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多中心研究:基于超声波的深度学习功能用于预测乳腺癌中的Ki-67表达

Qishan Cen¹, Man Wang², Siying Zhou¹

¹The First Clinical College of Medicine, Guangxi Medical University, Nanning, 530021, Guangxi, China.

Scientific reports

|March 26, 2025

概括

深度学习模型使用超声波特征准确预测乳腺癌Ki-67表达. 这有助于评估乳腺癌患者的预后和指导治疗决策.

科学领域:

在瘤学瘤学.
医疗成像医学成像
人工智能的人工智能

背景情况:

基-67表达是乳腺癌预后和治疗选择的关键生物标志物.
准确预测Ki-67水平对于个性化癌症护理至关重要.
目前用于Ki-67评估的方法可能是侵入性的和耗时的.

研究的目的:

开发和验证一个深度学习模型,用超声波成像来预测乳腺癌Ki-67表达水平.
整合瘤和周围瘤区域的特征,以提高预测准确度.
评估开发模型在乳腺癌患者管理中的临床实用性.

主要方法:

一项涉及929名乳腺癌患者的多中心回顾性研究.
应用深度学习算法从瘤和周区域提取超声波特征.
一个融合模型的构建和验证,将Ki-67预测的特征结合起来.
使用统计指标进行绩效评估,包括内部和外部数据集的准确性,ROAUC,灵敏度,特异性和F1得分.

主要成果:

采用支持矢量机 (SVM) 的融合模型在预测Ki-67表达方面取得了高性能.
在瘤区域深度特征模型上,SVM模型显示精度为0.782和ROAUC为0.771.
内部验证显示,在高基-67表达组 (P=0.005) 中,无病存活率明显较低.

结论:

对乳腺癌超声波图像的深度学习分析可以准确预测Ki-67表达水平.
开发的模型显示了对乳腺癌攻击性的非侵入性评估的潜力.
这种方法可以提高预后准确性,并为乳腺癌患者的治疗策略提供信息.

关键词:

乳腺癌乳腺癌乳腺癌深度学习是一种深度学习.在 Ki-67 机器人超声波成像超声波成像

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