Breast cancer patient axillary lymph node metastasis prediction model and construction method thereof

Abstract

The invention discloses a breast cancer patient axillary lymph node metastasis prediction model and a construction method thereof. The artificial intelligence prediction model for axillary lymph nodemetastasis of a breast cancer patient is established through employing an artificial intelligence machine learning algorithm based on the magnetic resonance image data and clinical feature data of thebreast cancer patient. The prediction model has the advantages of being accurate, simple, convenient, non-invasive and the like, can effectively evaluate preoperative axillary lymph node metastasis of a breast cancer patient, is helpful for assisting breast cancer clinical diagnosis and treatment decisions, reducing unnecessary axillary lymph node cleaning operations of the patient, reducing operative complications and improving the life quality of the patient, has higher prediction efficiency and clinical income, and has important guiding significance for guiding clinical treatment strategies and enhancing clinical treatment intervention and subsequent individualized follow-up visit.

Description

technical field [0001] The invention belongs to the field of biomedicine, and relates to a breast cancer patient's axillary lymph node metastasis prediction model 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 place in the incidence of female malignant tumors and the fifth place in the 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%. [0003] Axillary lymph node (ALN) metastasis is a common metastatic location of breast cancer. About 40% of breast cancer patients are ALN positive, and 18.7% of ALN positive patients will relapse after 10 years. Therefore, the ALN status of breast cancer patients affects surgical options and...

AI Technical Summary

Problems solved by technology

In breast cancer patients receiving neoadjuvant therapy, whether changes in lymphatic drainage pathways affect the recognition rate of SLNB and lead to an increase in the false negative rate is still controversial.

If subsequent clinical treatment decisions are based solely on the pathological results of lymph node biopsy or sentinel lymph node biopsy, it may result in insufficient chemotherapy and even increase the risk of postoperative recurrence and metastasis in patients

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