对CT扫描进行深度学习,以预测晚期黑色素瘤中检查点抑制剂治疗结果
Laurens S Ter Maat1, Rob A J De Mooij2, Isabella A J Van Duin3
1Image Sciences Institute, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
Scientific reports
|December 31, 2024
概括
与单独的临床因素相比,CT扫描的深度学习并没有改善预测晚期黑色素瘤治疗反应. 整合成像数据与临床预测因素对于黑色素瘤免疫疗法的准确结果至关重要.
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:
- 免疫检查点抑制剂 (ICI) 对晚期黑色素瘤有效,但具有显著的毒性和成本.
- 目前缺乏预测黑色素瘤ICI反应的生物标志物.
- 对CT成像的深度学习 (DL) 被探索用于预测治疗结果.
研究的目的:
- 评估深度学习模型的预测价值,应用于转移性病变的CT成像,以评估晚期黑色素瘤ICI治疗结果.
- 为了将DL模型的性能与已建立的临床预测因素进行比较.
- 评估将DL模型与临床预测指标相结合的好处.
主要方法:
- 从10个ICI治疗中心的730名晚期黑色素瘤患者的回顾性分析.
- 从基线CT扫描中对转移性病变体积进行了深度学习模型 (DLM) 的训练.
- 将DLM与临床预测模型 (肝/大脑转移,LDH,性能状态,器官计数) 进行比较和结合.
主要成果:
- DLM的AUROC为0.607,而临床模型的AUROC为0.635.
- 组合模型与单独的临床模型相比没有显著的改善 (AUROC 0.635).
- DLM输出与临床变量相关,显示有区分价值,但不超过临床预测指标.
结论:
- 仅使用CT成像进行深度学习并不能提高在晚期黑色素瘤中ICI反应的预测.
- 临床预测因素对于预测治疗结果至关重要.
- 将基于成像的评估与临床因素相结合,对于在黑色素瘤免疫治疗中进行细微预测至关重要.
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