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使用深度学习模型来预测神经内分泌瘤患者的无进展生存率

Marianne Pavel¹, Clarisse Dromain², Maxime Ronot³

¹Department of Medicine 1, Friedrich-Alexander-University of Erlangen-Nürnberg, Erlangen, Germany.

Future oncology (London, England)

|July 27, 2023

概括

深度学习模型分析了CT扫描,以预测神经内分泌瘤中无进展生存 (PFS). 虽然深度学习提取了相关特征,但与现有方法相比,深度学习并没有增强早期PFS预测.

科学领域:

在瘤学瘤学.
放射学放射学是一门学科.
人工智能的人工智能

背景情况:

神经内分泌瘤 (NETs) 需要准确的预后来规划治疗.
预测无进展生存率 (PFS) 对于管理NET至关重要.
深度学习 (DL) 提供了在瘤学中改善预测建模的潜力.

研究的目的:

评估深度学习 (DL) 是否可以提高神经内分泌瘤 (NET) 患者无进展生存 (PFS) 的早期预测.
为了比较DL模型与传统生物标志物相结合的预测性能与单独的生物标志物.

主要方法:

在CLARINET试验中,使用来自138名患者的CT扫描数据开发了深度学习模型.
这项研究使用了考克斯模型来评估PFS预测.
将包含DL特征的模型与仅使用最长直径比率 (SLDr) 和对数转换染色素A (logCgA) 的模型进行了性能比较.

主要成果:

深度学习模型确定了超出病变形状的特征,用于72周的PFS预测.
与单独使用SLDr和logCgA的模型相比,添加深度学习并没有显著改善PFS的预测性能.
相关的预测特征被DL提取出来,但早期预测的准确性没有得到增强.

结论:

深度学习模型可以提取相关的成像特征来预测神经内分泌瘤中的PFS.
当前的深度学习方法,当与已确定的生物标志物 (SLDr,logCgA) 结合时,无法在NET患者中提供优越的PFS早期预测.
可能需要进一步的研究来优化DL集成,以提高NET中的预后准确性.

关键词:

拒绝,反对,反对.人工智能的人工智能是人工智能.深度学习是一种深度学习.神经内分泌瘤的神经内分泌瘤没有进展的生存率.

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