基于临床因素的可解释机器学习模型,用于预测卵巢癌治疗后的肌肉辐射密度损失
Wan-Chun Lin1, Chia-Sui Weng2,3, Ai-Tung Ko1
1Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Li-Nong St., Beitou District, Taipei, 112304, Taiwan.
概括
机器学习模型可以预测治疗后卵巢癌患者的肌肉辐射密度损失. 这有助于识别有风险的患者,并了解有助于改善结果的因素.
科学领域:
- 在瘤学瘤学.
- 放射学 放射学是一门学科.
- 机器学习 机器学习
背景情况:
- 手术和化疗后的肌肉辐射密度损失与卵巢癌的不良结果有关.
- 评估肌肉辐射密度是具有挑战性的,因为CT协议要求和劳动强度.
研究的目的:
- 开发一种可解释的机器学习 (ML) 模型,用于预测卵巢癌患者的肌肉辐射密度损失.
- 确定导致肌肉辐射密度丧失的关键因素.
主要方法:
- 利用了723名接受手术和化疗的卵巢癌患者的数据.
- 通过CT扫描训练和评估六个ML模型,将肌肉辐射密度损失定义为≥5%的下降.
- 应用了夏普利添加式解释 (SHAP) 来实现模型的可解释性.
主要成果:
- CatBoost模型表现出高性能,其AUC为0.871和F1得分为0.688.
- 外部验证证实了模型的有效性,AUC为0.839和F1得分为0.673.
- 肌肉辐射密度丧失的关键预测因素包括白蛋白变化,瘤和残留疾病.
结论:
- 可解释的ML模型可以有效地识别患有肌肉放射密度丧失风险的卵巢癌患者.
- 这些模型为推动肌肉辐射密度丧失的因素提供了洞察力,有助于临床决策.
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