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

A lymph node metastasis and prediction model technology, applied in image data processing, equipment, health index calculation, etc., can solve the problems of increasing the risk of recurrence and metastasis of patients, affecting the recognition rate of SLNB, and increasing the false negative rate, so as to reduce surgical complications Occurrence, reduction of axillary lymph node dissection, intuitive effect of the model

Inactive Publication Date: 2021-01-12
SUN YAT SEN MEMORIAL HOSPITAL SUN YAT SEN UNIV
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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 tr

Method used

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

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Embodiment Construction

[0037] In order to better illustrate the purpose, technical solutions and advantages of the present invention, the present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0038]The construction process of a breast cancer patient's axillary lymph node metastasis prediction model of the present invention is as follows (technical route is as follows: figure 1 shown):

[0039] 1. Patient Screening

[0040] Inclusion criteria: (1) female patients, over 18 years old; (2) patients with early breast cancer (TNM stage I-III, according to the 8th edition of AJCC staging); (3) patients who have been diagnosed with unilateral breast cancer by histopathology. Breast cancer without distant organ metastasis; (4) The patient has undergone surgery or axillary lymph node dissection, and pathological biopsy has been performed to confirm the status of the axillary lymph nodes; Scan-enhanced sequence (T1+C), T2-weighted sequence...

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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...

Claims

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Application Information

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IPC IPC(8): G16H50/70G16H50/20G16H50/30G06T7/00
CPCG06T7/0012G06T2207/10088G06T2207/20081G06T2207/30068G06T2207/30096G16H50/20G16H50/30G16H50/70
Inventor 姚和瑞宋尔卫余运芳谭钰洁陈勇健何子凡
Owner SUN YAT SEN MEMORIAL HOSPITAL SUN YAT SEN UNIV
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