一个可解释的放射基因组框架来预测肺腺癌患者中KRAS和EGFR的突变状态
Berardino Prencipe1, Claudia Delprete1, Emilio Garolla2
1Department of Electrical and Information Engineering, Polytechnic University of Bari, Via Orabona 4, 70126 Bari, Italy.
Bioengineering (Basel, Switzerland)
|July 29, 2023
概括
来自CT扫描的放射性特征可以预测肺腺癌中的KRAS和EGFR突变. 这种放射遗传学方法有助于理解瘤遗传学,以改善预后和治疗策略.
科学领域:
- 在瘤学瘤学.
- 放射学 放射学是一门学科.
- 基因组学就是基因组学.
背景情况:
- 肺腺癌的复杂病理生物学需要先进的诊断工具.
- 早期识别KRAS和EGFR突变对于预后和治疗反应至关重要.
研究的目的:
- 开发一个放射基因组工作流程,用于检测肺腺癌中的KRAS和EGFR突变,使用CT衍生的放射性特征.
- 识别显著的放射性特征,并评估用于预测突变状态的分类模型.
主要方法:
- 从肺腺癌患者的计算机断层扫描 (CT) 图像中提取了放射性特征.
- 采用特征选择算法和分类模型 (包括多层感知器).
- 使用SHAP (夏普利添加式扩展) 进行特征解释.
主要成果:
- 在验证集上,EGFR的AUC达到0.69和KRAS突变预测的AUC为0.82.
- 多层感知器模型在两种突变中都表现出卓越的性能.
- 在特定的放射性特征和EGFR/KRAS突变状态之间建立了关联.
结论:
- 放射基因组分析对非侵入性预测肺腺癌中关键遗传变化的有希望.
- 这种方法可以通过基于瘤遗传学的治疗决策来支持个性化医疗.
- 进一步验证是有必要的,以整合这个放射基因组工作流程到临床实践.
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