Stem cells are defined as cells able to both extensively self-renew and differentiate into progenitors. Research data show that more resistant stem cells than common cancer cells exist in cancer patients.To identify unrecognized differences between cancer stem cells and cancer cells might be able to develope effective classification,diagnose and treat ment for cancer.

Stem Cells,Cancer and Cancer Stem Cells

Prognostic factors are clinical and pathological features that give information in estimating the likely clinical outcome of an individual suffering from cancer. The author gives a short review of the most important prognostic factors in breast cancer. 376 breast cancer cases of a ten year interval in a county hospital are summarized. Traditional clinico-pathological parameters i.e. TNM and steroid receptor status are discussed. The more common karyotipic, oncogene and tumor suppressor gene alterations are outlined in the study. Methods for their detection are presented and their value in prognostication is reviewed. Emphasis was laid on steroid receptors, c-erpB-2, p53 and bcl-2 alterations. Genes responsible for heritable forms of increased breast cancer risk are briefly reviewed.

Prognostic factors in breast cancer

Tamoxifen for early breast cancer: An overview of the randomised trials

Automated defect inspection has long been a challenging task especially in industrial applications, where collecting and labeling large amounts of defective samples are usually harsh and impracticable. In this paper, we propose an approach to detect and localize defects with only defect-free samples for model training. This approach is carried out by reconstructing image patches with convolutional denoising autoencoder networks at different Gaussian pyramid levels, and synthesizing detection results from these different resolution channels. Reconstruction residuals of the training patches are used as the indicator for direct pixelwise defect prediction, and the reconstruction residual map generated in each channel is combined to generate the final inspection result. This novel method has two prominent characteristics, which benefit the implementation of automatic defect inspection in practice. First, it is absolutely unsupervised that no human intervention is needed throughout the inspection process. Second, multimodal strategy is utilized in this method to synthesize results from multiple pyramid levels. This strategy is capable of improving the robustness and accuracy of the method. To evaluate this approach, experiments on convergence, noise immunity, and defect inspection accuracy are conducted. Furthermore, comparative tests with some excellent algorithms on actual and simulated data sets are performed. Experimental results demonstrated the effectiveness and superiority of the proposed method on homogeneous and nonregular textured surfaces.

An Unsupervised-Learning-Based Approach for Automated Defect Inspection on Textured Surfaces

Visual inspection of texture surface defects is still a challenging task in the industrial automation field due to the tremendous changes in the appearance of various surface textures. Current visual inspection methods cannot simultaneously and efficiently inspect various types of texture defects due to either the low discriminative capabilities of handcrafted features or their time-consuming sliding-window strategy. In this paper, we present a novel unsupervised multiscale feature-clustering-based fully convolutional autoencoder (MS-FCAE) method that efficiently and accurately inspects various types of texture defects based on a small number of defect-free texture samples. The proposed MS-FCAE method utilizes multiple FCAE subnetworks at different scale levels to reconstruct several textured background images. The residual images are obtained by subtracting these texture backgrounds from the input image individually; then, they are fused into one defect image. To maximize the efficiency, each FCAE subnetwork utilizes fully convolutional neural networks to extract the original feature maps directly from the input images. Meanwhile, each FCAE subnetwork performs feature clustering to improve the discriminant power of the encoded feature maps. The proposed MS-FCAE method is evaluated on several texture surface inspection data sets both qualitatively and quantitatively. This method achieves a Precision of 92.0% while requiring only 82 ms for input images of $1920\times 1080$ pixels. The extensive experimental results demonstrate that MS-FCAE achieves highly efficient and state-of-the-art inspection accuracy. Note to Practitioners —Most conventional visual inspection methods can address only one specific type of texture defect, while multiscale feature-clustering-based fully convolutional autoencoder (MS-FCAE) can simultaneously and accurately inspect various types of texture surface defects, such as those of thin-film transistor liquid crystal displays, wood, fabrics, and ceramic tiles. Furthermore, MS-FCAE requires only a small number of surface texture samples to learn a robust network model, and its training requires no defect samples. This is extremely important for industrial applications because identifying and labeling defect samples is difficult. Moreover, MS-FCAE can be applied to online visual inspection utilizing a graphics processing unit-based parallel processing strategy.

Multiscale Feature-Clustering-Based Fully Convolutional Autoencoder for Fast Accurate Visual Inspection of Texture Surface Defects

Owing to exceptional heterogeneity in the outcome of invasive breast cancer it is essential to develop highly accurate prognostic tools for effective therapeutic management. Based on this pressing need, we aimed to improve breast cancer prognosis by exploring the prognostic value of tumor histology image analysis. Patient group (n=78) selection was based on invasive breast cancer diagnosis without systemic treatment with a median follow-up of 147 months. Gray-level co-occurrence matrix texture analysis was performed retrospectively on primary tumor tissue section digital images stained either nonspecifically with hematoxylin and eosin or specifically with a pan-cytokeratin antibody cocktail for epithelial malignant cells. Univariate analysis revealed stronger association with metastasis risk by texture analysis when compared with clinicopathological parameters. The combination of individual clinicopathological and texture variables into composite scores resulted in further powerful enhancement of prognostic performance, with an accuracy of up to 90%, discrimination efficiency by the area under the curve [95% confidence interval (CI)] of 0.94 (0.87-0.99) and hazard ratio (95% CI) of 20.1 (7.5-109.4). Internal validation was successfully performed by bootstrap and split-sample cross-validation, suggesting that the models are generalizable. Whereas further validation is needed on an external set of patients, this preliminary study indicates the potential use of primary breast tumor histology texture as a highly accurate, simple, and cost-effective prognostic indicator of distant metastasis risk.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S1431927615000379

Gray-Level Co-Occurrence Matrix Texture Analysis of Breast Tumor Images in Prognosis of Distant Metastasis Risk.

Background Cancer progression and metastasis are complex processes, dependent of molecules involved in inflammation, degradation and invasion. These molecules can be used as prognostic indicators to single out patients with higher risk of recurrence. Interleukin-8 (IL-8) has a role in inflammation, urokinase plasminogen activator (uPA), plasminogen activator inhibitor type-1 (PAI-1) and matrix metalloproteinase-2, -9 have a decisive part in the process of degradation and invasion, while vascular endothelial growth factor (VEGF) is consequential for angiogenesis. Objectives Aim of our study is to determine relations between IL-8, uPA, PAI-1, MMP-2, -9, VEGF as their prognostic significance in terms of recurrence free survival. Methods This study included 91 untreated patients with lymph node negative (N0) primary breast cancer. Results Patients with higher levels of uPA (p= 0.05), PAI-1 (0.05), MMP2 (p= 0.05) and IL-8 (p= 0.02) have a poor prognosis. Positive correlations were found between ER - PR, uPA - PAI-1, uPA - MMP9, PAI-1 - IL-8, MMP9 - IL-8, MMP9 - VEGF. Negative correlations were found between ER - IL-8, uPA - IL-8, MMP2 - VEGF. Conclusions Higher concentrations of IL-8, uPA, PAI-1 and MMP2, as is MMP9 and VEGF, confirmed aggressive phenotype and poor prognosis in different subgroups.

Markers of progression and invasion in short term follow up of untreated breast cancer patients.

Summary
The purpose of this study was to improve the prognostic value of tumour histopathology image analysis methodology by image preprocessing

Key image qualities were modified including contrast, sharpness and brightness The texture information was subsequently extracted from images of haematoxylin/eosin-stained tumour tissue sections by GLCM, monofractal and multifractal algorithms without any analytical limitation to predefined structures Images were derived from patient groups with invasive breast carcinoma (BC, 93 patients) and inflammatory breast carcinoma (IBC, 51 patients)

The prognostic performance was indeed significantly enhanced by preprocessing with the average AUCs of individual texture features improving from 068 ± 005 for original to 078 ± 001 for preprocessed images in the BC group and 075 ± 001 to 080 ± 002 in the IBC group Image preprocessing also improved the prognostic independence of texture features as indicated by multivariate analysis Surprisingly, the tonal histogram compression by the nonnormalisation preprocessing has prognostically outperformed the tested contrast normalisation algorithms Generally, features without prognostic value showed higher susceptibility to prognostic enhancement by preprocessing whereas IDM texture feature was exceptionally susceptible The obtained results are suggestive of the existence of distinct texture prognostic clues in the two examined types of breast cancer The obtained enhancement of prognostic performance is essential for the anticipated clinical use of this method as a simple and cost-effective prognosticator of cancer outcome

Effects of different preprocessing algorithms on the prognostic value of breast tumour microscopic images

We evaluated urokinase-type plasminogen activator (uPA) and plasminogen activator inhibitor-1 (PAI-1) prognostic value in postmenopausal, node-negative breast cancer patients bearing tumors with estrogen receptor (ER)/progesterone receptor (PR) expression, treated with locoregional therapy alone, within an early follow-up. We focused our analysis on tumors of histological grade II in order to improve its prognostic value and, consequently, to improve a decision-making process. The cytosol extracts of 73 tumor samples were used for assessing several biomarkers. ER and PR levels were measured by classical biochemical method. Cathepsin D was assayed by a radiometric immunoassay while both uPA and PAI-1 level determinations were performed by enzyme-linked immunosorbent assays. HER-2 gene amplification was determined by chromogenic in situ hybridization (CISH) in primary tumor tissue. Patients bearing tumors smaller than or equal to 2 cm (pT1) or those with low PAI-1 levels (PAI-1 < 6.35 pg/mg) showed favorable outcome compared to patients bearing tumors greater than 2 cm (pT2,3) or those with high PAI-1 levels, respectively. Analyses of 4 phenotypes, defined by tumor size and PAI-1 status, revealed that patients bearing either pT1 tumors, irrespective of PAI-1 levels, or pT2,3 tumors with low PAI-1 levels, had similar disease-free interval probabilities and showed favorable outcome compared to those bearing pT2,3 tumors with high PAI-1 levels. Our findings suggest that tumor size and PAI-1, used in combination as phenotypes are not only prognostic but might also be predictive in node-negative, postmenopausal breast cancer patients bearing histological grade II tumors with ER/PR expression, during an early follow-up period.

Potential clinical relevance of uPA and PAI-1 levels in node-negative, postmenopausal breast cancer patients bearing histological grade II tumors with ER/PR expression, during an early follow-up

The time-dependent prognostic value of intratumoral cytokine expression profiles in a natural course of primary breast cancer with a long-term follow-up

Tijana Vujasinović

Papers

Gray-Level Co-Occurrence Matrix Texture Analysis of Breast Tumor Images in Prognosis of Distant Metastasis Risk.

Markers of progression and invasion in short term follow up of untreated breast cancer patients.

Effects of different preprocessing algorithms on the prognostic value of breast tumour microscopic images

Potential clinical relevance of uPA and PAI-1 levels in node-negative, postmenopausal breast cancer patients bearing histological grade II tumors with ER/PR expression, during an early follow-up

The time-dependent prognostic value of intratumoral cytokine expression profiles in a natural course of primary breast cancer with a long-term follow-up