一个基于计算机断层扫描的多任务深度学习模型,用于预测结直肠癌患者的瘤肌瘤比率和治疗结果:一个多中心队列研究
Yanfen Cui1,2,3,4, Ke Zhao1,2,3, Xiaochun Meng5
1Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University.
International journal of surgery (London, England)
|February 13, 2024
概括
一个新的多任务深度学习 (MDL) 模型使用CT扫描来预测结直肠癌 (CRC) 患者的瘤-肌瘤比率和存活率. 这种人工智能工具有助于个性化治疗决策,并确定化疗的好处.
科学领域:
- 在瘤学瘤学.
- 放射学 放射学是一门学科.
- 人工智能在医学中的应用
背景情况:
- 瘤-肌瘤比率 (TSR) 是结直肠癌 (CRC) 的预后指标.
- 目前的组织学TSR评估由于组织可访问性和空间异质性而面临限制.
- 开发非侵入性方法来预测TSR和预后对于CRC管理至关重要.
研究的目的:
- 开发和验证一种多任务深度学习 (MDL) 模型,用于对CRC患者的TSR和整体存活率的非侵入性预测.
- 评估模型预测预后的能力和辅助化疗的潜在益处.
主要方法:
- 这是一项回顾性研究,涉及来自四个中心的2268名CRC患者.
- 使用手术前计算机断层扫描 (CT) 图像开发一个MDL模型.
- 在内部和外部队列中验证模型,以进行TSR和生存预测的歧视和校准.
主要成果:
- 该MDL模型在预测TSR方面取得了很高的准确性 (AUC从0.838到0.857不等).
- 该模型有效地预测了所有验证队伍的整体生存率和无疾病生存率.
- 高MDL分数 (MDLS) 预测了II期和III期CRC患者的辅助化疗的益处.
结论:
- 开发的MDL模型使用CRC患者的手术前CT图像准确预测TSR和生存率.
- 这种人工智能驱动的方法为个性化治疗策略和化疗选择提供了潜力.
- 需要进一步的前性研究来确认该模型的临床实用性,特别是用于化学疗法益处预测.
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