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双对抗性解混自动编码器用于从多中心和多扫描仪放射学数据中联合删除批量效应
Lara Cavinato1, Michela Carlotta Massi2, Martina Sollini3,4
1MOX, Department of Mathematics, Politecnico di Milano, Piazza Leonardo da Vinci, 32, Milan, 20133, Italy. lara.cavinato@polimi.it.
Scientific reports
|November 2, 2023
概括
这项研究引入了一种新的算法,用于协调来自多个中心的医疗成像数据,用于霍奇金淋巴瘤患者. 该方法有效地减少了扫描器特定的噪音,并提高了预后模型的准确性,有助于临床决策.
科学领域:
- 医疗成像医学成像
- 定量图像分析 定量图像分析
- 在瘤学瘤学.
背景情况:
- 医学成像对于癌症的调查和监测至关重要.
- 多中心研究需要否定混因素,如扫描仪/中心特定的影响.
- 淋巴瘤成像需要代表多个地点的疾病传播,以获得有效的生物标志物.
研究的目的:
- 为了解决医学成像中的双重因素解混问题.
- 提出一种解混算法,用于协调多中心设置中的霍奇金淋巴瘤成像数据.
- 通过提高成像数据质量来改进预后模型.
主要方法:
- 开发一种新的解混算法.
- 应用该算法来协调霍奇金淋巴瘤多中心成像数据.
- 评估算法的数据无效化和保存空间损伤关系的能力.
主要成果:
- 该算法成功地从域特定变异性 (p < 0.001) 中取消了数据.
- 它保留了对等病变之间的空间关系 (p = 0),这是一个关键的预后生物标志物.
- 协调显著改善预后模型的性能 (p < 0.001 训练,p < 0.05 测试).
结论:
- 拟议的算法有效地协调了霍奇金淋巴瘤多中心医学成像数据.
- 这种方法使得更准确的预后评估和详尽的患者表述.
- 这项工作促进了对成像生物标志物的临床翻译进行大规模,可重复的分析.
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