Prediction model training method for predicting relative curative effect of EC and T in NAC

• Main Authors: HU YONGZHI, TANG YUXIA, LIU KAIWEN, WANG SHOUJU
• Format: Patent
• Language: Chinese; English
• Published: 10.06.2025
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA; IMAGE DATA PROCESSING OR GENERATION, IN GENERAL; INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS; PHYSICS

The invention discloses a prediction model training method for predicting the relative curative effect of EC and T in NAC, and the method comprises the steps: building a data set through obtaining the MRI image data and clinical pathological data of a subject, and building a radiomics model based on DCE and ADC sequences through the steps of image segmentation, feature extraction, qualitative relative curative effect features, model building and verification, and the like. And predicting the relative curative effect of EC and T treatment in combination with a mixed model of clinical pathological factors. According to the method, the relative curative effect of EC (epirubicin and cyclophosphamide) and T (taxus drugs) treatment schemes can be effectively predicted in the middle stage of NAC, so that the treatment schemes are timely adjusted, and the treatment effect is improved. 本发明公开了一种预测NAC中EC与T相对疗效的预测模型训练方法，本发明通过获取受试者的MRI影像数据与临床病理资料构建数据集，并通过影像分割、特征提取、定性相对疗效特征、模型构建与验证等步骤构建基于DCE和ADC序列的影像组学模型，以及结合临床病理因素的混合模型，来预