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机器学习算法用于预测晚期黑色素瘤患者在现实生活环境中对免疫治疗的反应

Cécile Frenard¹, Katleen Blanchet², Philippe Lecerf³

¹Department of Dermato-Cancerology, CHU Nantes, CIC 1413, CRCINA, University Nantes, Nantes, France.

European journal of dermatology : EJD

|July 11, 2023

概括

机器学习模型识别了生物标志物,以预测转移性黑色素瘤 (MM) 免疫检查点抑制剂 (ICI) 治疗成功. AJCC阶段是最重要的预测因素,有助于个性化治疗策略,以改善患者的生存率.

科学领域:

在瘤学瘤学.
皮肤病学皮肤病学
计算生物学计算生物学

背景情况:

黑色素瘤是一种致命的皮肤癌,需要生物标志物来预测治疗结果.
用免疫检查点抑制剂 (ICI) 治疗转移性黑色素瘤 (MM) 患者的生存率提高需要预测工具.

研究的目的:

为了比较机器学习模型来识别生物标志物.
在现实条件下预测MM患者对ICI的治疗反应.

主要方法:

利用了用ICI治疗的MM患者 (AJCC第三阶段C/D或IV) 的临床数据.
比较轻度梯度增强机,线性回归,随机森林 (RF),支向量机和极端梯度增强.
使用SHAP (夏普利添加式解释) 进行特征重要性分析.

主要成果:

随机森林 (RF) 实现了最高的精度 (0.63) 和灵敏度 (0.64).
AJCC阶段显示出最高的预测值 (SHAP平均值为0.076).
其他预测特征包括转移部位,治疗持续时间和布雷斯洛指数.

结论:

机器学习有效地识别了预测ICI治疗成功的生物标志物.
生物标志物识别可以增强黑色素瘤患者个性化治疗策略.

关键词:

人工智能的人工智能是人工智能.检查点抑制器基因病理学电子显微镜的电子显微镜机器学习是机器学习.黑色素瘤是一种黑色素瘤.

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