Intravoxel incoherent motion diffusion-weighted imaging of pancreatic neuroendocrine tumors: prediction of the histologic grade using pure diffusion coefficient and tumor size.
OBJECTIVE
The purpose of this study was to assess the value of intravoxel incoherent motion and diffusion-weighted imaging for predicting the histologic grade of pancreatic neuroendocrine tumors (PNETs).


METHODS
Forty patients with surgically diagnosed PNETs who underwent preoperative magnetic resonance imaging, including diffusion-weighted imaging with a series of 10 b values (0-1000 s/mm(2), were included in this institutional review board-approved retrospective study. The apparent diffusion coefficient (ADC(total)), the intravoxel incoherent motion parameters (pure diffusion coefficient [D], pseudodiffusion coefficient [D(*)], and perfusion fraction [f]) were measured on the tumors. Histologic grading was performed on the basis of the World Health Organization 2010 classification system. Logistic regression analysis and receiver operating curve analysis were performed to identify the significant factors predicting the histologic grades.


RESULTS
Grades 2 and 3 tumors were significantly larger than grade 1 tumors (average 3.62 cm vs 2.17 cm in diameter; P=0.001). Grades 2 and 3 tumors showed significantly lower D values than did grade 1 tumors (0.95 vs 1.21×10(-3) mm(2)/s; P=0.009), although the ADC(total) showed no significant difference. When any of the following 2 criteria was used, (a) tumor size smaller than 2.0 cm in diameter and (b) D value greater than 1.2×10(-3) mm(2)/s, the sensitivity, specificity, and positive predictive value for diagnosing grade 1 PNETs were 76.92%, 100%, and 100%, respectively.


CONCLUSIONS
Pure diffusion coefficient (D) is possibly a better marker than ADC(total) is for differentiating grade 1 from grade 2 or 3 PNET and, combined with tumor size, can predict grade 1 PNET with a high specificity.
From the *Department of Radiology, †Institute of Radiation Medicine, Departments of ‡Pathology, and §Surgery, Seoul National University College of Medicine, Seoul, South Korea; and ∥MR Application Development, Siemens Healthcare, Erlangen, Germany.
Eui Jin
EJ
Hwang
Jeong Min
JM
Lee
Jeong Hee
JH
Yoon
Jung Hoon
JH
Kim
Joon Koo
JK
Han
Byung Ihn
BI
Choi
Kyoung-Bun
KB
Lee
Jin-Young
JY
Jang
Sun-Whe
SW
Kim
Marcel Dominik
MD
Nickel
Berthold
B
Kiefer
D000328:Adult; D000368:Aged; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D060787:Neoplasm Grading; D018358:Neuroendocrine Tumors / Q000175:diagnosis* / Q000601:surgery; D010190:Pancreatic Neoplasms / Q000175:diagnosis* / Q000601:surgery; D011237:Predictive Value of Tests; D012189:Retrospective Studies