is high; however, the specificity is low. MRI has the advantage of the higher resolution of soft tissues and multidimensional imaging; however, there are still difficulties in the differential diagnosis of benign and malignant tumors using conventional MRI examination. With the ongoing development of imaging technology and high field strength MRI (1.5 T, 3.0 T) software, diffusionweighted imaging (DWI) has been widely used clinically, and can provide functional information of three parameters: Diffusion, and exponent apparent diffusion coefficient (EADC) and apparent diffusion coefficient (ADC) values (quantitative indicators). Currently, DWI has been widely used to examine the central nervous system, especially for early diagnosis of cerebral infarction.^{[5],[6]} ADC values have been used in breast,^{[7]} gallbladder,^{[8]} kidney,^{[9],[10],[11]} and benign and malignant ovarian tumors;^{[12],[13]} EADC values have also been used to identify benign and malignant kidney tumors,^{[14]} but not for the differential diagnosis of benign and malignant ovarian tumors. It is rare that ADC and EADC values are used for the differential diagnosis of benign and malignant ovarian tumors of epithelial origin.
This study retrospectively analyzed MRI data of 85 epithelial ovarian tumors from 76 pathologyconfirmed cases to investigate the diagnostic value of ADC and EADC to differentiate benign and malignant epithelial ovarian tumors.
> Materials and Methods   
General data
The clinical and MRI data from 76 cases collected at the Linyi People's Hospital, from July 2008 to May 2014 were evaluated for this study. A total of 85 lesions were examined: (21 lesions from 18 cases of serous cystadenocarcinoma, 15 lesions from 15 cases of mucinous cystadenocarcinoma, 28 lesions from 24 cases of serous cystadenoma, 19 lesions from 17 cases of mucinous cystadenoma, and 1 lesion each from single cases of clear cell carcinoma and endometrial carcinoma). The average patient age was 49 years (range, 18–74 years). The main clinical manifestations included back pain, lower abdominal pain or lumps, tenderness in adjacent areas, irregular vaginal bleeding, and menstrual disorders.
Magnetic resonance imaging examination
A 1.5 T Twin Speed Infinity with Excite II superconducting MRI system, and Torsopa abdominal phased array surface coils were used. Patients were trained to breathe with breasts, and parenthesis was bounded to the top of bilateral internal iliac to minimize the effects of respiratory motion. All patients received routine MRI scanning and DWI examination. The scanning sequence follows: (1) For the sagittal, horizontal position T2weighted imaging, the technology of respiratory trigger was used: TR/TE, 4000/80 ms; NEX, 2, matrix, 320 × 224; FOV, 32–36 cm; thickness/spacing, 5 mm/1 mm. Presaturated suppression technology was applied to the horizontal position T2weighted imaging of fatsuppressed sequence; (2) for the horizontal position of the SE sequences T1weighted imaging, the technology of respiratory compensation was used: TR/TE, 500/20 ms; NEX, 2; FOV, 3236 cm; matrix, 320 × 160; thickness/spacing, 5 mm/1 mm; (3) for the FSPGR axial of fatsuppressed sequence, TR/TE, 245/4.2 ms; NEX, 1; FOV, 32–36 cm; matrix, 256 × 128; thickness/spacing, 5 mm/1 mm; and (4) for the DWI sequence, singleshot spin echoecho planar imaging (SEEPI) sequence horizontal position scanning of EPI was applied: (b value, 0, 1000 s/mm ^{2}; TR/TE, 4000/52 ms; NEXT, 4; matrix, 128 × 128; thickness/spacing, 5 mm/1 mm; FOV, 32–36 cm). The scanning range was from the level of the anterior superior iliac spine to the pelvic floor, with appropriate adjustments based on the lesion. The pressure grease images and pressure grease sequences were not the terms used in usual MRI practice.
Image transmission
MR diffusion imaging information was transferred to the AW 4.0 workstation of the 1.5 T Twin Speed Infinity with Excite II superconducting MRI system and the diffusion image was processed using Functool 2.0 package applications.
Image processing
The following method was used to process the images. The images of fat, bone, gas, and surrounding tissue images were removed, and the threshold value was defined. Then the b value was entered to generate the DWI, ADC, and EADC maps.
A selection of the region of interest (ROI) was as follows: (1) The size of the ROI was approximately 50–100 mm ^{2}. According to conventional axial T2weighted imaging and DWI images, an ROI was determined (ROI was the same size at all levels if possible). The ROI was measured at three successive ADC, and EADC map levels, and the mean was considered the final measured value. (2) The ROI was placed in the solid section, avoiding the edges of the lesion and cystic necrotic areas. (3) The ADC and EADC values were measured and recorded, and the maps saved.
Statistical analysis
SPSS 17.0 (International Business Machines Corporation, New York, USA) software was used to determine the normal distribution of ADC and EADC values of benign and malignant epithelial ovarian tumors. The single factor variance analysis was used after confirming the data obeyed the normal distribution, and the ADC and EADC values in the benign and malignant groups were compared. P < 0.05 was considered as statistically significant.
> Results   
Magnetic resonance imaging scanning, diffusionweighted imaging, and apparent diffusion coefficient and exponent apparent diffusion coefficient maps of benign epithelial tumors
The solid portions of 47 benign epithelial ovarian tumors from 41 cases showed a slightly lower signal in TIWI, fatsuppressed sequence images of T2weighted imaging showed a slightly higher signal, and DWI showed an equal, slightly higher signal. The ADC map was mainly green; the EADC was light blue [Figure 1].  Figure 1: Mucinous cystadenoma of left ovary. (ad) Sagittal signal T2weighted imaging, the pressure grease of horizontal position T2weighted imaging and T1weighted imaging left attachment zone, huge and oval multiroom solid and cystic lumps can be seen, the border is still clear, the walls and thickness are uneven, showed equal signal of T1 and T2, the size of cysts is various, showed long signal of T1 and T2; (e) the horizontal position diffusionweighted imaging, the wall and separation showed a slightly higher signal, while cysts showed low signal; (f) map of apparent diffusion coefficient in the same level, the solid part of the wall and separation is mainly greenish yellow, apparent diffusion coefficient value is high; (g) map of exponent apparent diffusion coefficient in the same level, the solid part of the wall and separation is mainly light blue, exponent apparent diffusion coefficient value is low
Click here to view 
Magnetic resonance imaging scanning, diffusionweighted imaging, apparent diffusion coefficient, and exponent apparent diffusion coefficient maps of malignant epithelial tumors
The solid portion of 38 malignant epithelial ovarian tumors from 35 cases showed a slightly lower signal in TIWI, fatsuppressed sequence images in T2weighted imaging showed a slightly higher signal, and DWI showed a slightly higher signal. The ADC map was mainly blue; the EADC was greenish yellow [Figure 2].  Figure 2: Serous cystadenocarcinoma of left ovary. (ad) Sagittal signal T2weighted imaging, the pressure grease of horizontal position T2weighted imaging and T1weighted imaging left attachment zone, huge and oval multiroom solid and cystic lumps can be seen, the border is not clear, the thickness of walls is uneven, mural nodule can be seen, showed equal signal of T1 and a slightly longer signal of T2, the cysts showed long signal of T1 and T2; (e) the horizontal position diffusionweighted imaging, the wall and mural nodule showed a slightly higher signal, while cysts showed low signal; (f) map of apparent diffusion coefficient in the same level, the wall and mural nodule is mainly bluegreen, apparent diffusion coefficient value is low; (g) map of exponent apparent diffusion coefficient in the same level, the wall and mural nodule is mainly greenish yellow, exponent apparent diffusion coefficient value is low
Click here to view 
Comparison of apparent diffusion coefficient and exponent apparent diffusion coefficient values
As shown in [Table 1] and [Table 2], there was a statistical significance in ADC and EADC values between benign and malignant epithelial ovarian tumors (P < 0.05).  Table 1: Comparison of ADC values of benign and malignant ovarian epithelial tumor (b=1000 s/mm^{2})
Click here to view 
 Table 2: Comparison of EADC values of benign and malignant ovarian epithelial tumor (b=1000 s/mm^{2})
Click here to view 
> Discussion   
This study demonstrated that the ADC value of malignant epithelial tumors was lower than that of benign epithelial tumors, and the EADC value of malignant epithelial ovarian tumors was higher than that of benign epithelial tumors. Li et al.^{[15]} Takeuchi et al.^{[16]} and others have used ADC values to differentiate benign and malignant ovarian tumors and have reported conclusions consistent with our study. The ADC value of the malignant ovarian tumors measured by Takeuchi et al. was 1.03 ± 0.19 × 10^{−3} mm ^{2}/s and the benign ovarian tumors measured 1.38 ± 0.30 × 10^{−3} mm ^{2}/s. In our study, the mean ADC values of the malignant epithelial ovarian tumors was 0.86 ± 0.17 × 10^{−3} mm ^{2}/s and the EADC value was 42.37 ± 5.96 × 10^{−2} mm ^{2}/s. The mean ADC value of the benign ovarian epithelial tumors was 1.28 ± 0.23 × 10^{−3} mm ^{2}/s and the EADC value was 27.96 ± 5.78 × 10^{−2} mm ^{2}/s. Levy et al.^{[17]} Nakayama et al.^{[18]} and other researchers have used ADC values for the differential diagnosis of benign and malignant ovarian tumors (including ovarian epithelial tumors, sex cord stromal tumors, germ cell tumors, etc.), but not for the differential diagnosis of ovarian tumors of epithelial origin. In this study, ADC and EADC values were used primarily for the differential diagnosis of benign and malignant ovarian tumors of epithelial origin.
DWI is currently the only method to detect the movement of water molecules in the body and is based on the principles of Brownian motion ^{More Details}. The degree of freedom movement of water molecules is mainly reflected by DWI, thus reflecting the structural characteristics of tissues. The signal intensity of DWI can initially reflect the diffusion speed of water molecules in the tissue while the ADC and EADC values can quantitatively calculate the size; there are both correlations and differences between these. The formula to calculate the DWI signal strength is SI = SI_{0} × e ^{−bD}, where SI_{0} is representative of the SEEPI T2weighted imaging signal intensity (b = 0 mm ^{2}/s), e is the power exponent, D is the diffusion coefficient, and b is the diffusionsensitive factor. As seen in the formula, the signal intensity of DWI depends on the signal strength of T2weighted imaging and is related to the diffusion coefficient D, and the b value. When the subject tissue T2weighted imaging shows a high signal, the signal intensity of the DWI image also increases T2 transmission effects. ADC = ln (S2/S1)/(b1 − b2), EADC = S2/S1, b2 is a high value, while b0 is lower; S2 is the signal intensity of DWI when b value is high, and SI is the signal intensity of DWI when the b value is low, and ln represents a natural logarithm. S2/S1 eliminates the effects of proton density and the T2 transmission effect, so the ADC and EADC values can accurately and quantitatively reflect the degree of diffusion of water molecules. From the formula above, we can calculate the relationship between the ADC and EADC values. The EADC value = EADC = e ^{−bADC}; the EADC value is negative relative to the ADC value.^{[19]} The main factors affecting the movement of water molecules in the tissue are as follows: (1) Movement of water molecules within the tissue structure, the size of cell membranes and organelles in the internal and external cells, etc.^{[20]} In general, higher the cell specificity, the greater the volume of the organelles, the higher the extent of movement of water molecules within the cell, the larger the number of molecules and cells, and the larger the volume resulting in restricted movement of water molecules, (2) biochemical characteristics of the tissue, such as temperature and viscosity, and (3) microcirculation perfusion, etc., These factors will affect the speed and direction of movement of the water molecules. So when there are lesions, changes in these above factors above will have an effect on the DWI signal intensity of DWI, and ADC and EADC values will change. In this study, malignant epithelial ovarian tumors had more atypical molecules than benign tumors; the organelles (nucleus, nuclear bodies, etc.) were more abundant, the volume was larger, and the number of cells was larger. Therefore, there were more limitations to the movement of water molecules inside and outside the cells, resulting in decreased ADC and significantly increased EADC values.
This study demonstrates that ADC and EADC DWI values can differentiate benign and malignant epithelial ovarian tumors. This study was limited by the fewer cases of junctional cystadenoma and lack of comparison between groups. Larger sample sizes are needed in future studies.
> Conclusion   
Apparent diffusion coefficient and exponent apparent diffusion coefficient values of diffusionweighted magnetic resonance imaging can be used to differentiate benign and malignant ovarian epithelial tumors of DWI can be used to differentiate benign and malignant ovarian epithelial tumors.
Financial support and sponsorship
Nil
Conflicts of interest
There are no conflicts of interest.
> References   
1.  Sallinen H, Heikura T, Koponen J, Kosma VM, Heinonen S, YläHerttuala S, et al. Serum angiopoietin2 and soluble VEGFR2 levels predict malignancy of ovarian neoplasm and poor prognosis in epithelial ovarian cancer. BMC Cancer 2014;14:696. 
2.  Park B, Park S, Kim TJ, Ma SH, Kim BG, Kim YM, et al. Epidemiological characteristics of ovarian cancer in Korea. J Gynecol Oncol 2010;21:2417. 
3.  Ditto A, Martinelli F, Lorusso D, Haeusler E, Carcangiu M, Raspagliesi F. Fertility sparing surgery in early stage epithelial ovarian cancer. J Gynecol Oncol 2014;25:3207. 
4.  Burges A, Schmalfeldt B. Ovarian cancer: Diagnosis and treatment. Dtsch Arztebl Int 2011;108:63541. 
5.  Baird AE, Warach S. Magnetic resonance imaging of acute stroke. J Cereb Blood Flow Metab 1998;18:583609. 
6.  Campbell BC, Tu HT, Christensen S, Desmond PM, Levi CR, Bladin CF, et al. Assessing response to stroke thrombolysis: Validation of 24hour multimodal magnetic resonance imaging. Arch Neurol 2012;69:4650. 
7.  Tan SL, Rahmat K, Rozalli FI, MohdShah MN, Aziz YF, Yip CH, et al. Differentiation between benign and malignant breast lesions using quantitative diffusionweighted sequence on 3 T MRI. Clin Radiol 2014;69:6371. 
8.  Irie H, Kamochi N, Nojiri J, Egashira Y, Sasaguri K, Kudo S. High bvalue diffusionweighted MRI in differentiation between benign and malignant polypoid gallbladder lesions. Acta Radiol 2011;52:23640. 
9.  Chandarana H, Lee VS, Hecht E, Taouli B, Sigmund EE. Comparison of biexponential and monoexponential model of diffusion weighted imaging in evaluation of renal lesions: Preliminary experience. Invest Radiol 2011;46:28591. 
10.  Rheinheimer S, Stieltjes B, Schneider F, Simon D, Pahernik S, Kauczor HU, et al. Investigation of renal lesions by diffusionweighted magnetic resonance imaging applying intravoxel incoherent motionderived parameters – initial experience. Eur J Radiol 2012;81:e3106. 
11.  Baliyan V, Das CJ, Sharma S, Gupta AK. Diffusionweighted imaging in urinary tract lesions. Clin Radiol 2014;69:77382. 
12.  Punwani S. Diffusion weighted imaging of female pelvic cancers: Concepts and clinical applications. Eur J Radiol 2011;78:219. 
13.  Bakir B, Bakan S, Tunaci M, Bakir VL, Iyibozkurt AC, Berkman S, et al. Diffusionweighted imaging of solid or predominantly solid gynaecological adnexial masses: Is it useful in the differential diagnosis? Br J Radiol 2011;84:60011. 
14.  Zhang YL, Yu BL, Ren J, Qu K, Wang K, Qiang YQ, et al. EADC values in diagnosis of renal lesions by 3.0 T diffusionweighted magnetic resonance imaging: Compared with the ADC values. Appl Magn Reson 2013;44:349363. 
15.  Li W, Chu C, Cui Y, Zhang P, Zhu M. Diffusionweighted MRI: A useful technique to discriminate benign versus malignant ovarian surface epithelial tumors with solid and cystic components. Abdom Imaging 2012;37:897903. 
16.  Takeuchi M, Matsuzaki K, Nishitani H. Diffusionweighted magnetic resonance imaging of ovarian tumors: Differentiation of benign and malignant solid components of ovarian masses. J Comput Assist Tomogr 2010;34:1736. 
17.  Levy A, Medjhoul A, Caramella C, Zareski E, Berges O, Chargari C, et al. Interest of diffusionweighted echoplanar MR imaging and apparent diffusion coefficient mapping in gynecological malignancies: A review. J Magn Reson Imaging 2011;33:10207. 
18.  Nakayama T, Yoshimitsu K, Irie H, Aibe H, Tajima T, Nishie A, et al. Diffusionweighted echoplanar 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;22:2718. 
19.  Brüning R, Wu RH, Deimling M, Porn U, Haberl RL, Reiser M. Diffusion measurements in the ischemic human brain with a steadystate sequence. Invest Radiol 1996;31:70915. 
20.  Pierpaoli C, Jezzard P, Basser PJ, Barnett A, Di Chiro G. Diffusion tensor MR imaging of the human brain. Radiology 1996;201:63748. 
[Figure 1], [Figure 2]
[Table 1], [Table 2]
