深度学习发现了新的形态特征,同时预测了从乳头甲状腺癌细胞病理学的遗传变化
Ingrid Marion1, Stefan Schulz1, Christina Glasner1
1Institute of Pathology, University Medical Center Mainz, Mainz, Germany.
概括
人工智能精确地从组织病理学幻灯片中预测乳头甲状腺癌 (PTC) 的遗传变化. 这种人工智能模型对预先选和发现与PTC基因型相关的新形态模式充满希望.
科学领域:
- 在瘤学瘤学.
- 病理学 病理学 病理学
- 人工智能的人工智能
- 基因组学就是基因组学.
背景情况:
- 乳头甲状腺癌 (PTC) 是最常见的内分泌恶性瘤.
- 关键的遗传改变包括BRAF突变 (40-60%),panRAS突变 (10-15%) 和基因融合 (7-35%).
- 从组织病理学预测这些遗传变化对于理解PTC至关重要.
研究的目的:
- 开发和验证一种人工智能 (AI) 管道,用于使用基因病理幻灯片预测PTC中的遗传变化.
- 确定与PTC中特定的遗传变异相关的新型形态标准.
- 评估AI在PTC中预测BRAF,panRAS和基因融合状态方面的可行性和准确性.
主要方法:
- 这是一项回顾性研究,使用两个独立的PTC患者队列 (共662例).
- 开发一个人工智能管道使用视觉变压器训练在数字化血素和色染色的幻灯片.
- 在外部队列上进行独立验证,以评估BRAF,panRAS和基因融合状态预测的模型性能.
主要成果:
- 人工智能模型实现了基因改变的高预测准确度:BRAF的AUC为0.882,panRAS为0.876,基因融合为0.858.
- BRAF的准确率为79.3%,panRAS为89.3%,基因融合为84.7%,在验证和测试组中表现一致.
- 可解释性分析揭示了与融合相关的PTC相关的新型形态模式,有助于基因型发现.
结论:
- 从数字化基因病理幻灯片中对遗传变化的AI驱动的预测对于PTC来说是可行的和准确的.
- 开发的人工智能模型显示了作为一种预先选工具的潜力,用于识别PTC中的遗传变化.
- 基于人工智能的特征识别可以发现以前未被识别的形态模式,潜在地提高诊断准确性和对PTC的理解.
相关概念视频
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Cancers Originate from Somatic Mutations in a Single Cell
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...


