深度学习增强了MRI放射学,用于预测头部和部状癌对新辅助化疗免疫治疗的病理反应:回顾性分析
Tianjun Lan1,2, Yongmei Tan1,2, Huaxian Shi3
1Department of Oral and Maxillofacial Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China.
International journal of surgery (London, England)
|October 28, 2025
概括
对MRI扫描的深度学习分析改善了对头癌治疗反应的预测. 将这些特征与临床数据相结合,为预测头部和部状细胞癌患者的结果提供了更强大的策略.
科学领域:
- 在瘤学瘤学.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 新辅助化疗免疫疗法 (NACI) 是手术前对头部和部状细胞癌 (HNSCC) 的标准治疗方法.
- 预测患者对NACI的反应仍然具有挑战性,因为缺乏可靠的预测生物标志物.
研究的目的:
- 在接受NACI的HNSCC患者中开发和验证病理完整反应 (pCR) 的预测模型.
- 评估MRI的深度学习特征与传统放射学和临床病理学数据相结合的实用性.
主要方法:
- 接受NACI的HNSCC患者被追溯分析.
- 收集了临床病理学数据和MRI图像.
- 传统的放射学和深度学习技术被用来从MRI中提取特征.
- 使用斯皮尔曼相关性和LASSO分析进行了特征选择.
- 后勤回归模型是为PCR预测而构建的.
主要成果:
- 深度学习特征构成了所选预测特征的很大一部分.
- 将深度学习功能与放射学和临床病理学数据相结合,提高了预测准确度 (AUC: 0.781培训, 0.759测试, 0.740验证).
- 多模式预测显著提高了接受NACI的HNSCC患者的患者分层和预后.
结论:
- 对MRI图像的深度学习分析可以揭示HNSCC中NACI反应的预测模式.
- 结合不同的数据类型 (深度学习,放射学,临床) 提供了一个更强大的预测策略,用于HNSCC.NACI在HNSCC.
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