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probability parametric imaging with DCE-MRI data to identify prostate cancer in the peripheral zone

Progress in Natural Science:Materials International 2006年第9期16卷 948-953页

作者： HUANG Xinrui, WANG Qiang, XU Yufeng, WANG Xiaoying and BAO Shanglian ( The Key Laboratory of Medical Physics and Engineering, Peking University, Beijing 100871, China The Medical School of Peking University, Beijing 100083, China 3. The First Hospital of Peking University, Beijing 100034, China) The Key Laboratory of Medical Physics and Engineering Peking University Beijing 100871 China The Medical School of Peking University Beijing 100083 China The First Hospital of Peking University Beijing 100034 China

To quantitatively analyze prostate dynamic contrast-enhanced magnetic resonance image. (DCE-MRI), a retrospective study was carried out to construct a mathematical model to predict whether a lesion is cancerous or not. With known diagnosis of 52 re-gions-of-interest (10 malignant, 26 benign and 16 normal) in peripheral zone (PZ) of prostate from 18 patients undergoing needle biopsy and 8 normal volunteers, we combined feature parameters (k1, k2 and tp) from the linear-slope model with logistic regression to determine the statistical probability model. Prostate cancerous probability maps were derived using the model on each subject to examine the correlation between the model-based prediction and the known diagnosis. Compared the model prediction results of 52 regions-of-interest with the histopathologic results, the total agreement rate was 90.4 % ; the agreement rate was 60 % for the malignant and 97. 6 % for the nonmalignant. The diagnostic results only referencing the probability maps by two radiologists showed a high agreement in tissue characteristics and lesion location with the combining results from other diagnostic techniques such as PSA (prostate specific antigen) level and MRS (magnetic resonance spectroscopy) .

关键词： time-intensity curve prostate DCE-MRI logistic regression probability parametric image.

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