放射组学和深度学习在脑转移中的应用:当前趋势和未来应用路线图

Y. Park, Narae Lee, S. Ahn, Jong-Hee Chang, Seung-Koo Lee
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引用次数: 5

摘要

放射组学和深度学习(DL)的进展具有巨大的潜力,可以成为治疗脑转移患者的精准医学的前沿。放射组学和DL可以通过实现准确的诊断、促进分子标记的识别、提供准确的预后和监测治疗反应来帮助临床决策。在这篇综述中,我们总结了放射组学和DL治疗脑转移瘤的临床背景、未满足的需求和研究现状。放射组学和DL在脑转移中的前景、缺陷和未来路线图也得到了解决。
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Radiomics and Deep Learning in Brain Metastases: Current Trends and Roadmap to Future Applications
Advances in radiomics and deep learning (DL) hold great potential to be at the forefront of precision medicine for the treatment of patients with brain metastases. Radiomics and DL can aid clinical decision-making by enabling accurate diagnosis, facilitating the identification of molecular markers, providing accurate prognoses, and monitoring treatment response. In this review, we summarize the clinical background, unmet needs, and current state of research of radiomics and DL for the treatment of brain metastases. The promises, pitfalls, and future roadmap of radiomics and DL in brain metastases are addressed as well.
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