深度学习衍生的CT身体组成在局部晚期胃癌中增强了超越TNM系统的生存风险分层:一项多模式队列研究
Ying-Chieh Lai1, Yu-Ching Lin1, Tzong-Shyuan Tai2
1Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Chang Gung University, Taoyuan, Taiwan.
以内脏与皮下脂肪组织 (VAT/SAT) 的比率来衡量内脏脂肪,预测晚期胃癌的存活率较低. 这一比例反映了代谢和免疫变化,为个性化治疗提供了一个新的生物标志物.
科学领域:
- 在瘤学瘤学.
- 放射学 放射学是一门学科.
- 代谢学 代谢学 代谢学
- 免疫学 免疫学 免疫学
背景情况:
- 尽管目前的分期,胃癌的生存率是不可预测的.
- 内脏脂肪是潜在的预后因素,但缺乏临床应用.
研究的目的:
- 研究基于计算机断层扫描 (CTBC) 的身体组成指标在局部发达的胃癌中的预后价值.
- 探索CTBC指标与瘤免疫代谢概况之间的关联.
主要方法:
- 对227名经过胃切除术的第三期胃癌患者的回顾性分析.
- 深度学习模型 (UNet++) 用于自动化CTBC量化.
- 血代谢和瘤免疫概况通过质谱,IHC和流细胞计.
主要成果:
- 自动化CTBC指标与手动细分有很强的相关性.
- 较低的皮下脂肪组织 (SAT) 指数和高的内脏与皮下脂肪组织 (VAT/SAT) 比率预测了更差的生存率.
- 高的VAT/SAT比率与线粒体功能受损,脂质代谢改变以及瘤中免疫抑制细胞增加有关.
结论:
- 深度学习衍生的CTBC指标,特别是增值税/SAT比率,提高了超越TNM阶段预测的预测准确性.
- 增值税/SAT比率确定了胃癌中一个独特的免疫代谢表型.
- 增值税/SAT比率作为个性化治疗和风险分层的非侵入性生物标志物.
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