Method and system for predicting pathological complete remission probability after breast cancer neoadjuvant chemotherapy

A technology of complete remission and probability prediction, applied in the field of medical information, can solve problems such as difficult promotion, low diagnostic efficiency, and high price of multi-gene model detection, and achieve the effect of avoiding toxic side effects and optimizing treatment strategies

Pending Publication Date: 2021-03-23
SUN YAT SEN MEMORIAL HOSPITAL SUN YAT SEN UNIV +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The above multigene models predicting the probability of NAC-induced pCR in HR+ breast cancer has not been widely used in clinical practice, and the use of multigene models such as Oncotype DX and PAM50 to predict the probability of NAC-induced pCR still has some shortcomings
First, few models in current studies have s

Method used

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  • Method and system for predicting pathological complete remission probability after breast cancer neoadjuvant chemotherapy
  • Method and system for predicting pathological complete remission probability after breast cancer neoadjuvant chemotherapy
  • Method and system for predicting pathological complete remission probability after breast cancer neoadjuvant chemotherapy

Examples

Experimental program
Comparison scheme
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Example Embodiment

[0071]Example 1

[0072]This example proposes a predictive method of a new auxiliary chemotherapy after breast cancer, which includes the following steps:

[0073]S1. Collect patient information to form training sets;

[0074]S2. In the training set formed by S1, a separate predictor related to clinical pathological state is screened by multi-factor regression analysis;

[0075]S3. The independent prediction factor associated with the clinical pathological state obtained in S2 is formed into a column chart model, and the weight score of each independent predictor of the inclusion of the column chart model is obtained.

[0076]The probability prediction value of the pathology completely alleviated by the new adjuvant chemotherapy by column chart model.

[0077]Further, in S1, the patient information is a hormone receptor positive type breast cancer patient information, including humanity information, clinical pathological feature information, a 10-miRNA risk score and quantitative dynamic comparison o...

Example Embodiment

[0087]Example 2

[0088]This embodiment proposes a computer readable storage medium that stores a computer program that implements the following steps S1-S4 when executed by the processor.

[0089]S1. Collect patient information to form training sets;

[0090]S2. In the training set formed by S1, a separate predictor related to clinical pathological state is screened by multi-factor regression analysis;

[0091]S3. The independent prediction factor associated with the clinical pathological state obtained in S2 is formed into a column chart model, and the weight score of each independent predictor of the inclusion of the column chart model is obtained.

[0092]The probability prediction value of the pathology completely alleviated by the new adjuvant chemotherapy by column chart model.

[0093]Further, in S1, the patient information is a hormone receptor positive type breast cancer patient information, including humanity information, clinical pathological feature information, a 10-miRNA risk score and...

Example Embodiment

[0103]Example 3

[0104]This embodiment proposes a construction method for the prediction model of the pathological complete mitigation probability prediction model of breast cancer, including the following steps:

[0105]S1. Collect patient information to form training sets;

[0106]S2. In the training set formed by S1, a separate predictor related to clinical pathological state is screened by multi-factor regression analysis;

[0107]S3. The independent prediction factor associated with the clinical pathological state of S2 is formed into a column chart model, and the weight score of each independent predictor of the inclusion of the column chart model is obtained;

[0108]The net income of the threshold probability of the threshold probability of the pathology of the new auxiliary chemotherapy after breast cancer was analyzed by column line diagram model analysis to assess the clinical utility of the predictive model.

[0109]Further, in S1, the patient information is a hormone receptor positive t...

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Abstract

The invention discloses a pathological complete remission probability prediction method after breast cancer neoadjuvant chemotherapy. The method comprises the following steps: S1, forming a training set; S2, screening out independent prediction factors; S3, obtaining a weight score; S4, analyzing and calculating a probability prediction value of pathological complete remission after breast cancerneoadjuvant chemotherapy. The invention further discloses a computer readable storage medium, a computer program is stored on the computer readable storage medium, and when the program is executed bythe processor, the step S1-S4 is achieved. The invention further discloses a system for predicting the pathological complete remission probability after breast cancer neoadjuvant chemotherapy. The system comprises a training set generation module, a multi-factor regression analysis screening module, a column diagram model construction and display module, a weight score generation module and a netincome analysis module. According to the invention, patients with low profit possibility can be screened out, and the patients are finally free from neoadjuvant chemotherapy.

Description

technical field [0001] The present invention relates to the field of medical information technology, in particular to a method for predicting the probability of pathological complete remission after neoadjuvant chemotherapy for breast cancer, a prediction system, a computer-readable storage medium equipped with a program for executing the prediction method, and neoadjuvant chemotherapy for breast cancer. A method for constructing a predictive model for the probability of pathological complete remission after chemotherapy. Background technique [0002] Breast cancer is one of the main malignant tumors threatening women's health around the world, and about 70% of breast cancers are hormone receptor positive. Neoadjuvant chemotherapy can reduce the size of the tumor so as to achieve the purpose of breast-conserving surgery and drug sensitivity monitoring in breast cancer. However, unlike hormone receptor-negative breast cancer, hormone receptor-positive breast cancer has a rel...

Claims

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Application Information

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IPC IPC(8): G16H50/50G16H50/30
CPCG16H50/50G16H50/30
Inventor 宋尔卫龚畅沈君杨雅平程子亮林婉宜
Owner SUN YAT SEN MEMORIAL HOSPITAL SUN YAT SEN UNIV
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