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综合放射基因组学模型预测了高度血清性卵巢癌中新辅助化疗的反应

Mireia Crispin-Ortuzar^1,2, Ramona Woitek^3,4,5, Marika A V Reinius^3,6,7

¹Department of Oncology, University of Cambridge, Cambridge, UK. mc973@cam.ac.uk.

Nature communications

|October 24, 2023

概括

这项研究引入了一个新的机器学习框架,整合了临床,血液和放射性数据,以预测高度血清性卵巢癌 (HGSOC) 治疗反应. 该模型提高了预测准确度,有助于个性化新辅助化疗 (NACT) 策略.

科学领域:

在瘤学瘤学.
医疗成像医学成像
机器学习机器学习

背景情况:

高度血清性卵巢癌 (HGSOC) 是一种复杂,异质的癌症,通常在晚期被诊断出来.
由于疾病的复杂性,预测HGSOC中新辅助化疗 (NACT) 的反应具有挑战性.
识别可靠的生物标志物用于HGSOC治疗反应对于个性化医学至关重要.

研究的目的:

开发和验证一个综合框架,用于预测HGSOC患者对NACT的反应.
评估放射性生物标志物的附加价值,以及临床和血液数据.
提高HGSOC.治疗响应预测的准确性.

主要方法:

开发了一种集体机器学习模型,以预测疾病总体量的变化.
该模型整合了来自初级和转移性病变的基线临床,血液和放射性生物标志物.
来自72名患者的数据被用于培训,在内部 (n=20) 和外部 (n=42) 队列中进行验证.

主要成果:

综合放射学模型显示,与外部队列中的临床模型相比,预测误差提高了8%.
在RECIST 1.1分类中达到0.78的曲线下面面积 (AUC),显著超过临床模型的0.47.7的AUC.
放射性数据显著提高了综合模型的预测性能.

结论:

将放射性数据集成到预测模型中可以显著提高HGSOC治疗反应评估的准确性.
开发的框架为基于生物标志物的临床试验提供了一个有希望的方法,用于HGSOC.
这项研究强调了多模式数据集成对于优化卵巢癌中NACT策略的重要性.

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