Most commonly used diffusion-weighted imaging (DWI) models include intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI), stretched exponential model (SEM), and mono-exponential model (MEM). Previous studies of the four models were inconsistent on which model was more effective in distinguishing cervical cancer from normal cervical tissue.
Purpose
To assess the performance of four DWI models in characterizing cervical cancer and normal cervical tissue.
Material and Methods
Forty-seven women with suspected cervical carcinoma underwent DWI using eight b-values before treatment. Imaging parameters, calculated using IVIM, SEM, DKI, and MEM, were compared between cervical cancer and normal cervical tissue. The diagnostic performance of the models was evaluated using independent t-test, Mann–Whitney U test, receiver operating characteristic (ROC) curve analysis, and multivariate logistic regression analysis.
Results
All parameters except pseudo-diffusion coefficient (D*) differed significantly between cervical cancer and normal cervical tissue (P < 0.001). Through logistic regression analysis, all combined models showed a significant improvement in area under the ROC curve (AUC) compared to individual DWI parameters. The model with combined IVIM parameters had a larger AUC value compared to those of other combined models (P < 0.05).
Conclusion
All four DWI models are useful for differentiating cervical cancer from normal cervical tissue and IVIM may be the optimal model.
WuCJWangQZhangJ, et al.
Readout-segmented echo-planar imaging in diffusion-weighted imaging of the kidney: comparison with single-shot echo-planar imaging in image quality. Abdom Radiol (NY) 2016;
41:100–108.
2.
BammerR.Basic principles of diffusion-weighted imaging. Eur J Radiol2003;
45:169–184.
3.
Le BihanDBretonELallemandD, et al.
Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. Radiology1988;
168:497–505.
4.
MohrJPBillerJHilalSK, et al.
Magnetic resonance versus computed tomographic imaging in acute stroke. Stroke1995;
26:807–812.
5.
ChandaranaHLeeVSHechtE, et al.
Comparison of biexponential and monoexponential model of diffusion weighted imaging in evaluation of renal lesions: preliminary experience. Invest Radiol2011;
46:285–291.
6.
ShinmotoHTamuraCSogaS, et al.
An intravoxel incoherent motion diffusion-weighted imaging study of prostate cancer. AJR Am J Roentgenol2012;
199:W496–500.
7.
LiuCLiangCLiuZ, et al.
Intravoxel incoherent motion (IVIM) in evaluation of breast lesions: comparison with conventional DWI. Eur J Radiol2013;
82:e782–789.
8.
Le BihanDBretonELallemandD, et al.
MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders. Radiology1986;
161:401–407.
9.
BennettKMSchmaindaKMBennettRT, et al.
Characterization of continuously distributed cortical water diffusion rates with a stretched-exponential model. Magn Reson Med2003;
50:727–734.
10.
De SantisSGabrielliAPalomboM, et al.
Non-Gaussian diffusion imaging: a brief practical review. Magn Reson Imaging2011;
29:1410–1416.
11.
FerlayJSoerjomataramIDikshitR, et al.
Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer2015;
136:E359–386.
12.
WangMPeruchoJAUChanQ, et al.
Diffusion kurtosis imaging in the assessment of cervical carcinoma. Acad Radiol2020;
27:e94–e101.
13.
LinMYuXChenY, et al.
Contribution of mono-exponential, bi-exponential and stretched exponential model-based diffusion-weighted MR imaging in the diagnosis and differentiation of uterine cervical carcinoma. Eur Radiol2017;
27:2400–2410.
14.
ZhouYLiuJLiuC, et al.
Intravoxel incoherent motion diffusion weighted MRI of cervical cancer - Correlated with tumor differentiation and perfusion. Magn Reson Imaging2016;
34:1050–1056.
15.
LeeEYYuXChuMM, et al.
Perfusion and diffusion characteristics of cervical cancer based on intraxovel incoherent motion MR imaging-a pilot study. Eur Radiol2014;
24:1506–1513.
16.
LiCChenMWanB, et al.
A comparative study of Gaussian and non-Gaussian diffusion models for differential diagnosis of prostate cancer with in-bore transrectal MR-guided biopsy as a pathological reference. Acta Radiol2018;
59:1395–1402.
17.
ZhangJSuoSLiuG, et al.
Comparison of monoexponential, biexponential, stretched-exponential, and kurtosis models of diffusion-weighted imaging in differentiation of renal solid masses. Korean J Radiol2019;
20:791–800.
18.
YoudenWJ.Index for rating diagnostic tests. Cancer1950;
3:32–35.
19.
HoogendamJPKlerkxWMde KortGA, et al.
The influence of the b-value combination on apparent diffusion coefficient based differentiation between malignant and benign tissue in cervical cancer. J Magn Reson Imaging2010;
32:376–382.
20.
Charles-EdwardsEMorganVAttygalleAD, et al.
Endovaginal magnetic resonance imaging of stage 1A/1B cervical cancer with A T2- and diffusion-weighted magnetic resonance technique: effect of lesion size and previous cone biopsy on tumor detectability. Gynecol Oncol2011;
120:368–373.
21.
PayneGSSchmidtMMorganVA, et al.
Evaluation of magnetic resonance diffusion and spectroscopy measurements as predictive biomarkers in stage 1 cervical cancer. Gynecol Oncol2010;
116:246–252.
22.
ChenJZhangYLiangB, et al.
The utility of diffusion-weighted MR imaging in cervical cancer. Eur J Radiol2010;
74:e101–106.
23.
LiuYBaiRSunH, et al.
Diffusion-weighted magnetic resonance imaging of uterine cervical cancer.J Comput Assist Tomogr2009;
33:858–862.
24.
KilickesmezOBayramogluSInciE, et al.
Quantitative diffusion-weighted magnetic resonance imaging of normal and diseased uterine zones. Acta Radiol2009;
50:340–347.
25.
Le BihanD.Intravoxel incoherent motion perfusion MR imaging: a wake-up call. Radiology2008;
249:748–752.
26.
LiSPPadhaniAR.Tumor response assessments with diffusion and perfusion MRI. J Magn Reson Imaging2012;
35:745–763.
27.
ShaoHNiYDaiX, et al.
Diffusion-weighted MR imaging allows monitoring the effect of combretastatin A4 phosphate on rabbit implanted VX2 tumor model: 12-day dynamic results. Eur J Radiol2012;
81:578–583.
28.
WuQZhengDShiL, et al.
Differentiating metastatic from nonmetastatic lymph nodes in cervical cancer patients using monoexponential, biexponential, and stretched exponential diffusion-weighted MR imaging. Eur Radiol2017;
27:5272–5279.
29.
ReischauerCPatzwahlRKohDM, et al.
Non-mono-exponential analysis of diffusion-weighted imaging for treatment monitoring in prostate cancer bone metastases. Sci Rep2017;
7:5809.
30.
WangPThapaDWuG, et al.
A study on diffusion and kurtosis features of cervical cancer based on non-Gaussian diffusion weighted model. Magn Reson Imaging2018;
47:60–66.
31.
WinfieldJMOrtonMRCollinsDJ, et al.
Separation of type and grade in cervical tumours using non-mono-exponential models of diffusion-weighted MRI. Eur Radiol2017;
27:627–636.
32.
MazaheriYAfaqARoweDB, et al.
Diffusion-weighted magnetic resonance imaging of the prostate: improved robustness with stretched exponential modeling. J Comput Assist Tomogr2012;
36:695–703.
33.
WinfieldJMdeSouzaNMPriestAN, et al.
Modelling DW-MRI data from primary and metastatic ovarian tumours. Eur Radiol2015;
25:2033–2040.
34.
OrtonMRMessiouCCollinsD, et al.
Diffusion-weighted MR imaging of metastatic abdominal and pelvic tumours is sensitive to early changes induced by a VEGF inhibitor using alternative diffusion attenuation models. Eur Radiol2016;
26:1412–1419.
35.
AndreouAKohDMCollinsDJ, et al.
Measurement reproducibility of perfusion fraction and pseudodiffusion coefficient derived by intravoxel incoherent motion diffusion-weighted MR imaging in normal liver and metastases. Eur Radiol2013;
23:428–434.
36.
RosenkrantzABPadhaniARChenevertTL, et al.
Body diffusion kurtosis imaging: Basic principles, applications, and considerations for clinical practice. J Magn Reson Imaging2015;
42:1190–1202.
