Contribution of mono-exponential, bi-exponential and stretched exponential model-based diffusion-weighted MR imaging in the diagnosis and differentiation of uterine cervical carcinoma.
OBJECTIVE
To investigate the potential of various metrics derived from mono-exponential model (MEM), bi-exponential model (BEM) and stretched exponential model (SEM)-based diffusion-weighted imaging (DWI) in diagnosing and differentiating the pathological subtypes and grades of uterine cervical carcinoma.


METHODS
71 newly diagnosed patients with cervical carcinoma (50 cases of squamous cell carcinoma [SCC] and 21 cases of adenocarcinoma [AC]) and 32 healthy volunteers received DWI with multiple b values. The apparent diffusion coefficient (ADC), pure molecular diffusion (D), pseudo-diffusion coefficient (D*), perfusion fraction (f), water molecular diffusion heterogeneity index (alpha), and distributed diffusion coefficient (DDC) were calculated and compared between tumour and normal cervix, among different pathological subtypes and grades.


RESULTS
All of the parameters were significantly lower in cervical carcinoma than normal cervical stroma except alpha. SCC showed lower ADC, D, f and DDC values and higher D* value than AC; D and DDC values of SCC and ADC and D values of AC were lower in the poorly differentiated group than those in the well-moderately differentiated group.


CONCLUSIONS
Compared with MEM, diffusion parameters from BEM and SEM may offer additional information in cervical carcinoma diagnosis, predicting pathological tumour subtypes and grades, while f and D showed promising significance.


CONCLUSIONS
• DWI-derived parameters by different models are related but provide diversified information. • Commonly used ADC by MEM of DWI overestimates the tissue water diffusivity. • DWI processed by BEM could separate blood perfusion from true diffusion effects. • The derived diffusion-related and perfusion-related parameters by BEM are superior to ADC.
Department of Diagnostic Radiology, Cancer Institute & Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, No17, Panjiayuannanli, Chaoyang District, Beijing, 100021, People's Republic of China.
Meng
M
Lin
Department of Diagnostic Radiology, Cancer Institute & Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, No17, Panjiayuannanli, Chaoyang District, Beijing, 100021, People's Republic of China.
Xiaoduo
X
Yu
Department of Diagnostic Radiology, Cancer Institute & Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, No17, Panjiayuannanli, Chaoyang District, Beijing, 100021, People's Republic of China.
Yan
Y
Chen
Department of Diagnostic Radiology, Cancer Institute & Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, No17, Panjiayuannanli, Chaoyang District, Beijing, 100021, People's Republic of China. houybj@126.com.
Han
H
Ouyang
GE MR Research China, Beijing, People's Republic of China.
Bing
B
Wu
GE MR Research China, Beijing, People's Republic of China.
Dandan
D
Zheng
Department of Diagnostic Radiology, Cancer Institute & Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, No17, Panjiayuannanli, Chaoyang District, Beijing, 100021, People's Republic of China.
Chunwu
C
Zhou
D000230:Adenocarcinoma / Q000473:pathology*; D000328:Adult; D000368:Aged; D002294:Carcinoma, Squamous Cell / Q000473:pathology*; D002471:Cell Transformation, Neoplastic / Q000473:pathology; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods; D005240:Feasibility Studies; D005260:Female; D006801:Humans; D008875:Middle Aged; D008954:Models, Biological; D015588:Observer Variation; D012680:Sensitivity and Specificity; D002583:Uterine Cervical Neoplasms / Q000473:pathology*
Bi-exponential model; Diffusion-weighted imaging; Mono-exponential model; Stretched exponential model; Uterine cervical neoplasm
Cancer. 2003 Nov 1;98 (9 Suppl):2028-38
14603539
Invest Radiol. 2011 May;46(5):285-91
21102345
Int J Oncol. 2001 Oct;19(4):827-32
11562762
Gynecol Oncol. 1998 Jan;68(1):38-44
9454658
J Comput Assist Tomogr. 2009 Nov-Dec;33(6):858-62
19940650
Eur Radiol. 2014 Dec;24(12):3076-87
25052079
Balkan Med J. 2014 Jun;31(2):154-7
25207188
NMR Biomed. 2014 Oct;27(10):1184-91
25088433
Magn Reson Med. 2003 Oct;50(4):727-34
14523958
Magn Reson Imaging. 2015 Dec;33(10 ):1212-1218
26220861
NMR Biomed. 2015 Apr;28(4):468-85
25802212
Invest Radiol. 2009 Dec;44(12):769-75
19838121
J Comput Assist Tomogr. 2012 Nov-Dec;36(6):695-703
23192207
Magn Reson Med. 2015 May;73(5):1954-69
25045885
Magn Reson Imaging. 2009 Apr;27(3):355-9
18768281
Eur Radiol. 2014 Jan;24(1):176-83
23990005
Eur Radiol. 2014 Jul;24(7):1506-13
24744198
J Magn Reson Imaging. 2015 Oct;42(4):1078-85
25727776
Int J Gynecol Cancer. 2015 Jul;25(6):1073-8
25966933
J Magn Reson Imaging. 2015 Aug;42(2):454-9
25413245
J Magn Reson Imaging. 2010 Mar;31(3):531-7
20187193
Radiology. 2008 Dec;249(3):748-52
19011179
Radiology. 1986 Nov;161(2):401-7
3763909
AJR Am J Roentgenol. 2003 Jun;180(6):1621-31
12760933
Radiology. 2014 Mar;270(3):758-67
24475811
Magn Reson Insights. 2015 Jun 11;8:11-21
26106263
Eur Radiol. 2015 Jun;25(6):1708-13
25533630
Magn Reson Imaging. 2014 Dec;32(10):1206-13
25131628
Magn Reson Med. 2006 Aug;56(2):235-9
16929466
Radiother Oncol. 2014 Feb;110(2):335-41
24231244
Eur Radiol. 2013 Apr;23(4):1050-8
23179520
Int J Gynecol Cancer. 2014 May;24(4):751-7
24685827
Radiology. 2016 Feb;278(2):496-504
26230975
Radiother Oncol. 2013 Apr;107(1):117-22
23333024
Gynecol Oncol. 2010 Feb;116(2):246-52
19875159
AJR Am J Roentgenol. 2012 Oct;199(4):W496-500
22997399
Radiographics. 2000 Nov-Dec;20(6):1539-49
11112809
J Magn Reson Imaging. 2008 Oct;28(4):928-36
18821618
Eur Radiol. 2016 May;26(5):1412-9
26253255
Eur J Radiol. 2013 Dec;82(12):e782-9
24034833
Radiology. 2007 May;243(2):570-7
17400757
Eur Radiol. 2015 Jul;25(7):2033-40
25605133
J Magn Reson Imaging. 2014 Mar;39(3):624-32
24068649