一个级联细分分类深度学习框架用于术前预测隐性腹膜转移和晚期胃癌的早期复发
Tianxiu Zou1, Peng Chen1, Ting Wang2
1Department of Radiology, Fujian Medical University Union Hospital, Fuzhou 350001, China (T.Z., P.C., T.L., X.C., F.Y., X.L., L.W.).
Academic radiology
|September 5, 2025
概括
一个新的深度学习框架准确地预测了晚期胃癌患者的隐性腹膜转移和早期复发. 这种工具通过改善手术前风险分层来帮助个性化治疗.
科学领域:
- 癌症学
- 医学成像
- 人工智能
背景情况:
- 晚期胃癌 (GC) 在预测隐性腹膜转移 (OPM) 和早期腹膜复发 (PR) 方面存在挑战.
- 准确的手术前风险分层对于GC患者个性化治疗策略至关重要.
研究的目的:
- 开发和验证一个级联深度学习 (DL) 框架,用于预测高级GC中的OPM和早期PR.
- 整合瘤细分和风险分层以提高手术前预测的准确性.
主要方法:
- 一项多中心研究,涉及765名晚期GC患者.
- 开发一个两阶段的DL框架:V-Net瘤细分,然后基于DL的风险分类.
- 结合临床病理预测因子与DL概率进行组合预测模型.
主要成果:
- 组合模型在OPM预测 (0. 938火车,0. 916测试1) 和早期PR预测 (0. 820- 0. 825测试2/测试3) 中取得了高AUC.
- 与临床和DL-only模型相比,对OPM预测有显著的改善.
- 鉴定了博尔曼型,CA125水平和CT-N+阶段作为独立的OPM预测因素.
结论:
- 基于CT的级联DL框架提供了可靠的手术前风险分层,用于高级GC的OPM和早期PR.
- 这种方法有可能完善治疗指示,并为GC患者个性化治疗途径.
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