A semi-automated volumetric software for segmentation and perfusion parameter quantification of brain tumors using 320-row multidetector computed tomography: a validation study.
OBJECTIVE
We developed a semi-automated volumetric software, NPerfusion, to segment brain tumors and quantify perfusion parameters on whole-brain CT perfusion (WBCTP) images. The purpose of this study was to assess the feasibility of the software and to validate its performance compared with manual segmentation.


METHODS
Twenty-nine patients with pathologically proven brain tumors who underwent preoperative WBCTP between August 2012 and February 2015 were included. Three perfusion parameters, arterial flow (AF), equivalent blood volume (EBV), and Patlak flow (PF, which is a measure of permeability of capillaries), of brain tumors were generated by a commercial software and then quantified volumetrically by NPerfusion, which also semi-automatically segmented tumor boundaries. The quantification was validated by comparison with that of manual segmentation in terms of the concordance correlation coefficient and Bland-Altman analysis.


RESULTS
With NPerfusion, we successfully performed segmentation and quantified whole volumetric perfusion parameters of all 29 brain tumors that showed consistent perfusion trends with previous studies. The validation of the perfusion parameter quantification exhibited almost perfect agreement with manual segmentation, with Lin concordance correlation coefficients (ρc) for AF, EBV, and PF of 0.9988, 0.9994, and 0.9976, respectively. On Bland-Altman analysis, most differences between this software and manual segmentation on the commercial software were within the limit of agreement.


CONCLUSIONS
NPerfusion successfully performs segmentation of brain tumors and calculates perfusion parameters of brain tumors. We validated this semi-automated segmentation software by comparing it with manual segmentation. NPerfusion can be used to calculate volumetric perfusion parameters of brain tumors from WBCTP.
Department of Radiology, Korea University Guro Hospital, 148 Gurodong-ro, Guro-gu, Seoul, South Korea.
Soo Young
SY
Chae
Department of Radiology, Korea University Guro Hospital, 148 Gurodong-ro, Guro-gu, Seoul, South Korea. sangil.suh@gmail.com.
Sangil
S
Suh
Department of Radiology, Korea University Guro Hospital, 148 Gurodong-ro, Guro-gu, Seoul, South Korea.
Inseon
I
Ryoo
Department of Radiology, Korea University Guro Hospital, 148 Gurodong-ro, Guro-gu, Seoul, South Korea.
Arim
A
Park
Department of Electronic Engineering, Soonchunhyang University, Asan, South Korea.
Kyoung Jin
KJ
Noh
Toshiba Medical Systems Korea Co., Seoul, South Korea.
Hackjoon
H
Shim
Department of Radiology, Korea University Guro Hospital, 148 Gurodong-ro, Guro-gu, Seoul, South Korea.
Hae Young
HY
Seol
D000328:Adult; D000368:Aged; D000465:Algorithms; D001810:Blood Volume; D001932:Brain Neoplasms / Q000098:blood supply* / Q000000981:diagnostic imaging*; D003287:Contrast Media; D005260:Female; D006801:Humans; D007472:Iohexol / Q000031:analogs & derivatives; D008297:Male; D008875:Middle Aged; D061330:Multidetector Computed Tomography / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D012189:Retrospective Studies; D012984:Software; D047368:Tumor Burden
D003287:Contrast Media; D007472:Iohexol; C038192:iopromide
Brain tumor; CT perfusion; Segmentation; Semi-automated segmentation; Whole-brain CT perfusion