Tissue Architecture Analysis in Prostate Cancer and Its Precursors: An Innovative Approach to Computerized Histometry

Bartels, Peter H.; Montironi, Rodolfo; Silva, V. Duval Da; Hamilton, Peter; Thompson, Deborah; Vaught, L; Bartels, Hubert G.

doi:10.1159/000019884

Cited by 13 publications

(6 citation statements)

References 3 publications

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“…Image analysis has also been employed to identify consistent changes in "normal appearing" prostate tissue adjacent to tumor [17,18]. In one study cases of prostatic adenocarcinoma was consistently detected by examining histologically normal tissue using high-resolution image cytometry [18], and in another, combined highly sensitive and discriminating Fourier transform-infrared spectroscopy with statistical analysis was used to detect damaged DNA in normal appearing prostate tissue adjacent to cancer [34]. …”

Section: Discussionmentioning

confidence: 99%

Differences in gene expression in prostate cancer, normal appearing prostate tissue adjacent to cancer and prostate tissue from cancer free organ donors

et al. 2005

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Section: Discussionmentioning

confidence: 99%

Differences in gene expression in prostate cancer, normal appearing prostate tissue adjacent to cancer and prostate tissue from cancer free organ donors

et al. 2005

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“…Utilizing this approach, a previous study demonstrated that both histologically normalappearing ovarian epithelium in women at high risk for ovarian cancer, as well as normal ovarian epithelium adjacent to ovarian cancer harbored abnormal submicroscopic changes in nuclear chromatin, thereby suggesting potential for malignancy (15). This has been similarly shown in nuclei of normal, preinvasive, and invasive lesions in other tissues including breast, rectal, urinary tract, prostate, and skin (15,(38)(39)(40)(41)(42). Taken together, these data suggest that karyometry may allow identification of changes in nuclear signature typical of early transformative events occurring during carcinogenesis, as well as serve as a surrogate endpoint for assessment of chemopreventive agents.…”

Section: Discussionmentioning

confidence: 93%

Phase II Trial of Chemopreventive Effects of Levonorgestrel on Ovarian and Fallopian Tube Epithelium in Women at High Risk for Ovarian Cancer: An NRG Oncology Group/GOG Study

Rodriguez

Kauderer

Hunn

et al. 2019

Cancer Prevention Research

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A large body of epidemiologic evidence has shown that use of progestin-containing preparations lowers ovarian cancer risk. The purpose of the current study was to gather further preclinical evidence supporting progestins as cancer chemopreventives by demonstrating progestin-activation of surrogate endpoint biomarkers pertinent to cancer prevention in the genital tract of women at increased risk of ovarian cancer. There were 64 women enrolled in a multi-institutional randomized trial who chose to undergo risk-reducing bilateral salpingo-oophorectomy (BSO) and to receive the progestin levonorgestrel or placebo for 4 to 6 weeks prior to undergoing BSO. The ovarian and fallopian tube epithelia (FTE) were compared immunohistochemically for effects of levonorgestrel on apoptosis (primary endpoint). Secondary endpoints included TGFb isoform expression, proliferation, and karyometric features of nuclear abnormality. In both the ovary and fallopian tube, levonorgestrel did not confer significant changes in apoptosis or expression of the TGFb1, 2, or 3 isoforms. In the ovarian epithelium, treatment with levonorgestrel significantly decreased the proliferation index. The mean ovarian Ki-67 value in the placebo arm was 2.027 per 100 cells versus 0.775 per 100 cells in the levonorgestrel arm (two-sided P value via Mann-Whitney U test ¼ 0.0114). The karyometric signature of nuclei in both the ovarian and FTE deviated significantly from normal controls (women at average risk of ovarian cancer), but was significantly less abnormal in women treated with levonorgestrel. These karyometric data further support the idea that progestins may clear genetically abnormal cells and act as chemopreventive agents against ovarian and fallopian tube cancer.

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“…Having identified the objects, their spatial arrangement can possess diagnostic information too, for example in specific growth pattern (granulomas, adenoid growth pattern, epidermoid cellular arrangements, etc.). These features can be analyzed by various techniques, for example by syntactic structure analysis [ 12 - 19 ]. A related graph is constructed which represents the gravity centers of the objects (nodes), a neighborhood relationship (edges), and node/edge related attributes (distances, sizes, integrated optical density, etc.).…”

Section: Theoretical Considerationsmentioning

confidence: 99%

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et al. 2006

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Aims: To develop and implement an automated virtual slide screening system that distinguishes normal histological findings and several tissue -based crude (texture -based) diagnoses. Theoretical considerations:Virtual slide technology has to handle and transfer images of GB Bytes in size. The performance of tissue based diagnosis can be separated into a) a sampling procedure to allocate the slide area containing the most significant diagnostic information, and b) the evaluation of the diagnosis obtained from the information present in the selected area. Nyquist's theorem that is broadly applied in acoustics, can also serve for quality assurance in image information analysis, especially to preset the accuracy of sampling. Texture -based diagnosis can be performed with recursive formulas that do not require a detailed segmentation procedure. The obtained results will then be transferred into a "self-learning" discrimination system that adjusts itself to changes of image parameters such as brightness, shading, or contrast.Methods: Non-overlapping compartments of the original virtual slide (image) will be chosen at random and according to Nyquist's theorem (predefined error-rate). The compartments will be standardized by local filter operations, and are subject for texture analysis. The texture analysis is performed on the basis of a recursive formula that computes the median gray value and the local noise distribution. The computations will be performed at different magnifications that are adjusted to the most frequently used objectives (*2, *4.5, *10, *20, *40). The obtained data are statistically analyzed in a hierarchical sequence, and in relation to the clinical significance of the diagnosis. Results:The system has been tested with a total of 896 lung cancer cases that include the diagnoses groups: cohort (1) normal lung -cancer; cancer subdivided: cohort (2) small cell lung cancer -non small cell lung cancer; non small cell lung cancer subdivided: cohort (3) squamous cell carcinoma -adenocarcinoma -large cell carcinoma. The system can classify all diagnoses of the cohorts (1) and (2) correctly in 100%, those of cohort (3) in more than 95%. The percentage of the selected area can be limited to only 10% of the original image without any increased error rate. Conclusion:The developed system is a fast and reliable procedure to fulfill all requirements for an automated "prescreening" of virtual slides in lung pathology.

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Tissue Architecture Analysis in Prostate Cancer and Its Precursors: An Innovative Approach to Computerized Histometry

Cited by 13 publications

References 3 publications

Differences in gene expression in prostate cancer, normal appearing prostate tissue adjacent to cancer and prostate tissue from cancer free organ donors

Differences in gene expression in prostate cancer, normal appearing prostate tissue adjacent to cancer and prostate tissue from cancer free organ donors

Phase II Trial of Chemopreventive Effects of Levonorgestrel on Ovarian and Fallopian Tube Epithelium in Women at High Risk for Ovarian Cancer: An NRG Oncology Group/GOG Study

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