Statistical 3D Distribution Analysis of Prostate Cancers in Korean Using Digital Processing Techniques
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Objectives: Several researchers have shown that three dimensional (3D) distribution analysis of prostate cancer is helpful when initiating needle biopsy procedures. Knowledge regarding the distribution of prostate cancer could enhance understanding of the pathophysiology involved and improve detection of these malignancies. We propose utilizing digital processing techniques to analyze prostate cancer distribution in a 3D setting. Methods: Pre-made radical prostatectomy sample slices were digitized with a resolution of 76 dpi. Slices of each sample were aligned and registered by deformation algorithm and interpolated for analysis of relative distribution statistics. We analyzed 80 samples saved in electronic medical record and compared the detection rate of preoperative needle biopsies and radical prostatectomies using our 3D analysis technique. Results: The statistical 3D distribution of prostate cancer was evaluated using a 36-sector process. Results were represented in the following two ways: distribution of a single patient, and statistical distribution of prostate cancers of multiple patients. The overall concordance rate was 62.7% between the two methods; therefore a technique is needed which can raise this percentage. Conclusions: We suggest using the normalization method to develop a software tool which permits reconstruction of the 3D distribution of prostate cancer from 2D legacy images and reduces the loss of image quality as well. This application will facilitate detection of prostate cancer by aiding in the determination of the most effective clinical position via partial sampling with decreased patient inconvenience.
Abstract
Objectives: Several researchers have shown that three dimensional (3D) distribution analysis of prostate cancer is helpful when initiating needle biopsy procedures. Knowledge regarding the distribution of prostate cancer could enhance understanding of the pathophysiology involved and improve detection of these malignancies. We propose utilizing digital processing techniques to analyze prostate cancer distribution in a 3D setting. Methods: Pre-made radical prostatectomy sample slices were digitized with a resolution of 76 dpi. Slices of each sample were aligned and registered by deformation algorithm and interpolated for analysis of relative distribution statistics. We analyzed 80 samples saved in electronic medical record and compared the detection rate of preoperative needle biopsies and radical prostatectomies using our 3D analysis technique. Results: The statistical 3D distribution of prostate cancer was evaluated using a 36-sector process. Results were represented in the following two ways: distribution of a single patient, and statistical distribution of prostate cancers of multiple patients. The overall concordance rate was 62.7% between the two methods; therefore a technique is needed which can raise this percentage. Conclusions: We suggest using the normalization method to develop a software tool which permits reconstruction of the 3D distribution of prostate cancer from 2D legacy images and reduces the loss of image quality as well. This application will facilitate detection of prostate cancer by aiding in the determination of the most effective clinical position via partial sampling with decreased patient inconvenience.
참고문헌 (16)
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