MR diffusion-weighted imaging-based subcutaneous tumour volumetry in a xenografted nude mouse model using 3D Slicer: an accurate and repeatable method.
Accurate and repeatable measurement of the gross tumour volume(GTV) of subcutaneous xenografts is crucial in the evaluation of anti-tumour therapy. Formula and image-based manual segmentation methods are commonly used for GTV measurement but are hindered by low accuracy and reproducibility. 3D Slicer is open-source software that provides semiautomatic segmentation for GTV measurements. In our study, subcutaneous GTVs from nude mouse xenografts were measured by semiautomatic segmentation with 3D Slicer based on morphological magnetic resonance imaging(mMRI) or diffusion-weighted imaging(DWI)(b = 0,20,800 s/mm(2)) . These GTVs were then compared with those obtained via the formula and image-based manual segmentation methods with ITK software using the true tumour volume as the standard reference. The effects of tumour size and shape on GTVs measurements were also investigated. Our results showed that, when compared with the true tumour volume, segmentation for DWI(P = 0.060-0.671) resulted in better accuracy than that mMRI(P < 0.001) and the formula method(P < 0.001). Furthermore, semiautomatic segmentation for DWI(intraclass correlation coefficient, ICC = 0.9999) resulted in higher reliability than manual segmentation(ICC = 0.9996-0.9998). Tumour size and shape had no effects on GTV measurement across all methods. Therefore, DWI-based semiautomatic segmentation, which is accurate and reproducible and also provides biological information, is the optimal GTV measurement method in the assessment of anti-tumour treatments.
Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, 510080, China.|Graduate College, Southern Medical University, Guangzhou, Guangdong, 510515, China.
Zelan
Z
Ma
Department of Radiology, The Affiliated Guangzhou First Hospital, Guangzhou Medical University, Guangzhou, Guangdong, 510180, China.
Xin
X
Chen
Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, 510080, China.|Graduate College, Southern Medical University, Guangzhou, Guangdong, 510515, China.
Yanqi
Y
Huang
Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, 510080, China.|School of Medicine, South China University of Technology, Guangzhou, Guangdong, 510006, China.
Lan
L
He
Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, 510080, China.|Graduate College, Southern Medical University, Guangzhou, Guangdong, 510515, China.
Cuishan
C
Liang
Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, 510080, China.
Changhong
C
Liang
Department of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, 510080, China.
Zaiyi
Z
Liu
D000465:Algorithms; D000818:Animals; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D051379:Mice; D009369:Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D011859:Radiography; D012984:Software; D047368:Tumor Burden; D023041:Xenograft Model Antitumor Assays