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Cancer disease can take several forms, but the most dominant one among women is “Breast Cancer”, with the statistics predicting about 12% of women in the US alone will test positive for this disease. In light of this severity, the current research is based on a rigorous process of improving the breast cancer patients’ life chances which entirely depend on the disease’s early detection.
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The research has discovered the use of a linear programming in two fundamental aspects of breast cancer patient’s survival, namely,diagnosis and prognosis. These two processes are done by using of computer program software called Xcyt that analyses cellular images as opposed to the traditional lymph node examination, which has the longer term risks to the patient. The Xcyt works in three dimensional features: the examination of cellular elements using a digital scan; the scanned image’s diagnosis of the detecting benign or malignant tissues as well as accompanying probability of recurrence; and prediction of cancer recurrence in a patient.
There are three diagnostic methods of cancer: mammography, FNA with visual interpretation and surgical biopsy. These methods exhibit different levels of sensitivity. Moreover, they are intrusive and time consuming. The research has adopted the use of a linear based diagnostic by a multi-surface method’s (MSM) variant, which examines thirty dimensional feature vectors for each patient with the intention to overcome these challenges. The most crucial elements of Xcyt diagnosis are the MSM together with MINOS numerical optimization software. On the top of this, Xcyt uses Parzen window density estimation to determine the probability of malignancy in new patients.
Xcyt linear program is also applicable in the clinical offices where it produces 100% accuracy on the new cases. Actually, analysis and subsequent diagnosis of the FNA for a patient will take a few minutes and give a chance to the patient to appraise the results personally. Therefore, the patient is more informed about the methods of treatment. It is also important to note that the Xcyt program has an accuracy of 97.5% which is much better as any other system.
Another application of Xcyt is in cancer prognosis which is complicated on the following ground: the time to recur (TTR) is unknown for those patients who have not experienced cancer recurrence. Xcyt solves this problem by mapping n-dimensional input to a one dimensional output that stands for the expected time of recurrence. This time estimation is made through the linear programming, which is based on the recurrence surface of the approximation technique and is embedded on the idea of creating a surface with the limits stretching from the above Disease Free Survival (DFS) for the non-recurring training cases. Moreover, this linear program takes cognizance of the fact that the underestimated recurrent points are not as serious as just overestimated. In conclusion, a linear programming is instrumental in providing an accurate cancer diagnosis and prognosis and therefore should be adopted by many institutions.