甲状腺癌的无注释基因突变估计使用来自多中心的细胞学幻灯片
Siping Xiong1, Shuguang Liu1, Wei Zhang1
1Department of Pathology, The Eighth Affiliated Hospital of Sun Yat-Sen University, Shenzhen, 518000, Guangdong, China.
Diagnostic pathology
|February 22, 2025
概括
这项研究开发了一种深度学习模型,直接从甲状腺细针吸收 (FNA) 细胞学图像中预测BRAF和RAS基因突变,有助于甲状腺癌诊断和风险评估.
科学领域:
- 内分泌学 在内分泌学.
- 在瘤学瘤学.
- 计算生物学 计算生物学
背景情况:
- 甲状腺癌是最常见的内分泌恶性瘤.
- 精细针吸收 (FNA) 细胞学对于诊断至关重要.
- 基因突变状态有助于甲状腺癌的诊断和风险分层.
研究的目的:
- 从甲状腺细胞学图像中开发一个多式级联网络框架,用于估计BRAF V600E和RAS突变.
- 评估深度学习在甲状腺癌遗传学的诊断和预后潜力.
主要方法:
- 收集了来自310个不确定的 (TBS3或4) 和392个状甲状腺癌 (PTC) (TBS5或6) 病例的甲状腺细胞学图像数据集.
- 引入了一种多模式级联网络框架,直接从细胞学幻灯片中预测BRAF和RAS突变.
- 在外部测试套件上验证模型.
主要成果:
- 该模型实现了BRAF V600E突变预测的曲线下面面积 (AUC) 0.902 ± 0.063.
- 该模型实现了RAS突变预测的AUC值为0.801±0.137.
- 证明了深度神经网络在从细胞学图像中预测遗传状态的能力.
结论:
- 深度神经网络显示出从甲状腺细胞学幻灯片中预测有价值的诊断和全面的遗传信息的巨大潜力.
- 这种方法可以提高甲状腺癌诊断的准确性和风险分层.
- 该研究强调了人工智能在促进细胞病理学和分子诊断方面的实用性.
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Cancer arises from mutations in the critical genes that allow healthy cells to escape cell cycle regulation and acquire the ability to proliferate indefinitely. Though originating from a single mutation event in one of the originator cells, cancer progresses when the mutant cell lines continue to gain more and more mutations, and finally, become malignant. For example, chronic myelogenous leukemia (CML) develops initially as a non-lethal increase in white blood cells, which progressively...
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Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
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