超声联合免疫组织化学指标预测新辅助化疗后乳腺癌转移性腋窝淋巴结病理完全缓解
Chinese Journal of Medical Imaging Technology（2022）
Abstract
目的 观察超声联合免疫组织化学指标预测新辅助化疗(NACT)后乳腺癌转移性腋窝淋巴结病理完全缓解(pCR)的价值.方法 纳入155例接受NACT的乳腺癌伴腋窝淋巴结转移患者,根据腋窝淋巴结清扫(ALND)术后病理结果将其分为pCR组(n=59)及非pCR组(n=96);比较2组NACT前免疫组织化学指标及乳腺癌病灶及腋窝淋巴结的超声特征,建立logistic预测模型,观察其预测NACT后腋窝淋巴结pCR的价值.结果 2组乳腺癌原发灶Ki-67表达、人表皮生长因子受体2(HER-2)表达及腋窝转移性淋巴结短径、最大皮质厚度和形态差异均有统计学意义(P均<0.05).logistic预测模型预测NACT后乳腺癌腋窝转移性淋巴结pCR的AUC为0.734,敏感度为42.37％(25/59),特异度为84.38％(81/96).结论 NACT前超声联合免疫组织化学预测NACT后乳腺癌转移性腋窝淋巴结pCR具有一定价值.
More求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined