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A pilot study on bladder wall thickness at different filling stages
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Paper Abstract

The ever-growing death rate and the high recurrence of bladder cancer make the early detection and appropriate followup procedure of bladder cancer attract more attention. Compare to optical cystoscopy, image-based studies have revealed its potentials in non-invasive observations of the abnormities of bladder recently, in which MR imaging turns out to be a better choice for bladder evaluation due to its non-ionizing and high contrast between urine and wall tissue. Recent studies indicate that bladder wall thickness tends to be a good indicator for detecting bladder wall abnormalities. However, it is difficult to quantitatively compare wall thickness of the same subject at different filling stages or among different subjects. In order to explore thickness variations at different bladder filling stages, in this study, we preliminarily investigate the relationship between bladder wall thickness and bladder volume based on a MRI database composed of 40 datasets acquired from 10 subjects at different filling stages, using a pipeline for thickness measurement and analysis proposed in our previous work. The Student’s t-test indicated that there was no significant different on wall thickness between the male group and the female group. The Pearson correlation analysis result indicated that negative correlation with a correlation coefficient of -0.8517 existed between the wall thickness and bladder volume, and the correlation was significant(p <0.01). The corresponding linear regression equation was then estimated by the unary linear regression. Compared to the absolute value of wall thickness, the z-score of wall thickness would be more appropriate to reflect the thickness variations. For possible abnormality detection of a bladder based on wall thickness, the intra-subject and inter-subject thickness variation should be considered.

Paper Details

Date Published: 20 March 2015
PDF: 7 pages
Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 94143O (20 March 2015); doi: 10.1117/12.2082490
Show Author Affiliations
Xi Zhang, Fourth Military Medical Univ. (China)
Yang Liu, Fourth Military Medical Univ. (China)
Baojuan Li, Fourth Military Medical Univ. (China)
Guopeng Zhang, Fourth Military Medical Univ. (China)
Zhengrong Liang, Stony Brook Univ. (United States)
Hongbing Lu, Fourth Military Medical Univ. (China)

Published in SPIE Proceedings Vol. 9414:
Medical Imaging 2015: Computer-Aided Diagnosis
Lubomir M. Hadjiiski; Georgia D. Tourassi, Editor(s)

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