Live Logic TM: Method for Approximate Knowledge Discovery and Decision Making

Abstract. Live Logic is an integrated approach for support of the learning and decision making in conditions of uncertainty. The approach covers both induction of probabilistic logical hypotheses from known examples and deduction of the plausible solution for an unknown case based on the inducted hypotheses. The induction method generalizes empirical data, discovering statistical patterns, expressed in logical language. The deduction method uses multidimensional ranking to reconcile contradictory patterns exhibited by a particular case. The method was applied on clinical data of the patients with prostate cancer who underwent prostatectomy. The goal was to predict biochemical failure based on the pre- and post- operative status of the patient. The patterns found by the method proved to be insightful from the pathologist’s point of view. Most of them had been confirmed on the control dataset. In our experiments, the predictive accuracy of the Live Logic TM was also higher than that of other tested methods.

58 Automated Prostate Cancer Diagnosis and Gleason Grading of Tissue Microarrays

We present the results on the development of an automated system for prostate cancer diagnosis and Gleason grading. Images of representative areas of the original Hematoxylin-and-Eosin (H&E)-stained tissue retrieved from each patient, either from a tissue microarray (TMA) core or whole section, were captured and analyzed. The image sets consisted of 367 and 268 color images for the diagnosis and Gleason grading problems, respectively. In diagnosis, the goal is to classify a tissue image into tumor versus non-tumor classes. In Gleason grading, which characterizes tumor aggressiveness, the objective is to classify a tissue image as being from either a low- or high-grade tumor. Several feature sets were computed from the image. The feature sets considered were: (i) color channel histograms, (ii) fractal dimension features, (iii) fractal code features, (iv) wavelet features, and (v) color, shape and texture features computed using Aureon Biosciences ' MAGIC ™ system. The linear and quadratic Gaussian classifiers together with a greedy search feature selection algorithm were used. For cancer diagnosis, a classification accuracy of 94.5 % was obtained on an independent test set. For Gleason grading, the achieved accuracy of classification into low- and high-grade classes of an independent test set was 77.6%

Improved Prediction Of Prostate Cancer Recurrence Based On An

Prostate tissue characteristics play an important role in predicting the recurrence of prostate cancer. Currently, experienced pathologists manually grade these prostate tissues using the Gleason scoring system, a subjective approach which summarizes the overall progression and aggressiveness of the cancer. Using advanced image processing techniques, Aureon Biosciences Corporation has developed a proprietary image analysis system (MAGIC TM ), which here is specifically applied to prostate tissue analysis and designed to be capable of processing a single prostate tissue Hematoxylin-and-Eosin (H&E) stained image and automatically extracting a variety of raw measurements (spectral, shape, etc.) of histopathological objects along with spatial relationships amongst them. In the context of predicting prostate cancer recurrence, the performance of the image features is comparable to that achieved using the Gleason scoring system. Moreover, an improved prediction rate is observed by combining the Gleason scores with the image features obtained using MAGIC^TM, suggesting that the image data itself may possess information complementary to that of Gleason scores

Improved prediction of prostate cancer recurrence through systems pathology

We have developed an integrated, multidisciplinary methodology, termed systems pathology, to generate highly accurate predictive tools for complex diseases, using prostate cancer for the prototype. To predict the recurrence of prostate cancer following radical prostatectomy, defined by rising serum prostate-specific antigen (PSA), we used machine learning to develop a model based on clinicopathologic variables, histologic tumor characteristics, and cell type–specific quantification of biomarkers. The initial study was based on a cohort of 323 patients and identified that high levels of the androgen receptor, as detected by immunohistochemistry, were associated with a reduced time to PSA recurrence. The model predicted recurrence with high accuracy, as indicated by a concordance index in the validation set of 0.82, sensitivity of 96%, and specificity of 72%. We extended this approach, employing quantitative multiplex immunofluorescence, on an expanded cohort of 682 patients. The model again predicted PSA recurrence with high accuracy, concordance index being 0.77, sensitivity of 77% and specificity of 72%. The androgen receptor was selected, along with 5 clinicopathologic features (seminal vesicle invasion, biopsy Gleason score, extracapsular extension, preoperative PSA, and dominant prostatectomy Gleason grade) as well as 2 histologic features (texture of epithelial nuclei and cytoplasm in tumor only regions). This robust platform has broad applications in patient diagnosis, treatment management, and prognostication