Computerized characterization of prostate cancer by fractal analysis in MR images
Dongjiao Lv PhD
Department of Biomedical Engineering, Peking University, Beijing, China, People's Republic of China
Center for Functional Imaging, Peking University, Beijing, China, People's Republic of China
Search for more papers by this authorXuemei Guo MD
Center for Functional Imaging, Peking University, Beijing, China, People's Republic of China
Department of Radiology, Peking University First Hospital, Beijing, China, People's Republic of China
Search for more papers by this authorCorresponding Author
Xiaoying Wang MD
Center for Functional Imaging, Peking University, Beijing, China, People's Republic of China
Department of Radiology, Peking University First Hospital, Beijing, China, People's Republic of China
Xiaoying Wang, 8 Xishiku Street, Xicheng District, Beijing China, 100034
Jue Zhang, Department of Biomedical Engineering & Center for Functional Imaging, Peking University, Yiheyuan Road No. 5, Beijing, 100871, China
Search for more papers by this authorCorresponding Author
Jue Zhang PhD
Department of Biomedical Engineering, Peking University, Beijing, China, People's Republic of China
Center for Functional Imaging, Peking University, Beijing, China, People's Republic of China
Xiaoying Wang, 8 Xishiku Street, Xicheng District, Beijing China, 100034
Jue Zhang, Department of Biomedical Engineering & Center for Functional Imaging, Peking University, Yiheyuan Road No. 5, Beijing, 100871, China
Search for more papers by this authorJing Fang PhD
Department of Biomedical Engineering, Peking University, Beijing, China, People's Republic of China
Center for Functional Imaging, Peking University, Beijing, China, People's Republic of China
Search for more papers by this authorDongjiao Lv PhD
Department of Biomedical Engineering, Peking University, Beijing, China, People's Republic of China
Center for Functional Imaging, Peking University, Beijing, China, People's Republic of China
Search for more papers by this authorXuemei Guo MD
Center for Functional Imaging, Peking University, Beijing, China, People's Republic of China
Department of Radiology, Peking University First Hospital, Beijing, China, People's Republic of China
Search for more papers by this authorCorresponding Author
Xiaoying Wang MD
Center for Functional Imaging, Peking University, Beijing, China, People's Republic of China
Department of Radiology, Peking University First Hospital, Beijing, China, People's Republic of China
Xiaoying Wang, 8 Xishiku Street, Xicheng District, Beijing China, 100034
Jue Zhang, Department of Biomedical Engineering & Center for Functional Imaging, Peking University, Yiheyuan Road No. 5, Beijing, 100871, China
Search for more papers by this authorCorresponding Author
Jue Zhang PhD
Department of Biomedical Engineering, Peking University, Beijing, China, People's Republic of China
Center for Functional Imaging, Peking University, Beijing, China, People's Republic of China
Xiaoying Wang, 8 Xishiku Street, Xicheng District, Beijing China, 100034
Jue Zhang, Department of Biomedical Engineering & Center for Functional Imaging, Peking University, Yiheyuan Road No. 5, Beijing, 100871, China
Search for more papers by this authorJing Fang PhD
Department of Biomedical Engineering, Peking University, Beijing, China, People's Republic of China
Center for Functional Imaging, Peking University, Beijing, China, People's Republic of China
Search for more papers by this authorAbstract
Purpose
To explore the potential of computerized characterization of prostate MR images by extracting the fractal features of texture and intensity distributions as indices in the differential diagnosis of prostate cancer.
Materials and Methods
MR T2-weighted images (T2WI) of 55 patients with pathologic results detected by ultrasound guided biopsy were collected and then divided in two groups, 27 with prostate cancer (PCa) and 28 with no histological abnormality. Texture fractal dimension (TFD) and histogram fractal dimension (HFD) were calculated to analyze complexity features of regions of Interest (ROIs) selected from the peripheral zone. Two-sample t-tests were performed to evaluate group differences for both parameters. Receiver operating characteristic (ROC) analysis was used to estimate the performance of TFD and HFD for discriminating PCa.
Results
Significant differences were found in both TFD and HFD between the two patient groups. The areas under the ROC curves of TFD and HFD were 0.691 and 0.966, respectively, in distinguishing prostatic carcinoma from normal peripheral zone. As characterized by the fractal indices, cancerous prostatic tissue exhibited smoother texture and lower variation in intensity distribution than normal prostatic tissue.
Conclusion
The study suggests that TFD and HFD depict the changes in texture and intensity distribution associated with prostate cancer on T2WI. Both TFD and HFDprovide promising quantitative indices for cancer identification. HFD performs better than TFD offering a more robust MR-based indicator in the diagnosis of prostatic carcinoma. J. Magn. Reson. Imaging 2009;30:161–168. © 2009 Wiley-Liss, Inc.
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