The invasion depth measurement of bladder cancer using T2-weighted magnetic resonance imaging
| Autor: | Peng Du, Hao-jie Zheng, Yang Liu, Hongbing Lu, Xi Zhang, Ji-min Liang, Xiaopan Xu |
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| Rok vydání: | 2020 |
| Předmět: |
Invasion depth
lcsh:Medical technology Support vector machine Biomedical Engineering Feature selection computer.software_genre 030218 nuclear medicine & medical imaging Biomaterials 03 medical and health sciences 0302 clinical medicine Segmentation Voxel medicine Image Processing Computer-Assisted Humans Radiology Nuclear Medicine and imaging Neoplasm Invasiveness Mathematics Bladder cancer Radiological and Ultrasound Technology medicine.diagnostic_test business.industry Research Subtraction Magnetic resonance imaging General Medicine medicine.disease Magnetic Resonance Imaging lcsh:R855-855.5 Urinary Bladder Neoplasms 030220 oncology & carcinogenesis T2 weighted Nuclear medicine business computer |
| Zdroj: | BioMedical Engineering BioMedical Engineering OnLine, Vol 19, Iss 1, Pp 1-13 (2020) |
| ISSN: | 1475-925X |
| Popis: | Background Invasion depth is an important index for staging and clinical treatment strategy of bladder cancer (BCa). The aim of this study was to investigate the feasibility of segmenting the BCa region from bladder wall region on MRI, and quantitatively measuring the invasion depth of the tumor mass in bladder lumen for further clinical decision-making. This retrospective study involved 20 eligible patients with postoperatively pathologically confirmed BCa. It was conducted in the following steps: (1) a total of 1159 features were extracted from each voxel of both the certain cancerous and wall tissues with the T2-weighted (T2W) MRI data; (2) the support vector machine (SVM)-based recursive feature elimination (RFE) method was implemented to first select an optimal feature subset, and then develop the classification model for the precise separation of the cancerous regions; (3) after excluding the cancerous region from the bladder wall, the three-dimensional bladder wall thickness (BWT) was calculated using Laplacian method, and the invasion depth of BCa was eventually defined by the subtraction of the mean BWT excluding the cancerous region and the minimum BWT of the cancerous region. Results The segmented results showed a promising accuracy, with the mean Dice similarity coefficient of 0.921. The “soft boundary” defined by the voxels with the probabilities between 0.1 and 0.9 could demonstrate the overlapped region of cancerous and wall tissues. The invasion depth calculated from proposed segmentation method was compared with that from manual segmentation, with a mean difference of 0.277 mm. Conclusion The proposed strategy could accurately segment the BCa region, and, as the first attempt, realize the quantitative measurement of BCa invasion depth. |
| Databáze: | OpenAIRE |
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