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Breast cancer is most common tumour diagnosis for women worldwide. Over the last almost 40 years widespread adoption of mammographic screening has established Breast Conserving Surgery (BCS) followed by irradiation as the most practised treatment of choice. However, in absence of tools to determine the optimal quantum of tissue to be excised the debate continues for achieving a balance between the effectiveness of surgical intervention and the later stage personalisation of treatment, and so, a wide variation in practice is a common phenomena globally. We attempt to introduce a definite measure that determines efficacy of BCS while protecting aesthetic value of life for women affected with breast cancer.74 mammography examinations and the surgical interventions of those women underwent for the management of breast cancer were used to compute the coefficient of lesion. In first step the lesion and the mammary gland proper are measured applying geometry. In the second step volume of tissue mass to be removed was calculated taking into account the measures from the 1st step and we present the coefficient of lesion mathematically. We empirically illustrated our methodological approach for determining the tissue mass to be excised.Conventionally, it is assumed that if the volume of tissues to be removed does not exceed 25 % of the volume of the mammary gland, a Breast-conserving surgery (BCS), is performed, however, our empirical illustration demonstrated that the established decision making parameter is not tenable for determining the extent / type of surgery undertaken.We have developed a coefficient aligned with the stage of the carcinoma and founded the base for developing a statistical (mathematical) model. Application of such a model accommodating tumor biology and patient characteristics shall not only provide intraoperative real time information to surgeons but also predict the prognosis of optimal surgical intervention of breast cancer. Key words: coefficient of lesion for mammary gland, optimum surgical intervention, breast cancer, survival, probit regression model.

Forecasting the volume of operations for breast cancer according to the mammographic examination