Statistical modeling and visualization of localized prostate cancer

Yue Joseph Wang; Jianhua Xuan; Isabell A. Sesterhenn; Wendelin S. Hayes; David S. Ebert; John H. Lynch; Seong Ki Mun

doi:10.1117/12.273958

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7 May 1997 Statistical modeling and visualization of localized prostate cancer

Yue Joseph Wang, Jianhua Xuan, Isabell A. Sesterhenn, Wendelin S. Hayes, David S. Ebert, John H. Lynch, Seong Ki Mun

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Proceedings Volume 3031, Medical Imaging 1997: Image Display; (1997) https://doi.org/10.1117/12.273958
Event: Medical Imaging 1997, 1997, Newport Beach, CA, United States

Abstract

In this paper, a statistically significant master model of localized prostate cancer is developed with pathologically- proven surgical specimens to spatially guide specific points in the biopsy technique for a higher rate of prostate cancer detection and the best possible representation of tumor grade and extension. Based on 200 surgical specimens of the prostates, we have developed a surface reconstruction technique to interactively visualize in the clinically significant objects of interest such as the prostate capsule, urethra, seminal vesicles, ejaculatory ducts and the different carcinomas, for each of these cases. In order to investigate the complex disease pattern including the tumor distribution, volume, and multicentricity, we created a statistically significant master model of localized prostate cancer by fusing these reconstructed computer models together, followed by a quantitative formulation of the 3D finite mixture distribution. Based on the reconstructed prostate capsule and internal structures, we have developed a technique to align all surgical specimens through elastic matching. By labeling the voxels of localized prostate cancer by '1' and the voxels of other internal structures by '0', we can generate a 3D binary image of the prostate that is simply a mutually exclusive random sampling of the underlying distribution f cancer to gram of localized prostate cancer characteristics. In order to quantify the key parameters such as distribution, multicentricity, and volume, we used a finite generalized Gaussian mixture to model the histogram, and estimate the parameter values through information theoretical criteria and a probabilistic self-organizing mixture. Utilizing minimally-immersive and stereoscopic interactive visualization, an augmented reality can be developed to allow the physician to virtually hold the master model in one hand and use the dominant hand to probe data values and perform a simulated needle biopsy. An adaptive self- organizing vector quantization method is developed to determine the optimal locations of selective biopsies where maximum likelihood of cancer detection and the best possible representation of tumor grade and extension can be achieved theoretically, thus allowing a comprehensive analysis of pathological information. The preliminary results show that a statistical pattern of localized prostate cancer exists, and a better understanding of disease patterns associated with tumor volume, distribution, and multicentricity of prostate carcinoma can be obtained from the computerized master model.

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Yue Joseph Wang, Jianhua Xuan, Isabell A. Sesterhenn, Wendelin S. Hayes, David S. Ebert, John H. Lynch, and Seong Ki Mun "Statistical modeling and visualization of localized prostate cancer", Proc. SPIE 3031, Medical Imaging 1997: Image Display, (7 May 1997); https://doi.org/10.1117/12.273958

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