Characterization of Texture Features of Bladder Carcinoma and the Bladder Wall on MRI

Abstract

Rationale and Objectives The purpose of this study was to determine textural features that show a significant difference between carcinomatous tissue and the bladder wall on magnetic resonance imaging (MRI) and explore the feasibility of using them to differentiate malignancy from the normal bladder wall as an initial step for establishing MRI as a screening modality for the noninvasive diagnosis of bladder cancer. Materials and Methods Regions of interest (ROIs) were manually placed on foci of bladder cancer and uninvolved bladder wall in 22 patients and on the normal bladder wall of 23 volunteers to calculate 40 known textural features. Statistical analysis was applied to determine the difference in these features in bladder cancer versus uninvolved bladder wall versus normal bladder wall of volunteers. The significantly different features were then analyzed using a support vector machine (SVM) classifier to determine their accuracy in differentiating malignancy from the bladder wall. Results Thirty-three of 40 features show significant differences between bladder cancer and the bladder wall. Nine of 40 features were significantly different in uninvolved bladder wall of patients versus normal bladder wall of volunteers. Further study indicates that seven of these 33 features were significantly different between uninvolved bladder wall of patients with early cancer and that of volunteers, whereas 15 of 33 features were different between that of patients with advanced cancer and normal wall. With the testing dataset consisting of ROIs acquired from patients, the classification accuracy using 33 textural features fed into the SVM classifier was 86.97%. Conclusion The initial experience demonstrates that texture features are sensitive to reveal the differences between bladder cancer and the bladder wall on MRI. The different features can be used to develop a computer-aided system for the evaluation of the entire bladder wall.

Motion Correction for MR Cystography by an Image Processing Approach

“…Given the extracted bladder wall by the use of the anatomic MR imaging technology, other functional imaging modalities, such as diffusion-weighted imaging, contrast-enhanced imaging, etc ., can be utilized to provide tissue contrast information inside the wall for functional studies and pathological assessment [40]. …”

“…1(b) [23]. Since T 2 -weighted images provide good pathological information about the tumor invading in the wall, it would be useful for wall analysis after the bladder extraction [40]. In our experiment, both T 1 and T 2 images were acquired without exogenetic contrast medium, i.e.…”

MRI Texture Analysis Predicts p53 Status in Head and Neck Squamous Cell Carcinoma

“…27 Other studies have shown that normal liver tissue could be differentiated from focal liver lesions with accuracies up to 88% 39 and that normal bladder wall could be differentiated from bladder cancer with an accuracy of 87.0%. 40 We found that texture features of the Post-Gad T1WI, T2WI, and ADC map were influential in the analysis. A possible physiologic explanation for this finding is the differences in vascularity between p53-positive and p53-negative tumors.…”

α-Information-Based Registration of Dynamic Scans for Magnetic Resonance Cystography

“…Other MR scanning protocols or pulse sequences shall be examined, e.g. T 2 –weighted MR imaging modality, as additional information to improve the wall extraction and perform clinical wall evaluation for tumor detection and staging quantification [37]. …”