The first BGICC consensus and recommendations for breast cancer awareness, early detection and risk reduction in low- and middle-income countries and the MENA region.

In low-middle income countries (LMICs) and the Middle East and North Africa (MENA) region, there is an unmet need to establish and improve breast cancer (BC) awareness, early diagnosis and risk reduction programs. During the 12th Breast, Gynecological & Immuno-oncology International Cancer Conference - Egypt 2020, 26 experts from 7 countries worldwide voted to establish the first consensus for BC awareness, early detection and risk reduction in LMICs/MENA region. The panel advised that there is an extreme necessity for a well-developed BC data registries and prospective clinical studies that address alternative modalities/modified BC screening programs in areas of limited resources. The most important recommendations of the panel were: (a) BC awareness campaigns should be promoted to public and all adult age groups; (b) early detection programs should combine geographically distributed mammographic facilities with clinical breast examination (CBE); (c) breast awareness should be encouraged; and (d) intensive surveillance and chemoprevention strategies should be fostered for high-risk women. The panel defined some areas for future clinical research, which included the role of CBE and breast self-examination as an alternative to radiological screening in areas of limited resources, the interval and methodology of BC surveillance in women with increased risk of BC and the use of low dose tamoxifen in BC risk reduction. In LMICs/MENA region, BC awareness and early detection campaigns should take into consideration the specific disease criteria and the socioeconomic status of the target population. The statements with no consensus reached should serve as potential catalyst for future clinical research.

Multistage classification and recognition that employs vector quantization coding and criteria extracted from nonorthogonal and preprocessed signal representations.

Classification decision tree algorithms have recently been used in pattern-recognition problems. In this paper, we propose a self-designing system that uses the classification tree algorithms and that is capable of recognizing a large number of signals. Preprocessing techniques are used to make the recognition process more effective. A combination of the original, as well as the preprocessed, signals is projected into different transform domains. Enormous sets of criteria that characterize the signals can be developed from the signal representations in these domains. At each node of the classification tree, an appropriately selected criterion is optimized with respect to desirable performance features such as complexity and noise immunity. The criterion is then employed in conjunction with a vector quantizer to divide the signals presented at a particular node in that stage into two approximately equal groups. When the process is complete, each signal is represented by a unique composite binary word index, which corresponds to the signal path through the tree, from the input to one of the terminal nodes of the tree. Experimental results verify the excellent classification accuracy of this system. High performance is maintained for both noisy and corrupt data.