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Rapid Segmentation of Renal Tumours to Calculate Volume Using 3D Interpolation
被引:1
作者:
Michael Y. Chen
Maria A. Woodruff
Boon Kua
Nicholas J. Rukin
机构:
[1] Redcliffe Hospital,Department of Urology
[2] University of Queensland,School of Medicine
[3] Queensland University of Technology,Science and Engineering Faculty
[4] Wesley Hospital,undefined
来源:
Journal of Digital Imaging
|
2021年
/
34卷
关键词:
Renal cancer;
Prognosis;
Diagnosis;
Organ volume;
D O I:
暂无
中图分类号:
学科分类号:
摘要:
Small renal masses are commonly diagnosed with modern medical imaging. Renal tumour volume has been explored as a prognostic tool to help decide when intervention is needed and appears to provide additional prognostic information for smaller tumours compared with tumour diameter. However, the current method of calculating tumour volume in clinical practice uses the ellipsoid equation (π/6 × length × width × height) which is an oversimplified approach. Some research groups trace the contour of the tumour in every image slice which is impractical for clinical use. In this study, we demonstrate a method of using 3D segmentation software and the 3D interpolation method to rapidly calculate renal tumour volume in under a minute. Using this method in 27 patients that underwent radical or partial nephrectomy, we found a 10.07% mean absolute difference compared with the traditional ellipsoid method. Our segmentation volume was closer to the calculated histopathological tumour volume than the traditional method (p = 0.03) with higher Lin’s concordance correlation coefficient (0.79 vs 0.72). 3D segmentation has many uses related to 3D printing and modelling and is becoming increasingly common. Calculation of tumour volume is one additional benefit it provides. Further studies on the association between segmented tumour volume and prognosis are needed.
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页码:351 / 356
页数:5
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