Automatic Diagnosis for Prostate Cancer Using Run-Length Matrix Method

Prostate cancer is the most common type of cancer and the second leading cause of cancer death among men in US1. Quantitative assessment of prostate histology provides potential automatic classification of prostate lesions and prediction of response to therapy. Traditionally, prostate cancer diagnosis is made by the analysis of prostate-specific antigen (PSA) levels and histopathological images of biopsy samples under microscopes. In this application, we utilize a texture analysis method based on the run-length matrix for identifying tissue abnormalities in prostate histology. A tissue sample was collected from a radical prostatectomy, H&E fixed, and assessed by a pathologist as normal tissue or prostatic carcinoma (PCa). The sample was then subsequently digitized at 50X magnification. We divided the digitized image into sub-regions of 20 X 20 pixels and classified each sub-region as normal or PCa by a texture analysis method. In the texture analysis, we computed texture features for each of the sub-regions based on the Gray-level Run-length Matrix(GL-RLM). Those features include LGRE, HGRE and RPC from the run-length matrix, mean and standard deviation of the pixel intensity. We utilized a feature selection algorithm to select a set of effective features and used a multi-layer perceptron (MLP) classifier to distinguish normal from PCa. In total, the whole histological image was divided into 42 PCa and 6280 normal regions. Three-fold cross validation results show that the proposed method achieves an average classification accuracy of 89.5% with a sensitivity and specificity of 90.48% and 89.49%, respectively

Process-induced extracellular matrix alterations affect the mechanisms of soft tissue repair and regeneration

Extracellular matrices derived from animal tissues for human tissue repairs are processed by various methods of physical, chemical, or enzymatic decellularization, viral inactivation, and terminal sterilization. The mechanisms of action in tissue repair vary among bioscaffolds and are suggested to be associated with process-induced extracellular matrix modifications. We compared three non-cross-linked, commercially available extracellular matrix scaffolds (Strattice, Veritas, and XenMatrix), and correlated extracellular matrix alterations to in vivo biological responses upon implantation in non-human primates. Structural evaluation showed significant differences in retaining native tissue extracellular matrix histology and ultrastructural features among bioscaffolds. Tissue processing may cause both the condensation of collagen fibers and fragmentation or separation of collagen bundles. Calorimetric analysis showed significant differences in the stability of bioscaffolds. The intrinsic denaturation temperature was measured to be 51°C, 38°C, and 44°C for Strattice, Veritas, and XenMatrix, respectively, demonstrating more extracellular matrix modifications in the Veritas and XenMatrix scaffolds. Consequently, the susceptibility to collagenase degradation was increased in Veritas and XenMatrix when compared to their respective source tissues. Using a non-human primate model, three bioscaffolds were found to elicit different biological responses, have distinct mechanisms of action, and yield various outcomes of tissue repair. Strattice permitted cell repopulation and was remodeled over 6 months. Veritas was unstable at body temperature, resulting in rapid absorption with moderate inflammation. XenMatrix caused severe inflammation and sustained immune reactions. This study demonstrates that extracellular matrix alterations significantly affect biological responses in soft tissue repair and regeneration. The data offer useful insights into the rational design of extracellular matrix products and bioscaffolds of tissue engineering