Breast cancer patient disease-free lifetime prediction model and construction method thereof

Abstract

The invention discloses a breast cancer patient disease-free lifetime prediction model and a construction method thereof. The method builds a breast cancer patient disease-free lifetime artificial intelligence prediction model by employing an artificial intelligence machine learning algorithm based on the magnetic resonance image data and clinical feature data of a breast cancer patient, and can make early breast cancer patients effectively divided into a high-risk group and a low-risk group. The prediction model has the advantages of accuracy, simplicity, convenience, noninvasiveness and thelike, can effectively evaluate the postoperative disease-free lifetime of early breast cancer patients, is helpful for assisting clinicians to optimize treatment schemes, strengthens treatment intervention on high-risk patients as soon as possible, reduces recurrence risks of the patients, and has important guiding significance for guiding clinical treatment strategies and enhancing clinical treatment intervention and subsequent individualized follow-up visits.

Description

technical field [0001] The invention belongs to the field of biomedicine, and relates to a prediction model of disease-free survival period of breast cancer patients and a construction method thereof. Background technique [0002] Breast cancer is a malignant tumor that seriously endangers women's health. It accounts for the first incidence rate of female malignant tumors and the fifth death rate. The incidence rate is also increasing year by year. About 30%-40% of breast cancer patients undergo recurrence and metastasis after surgery and postoperative adjuvant therapy, and eventually develop into advanced breast cancer, and the 5-year survival rate is less than 23%. Research on the risk of recurrence and metastasis of early breast cancer has shown that, in addition to age and tumor size, preoperative axillary lymph node (ALN) status, pathological complete response (pCR) after neoadjuvant chemotherapy, molecular typing and related gene expression, etc. It is also an importa...

AI Technical Summary

Problems solved by technology

However, most of these methods are limited to the prediction of specific populations, and lack the support of prospective studies with high levels of evidence

In addition, there is no unified diagnosis and treatment prediction standard for some important molecular types of breast cancer, such as other types of triple-negative breast cancer with poor prognosis

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