Diffusion-weighted magnetic resonance imaging of uterine cervical cancer.
OBJECTIVE
To determine the feasibility of diffusion-weighted magnetic resonance (MR) imaging (DWI) of uterine cervical cancer and to investigate whether the apparent diffusion coefficient (ADC) values of cervical cancer differ from those of normal cervix and whether they could indicate the histologic type and the pathologic grade of tumor.


METHODS
Forty-two female patients with histopathologically proven uterine cervical cancer and 15 female patients with uterine leiomyomas underwent preoperative MR examinations using a 1.5-T clinical scanner (GE 1.5T Twin-Speed Infinity with Excite II scanner; GE Healthcare, Waukesha, Wis). Scanning sequences included T2-weighted fast spin-echo imaging, T2-weighted fast spin-echo with fat suppression imaging, T1-weighted spin-echo imaging, and DWI with diffusion factors of 0 and 1000 s/mm2. Parameters evaluated consisted of ADC values of uterine cervical cancer and normal cervix. Histologic specimens were stained with hematoxylin and eosin. The cellular densities of 32 uterine cervical cancers were calculated, which were regarded as the ratio of the total area of tumor cell nuclei divided by the area of sample image.


RESULTS
Apparent diffusion coefficient value was statistically different (P = 0.000) between normal and cancerous tissue in the uterine cervix; the former one was (mean [SD], 1.50 [0.16]) x 10(-3) mm2/s, and the latter one was (0.88 [0.15]) x 10(-3) mm2/s. Apparent diffusion coefficient value of squamous carcinoma was statistically lower than that of adenocarcinoma (P = 0.040). The ADC value of uterine cervical cancer correlated negatively with cellular density (r = -0.711, P = 0.000) and the grading of tumor (r = -0.778, P = 0.000).


CONCLUSIONS
Diffusion-weighted MR imaging has a potential ability to differentiate between normal and cancerous tissue in the uterine cervix, and it can indicate the histologic type of uterine cervical cancer as well. The ADC value of uterine cervical cancer represents tumor cellular density, thus providing a new method for evaluating the pathologic grading of tumor.
Liu
Ying
Y
Department of Radiology, General Hospital of Tianjin Medical University, Tianjin, China.
Bai
Renju
R
Sun
Haoran
H
Liu
Haidong
H
Wang
Dehua
D
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000704:Analysis of Variance; D003937:Diagnosis, Differential; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D005260:Female; D006801:Humans; D007889:Leiomyoma / Q000473:pathology; D008875:Middle Aged; D002583:Uterine Cervical Neoplasms / Q000473:pathology*