基于CT的放射学和深度学习模型用于预测甲状腺软骨侵袭和喉癌患者的预后
Xinwei Chen1, Shuang Xia2, Huan Jiang1
1Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China.
Scientific reports
|November 17, 2025
概括
一个2D深度学习 (DL) 模型有效地预测了喉状细胞癌 (LSCC) 的甲状腺软骨侵袭,超过了放射学家和放射科医生. 这种DL模型有助于评估LSCC患者的预后并改善治疗决策.
科学领域:
- 在瘤学瘤学.
- 放射学 放射学是指放射学
- 人工智能的人工智能
背景情况:
- 准确评估甲状腺软骨侵袭对于喉状细胞癌 (LSCC) 治疗和预后至关重要.
- 当前的方法在准确评估这种入侵时可能存在局限性.
研究的目的:
- 为了比较放射学和深度学习 (DL) 模型来预测LSCC中的甲状腺软骨入侵.
- 评估最佳预测模型的预后值.
主要方法:
- 使用静脉相CT图像从两个中心的418名LSCC患者身上开发和比较放射学和2DDL模型.
- 在内部和外部队列中验证,与两个放射科医生进行比较.
- 构建一个整合最佳模型与临床风险因素的诺莫图.
主要成果:
- 2D DL 模型在预测甲状腺软骨侵袭方面表现出卓越的性能.
- 结合2D DL签名和临床因素的名图显示出高AUC,尽管外部验证显示没有显著差异.
- 二维DL标志和临床N阶段被确定为无病生存期 (DFS) 的独立预测因素.
结论:
- 基于2D DL的诺米图为LSCC的甲状腺软骨侵袭提供了令人满意的预测性能.
- 这种方法显示出改善LSCC管理中的预后评估和指导治疗策略的潜力.
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