通过使用机器学习和深度学习模型,对多种原发性癌症中脑转移的空间分布进行比较分析
Saeedeh Mahmoodifar1, Dhiraj J Pangal2, Josh Neman2
1Department of Physics & Astronomy, University of Southern California, Los Angeles, CA, 90089, USA.
Journal of neuro-oncology
|April 2, 2024
概括
大脑转移 (BM) 的解剖分布因原发性癌症类型而异. 机器学习模型准确地分类这些模式,有助于早期检测和监测大脑转移.
科学领域:
- 神经瘤学神经瘤学
- 医学成像分析分析 医学成像分析
- 计算生物学是一种计算生物学.
背景情况:
- 大脑转移 (BM) 显著恶化患者的预后和生存率.
- 早期发现和监测BM对于改善患者的治疗结果至关重要.
- 缺乏在不同原发性癌症中BM解剖分布的系统比较.
研究的目的:
- 为了调查BM的解剖分布是否根据原发性癌症类型不同.
- 利用空间坐标和机器学习来识别不同的BM模式.
- 增强对早期检测和监测策略的理解,以改善早期检测和监测策略.
主要方法:
- 分析了来自五种主要癌症类型的3949个内转移的空间坐标.
- 应用主要组件 (PC) 分析来绘制BM分布图.
- 使用机器学习 (ML) 算法,包括随机森林 (RF),支持矢量机器 (SVM) 和TabNet深度学习 (DL).
主要成果:
- PC1主要与Y轴相关,表示特定的空间方向.
- 在乳房与乳房之间观察到明显的解剖传播模式. 肺癌和乳腺癌与脏癌.
- ML和DL模型在按原发性癌症类型分类BM分布方面实现了高精度 (SVM高达97%) .
结论:
- 这项研究支持多类ML分类对大脑转移分布的有效性.
- 在BM传播模式中发现的差异可以为有针对性的诊断和治疗方法提供信息.
- 对BM的空间分析为癌症进展和患者分层提供了宝贵的见解.
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