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Updated: May 2, 2026

Employing Digital Droplet PCR to Detect BRAF V600E Mutations in Formalin-fixed Paraffin-embedded Reference Standard Cell Lines

Employing Digital Droplet PCR to Detect BRAF V600E Mutations in Formalin-fixed Paraffin-embedded Reference Standard Cell Lines

Published on: October 8, 2015

打破障碍:用于BRAF^V600E突变识别的非侵入性AI模型

Fan Wu¹, Xiangfeng Lin², Yuying Chen³

¹Department of Oncological Surgery, Affiliated Hangzhou First People's Hospital, Westlake University School of Medicine, Hangzhou, 310006, Zhejiang, China.

International journal of computer assisted radiology and surgery

|February 15, 2025

概括

这项研究开发了一种使用超声波图像和深度转移学习的非侵入性AI模型,用于识别皮质甲状腺癌中的BRAF^V600E突变. DTLR模型实现了高精度,为侵入性遗传测试提供了一个有希望的替代方案.

科学领域:

放射学放射学是一门学科.
在瘤学瘤学.
人工智能的人工智能

背景情况:

BRAF^V600E突变在乳头甲状腺癌 (PTC) 中很常见.
目前的BRAF^V600E突变检测方法是侵入性的.
需要用于识别BRAF^V600E突变的非侵入性方法.

研究的目的:

开发一种非侵入性人工智能 (AI) 模型,用于识别PTC中的BRAF^V600E突变.
从超声波图像中提取放射性特征并利用深度转移学习 (DTL).
为了改善诊断能力,将DTL和放射学结合起来.

主要方法:

感兴趣的区域 (ROI) 在超声波图像中进行了注释.
放射性和DTL特征被提取并组合成一个DTLR模型.
用Lasso回归来选择特征,并使用8种机器学习方法来构建模型.
使用AUC,精度,灵敏度和特异性来评估性能. 使用Grad-CAM进行可视化.

主要成果:

DTLR模型,特别是ResNet152,有效地识别了BRAF^V600E突变.
在验证组中,最佳的DTLR模型实现了0.833的AUC,准确率为80.6%,灵敏度为76.2%,特异性为81.7%.
DTLR模型的性能优于独立的DTL和无线电学模型.
格拉德-CAM可视化证实了模型识别相关超声波图像特征的能力.

结论:

基于ResNet152的DTLR模型显示了使用超声波在PTC患者中非侵入性识别BRAF^V600E突变的显著潜力.
格拉德-CAM可以帮助对BRAF突变进行视觉分层.
建议使用更大的数据集进行进一步的多中心验证.

关键词:

在BRAF V600E中使用.深度学习是一种深度学习.乳头甲状腺癌是一种乳头甲状腺癌.无线电学 (Radiomics) 是一种无线电学.

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