Computer Aided Diagnosis For Bladder Cancer Based On Mr Image

Bladder cancer is a severe disease that seriously threatens the human health all over the world.《Cancer facts & figures 2009》indicates that bladder cancer is the fourth most common malignancy in men and eighth in woman, it accounts for 7% of all malignancy in men and 3% in woman all over American. This report also estimates that 70,980 new cases and 14,330 deaths are expected to occur in 2009 in American. In addition, according to the National Cancer Institute's Surveillance Epidemiology and End Result Registry (SEER), there has been a rising trend in bladder cancer incidence by approximately 40% since 1975. Bladder cancer is the eighth most common malignancy in men in China and the incidence is climbing up rapidly in some cities recently. Besides basic diagnosis based on symptom and physical exam, cystoscopy is currently the gold standard for bladder cancer diagnosis. Meanwhile, radiological imaging is often preformed in conjunction with the cystoscopy for the evaluation of malignant invasion into adjacent structures. Radiograph reading and interpretation process depends greatly on the experience of radiologists. Since the densities of image voxels inside carcinomatous tissue differ only subtly from those of the surrounding and the appearance of bladder cancer varies greatly, it is quite difficult to acquire some essential information such as degree of muscle invasion (stage of bladder cancer) directly from radiological images, which is of great importance to accurate and early diagnosis and surgical treatment planning. The aim of this study was to explore characteristic texture features that could distinguish bladder cancer form bladder wall tissue in MR images, which may help us determine the invasion depth of bladder cancer into bladder wall muscle automatically.Twenty-two consecutive patients with confirmed bladder tumors and twenty-three volunteers with fine bladders were included in this study. All the MRI data was divided into three main groups. Group A: pixels within carcinoma tissue area of patients in MRI images; group B: pixels within bladder wall area of patients in MR images; group C: pixels within bladder wall area of volunteers in MRI images. Group B was further subdivided into two small groups, B1: MRI data from patients of bladder cancer at Ta,T1 or T2 stage, whose bladder wall is thin (thirteen cases); B2: MRI data from patients of bladder cancer at T3 or T4 stage, whose bladder wall is thick (seven cases). Forty-two texture features from five categories were employed in this study. The experiment consists of four parts. In order to extract exact areas of carcinoma and normal bladder wall in MRI images, the ROIs (region of interest) were first selected manually. Second, texture features demonstrated the textures of ROIs were calculated. Third, statistical analysis was applied to results of features to reflect their significance. Fourth, texture analysis based on texture features which filtered from third step was preformed in conjunction with SVM (Support Vector Machine) which recognized patterns (textures) in MRI images. The stage of bladder cancer can be acquired by combining texture analysis with the characteristics of growth of bladder cancer.There were significant differences between group A and group B on thirty-five features, including Mean, Entropy, Uniformity, Standard deviation, Smoothness, Third moment (DG); Norm of Vector(auto-covariance coefficient); Coarseness; Contrast, Line_likeness; Roughness (Tamura features); all GLGCM features expect T6; all GLCM features expect f12,f13. Although Coarseness and Roughness showed significant differences, we still decided to cancel them because the values of these two features were greatly influenced by the size of ROI. In next step, t-test was made between the group B and group C. There were significant difference between two groups on nine features, including Entropy, Uniformity; Directionality (Tamura features); T5, T6, T9 (GLGCM features); f1, f9, f12 (GLCM features). Thirdly, analysis of variance was made among group B1, B2 and C. Features that shown significant differences in first test were employed in this step (Thirty-three features). There were significant differences between group B2 and C on fifteen features, including Entropy, Uniformity, Standard deviation, Line_likeness, T3~T5,T7~T9, f1,f8~f11(most features were same as the results of second t-test), while twenty-six features didn't showed significant differences between group B1 and C, including Mean, Smoothness, Standard deviation, Norm of Vector, Contrast, Line_likeness, T1~T4,T7,T8,T10,T11,f2~f8,f10,f11,f14~f16. There were significant differences between group B2 and C on nine features, including Standard deviation, Line_likeness, T3, T4, T7, T8, f8, f10, f11, while there were no significant differences between group B1 and C on these features. On the contrary, only one features, i.e. the Third moment, showed an opposite statistical result between the two comparisons. Fourthly, we utilized texture features filtered from the third step, SVM and the characteristic of growth of cancer to determine the depth of tumor invasion(stage) into the bladder wall., Twelve(75%) of sixteen stage of tumors determined by pathology examine recognized by our method.The preliminary results obtained in this study indicates that there are statistically significant differences existed in thirty-three texture features extracted from MR images between bladder cancer tissue and bladder wall tissue of patients. Additionally, the statistical results demonstrate that bladder wall tissue of patients differ from the bladder wall tissue of volunteers. The results of analysis of variance indicate that tissue of bladder wall of early stage of bladder cancer is different from the tissue of advanced stage. Moreover, tissue of bladder wall of early stage of bladder cancer is more similar to the tissue of bladder wall of volunteers. These observations indicate that with the development of tumor angiogenesis and fibrosis in smooth muscle of bladder wall, the patterns of texture feature in MR images change in patients with advanced stage of bladder cancer. The most important conclusion is that the stage of bladder cancer is quite possibly be identified by using the proposed method describe in this study.