Breast cancer risk coordinators: Artificial intelligence-based density measurement and Mullerian-inhibiting substance.

Abstract

Due to its increasing prevalence, breast cancer has become a serious public health problem. In addition to the models used to identify individuals at risk, the search for fast and accurate tools has continued for years. In our study, we aimed to examine the correlation of mammographic density measurement and serum Mullerian-inhibiting substance (MIS) levels with an effective model such as Gail. Of the women whose serum MIS levels were measured in the last 1year, 214 participants who applied for routine breast examination were included in the study. The age range was between 40 and 60. Exclusion criteria were determined as pathological mammographic findings, active breast symptom, and thoracic radiotherapy history. Mammographic density measurement (PD) was performed with the artificial intelligence-based Deep-LIBRA software. The relationship of these two parameters with the lifetime risk of developing breast cancer was examined. The correlation between PD and GRP was remarkable (p < 0.01cc:0.35). A positive correlation was observed between serum MIS levels and increased breast cancer, but it was not possible to prove this statistically (p = 0.056). It was thought that this situation was caused by perimenopausal patients. Because when the menopause group was excluded, the correlation between MIS levels and GRP decreased (p = 0.12cc:0.17). PD measurement can be considered as a promising method for the determination of individuals at risk for breast cancer in a large group of patients, but we think that serum MIS levels are not suitable for risk assessment in perimenopausal patients.