人工智能将CT图像与质病理特征和存活结果联系起来
Ying Xiong1, Linpeng Yao2, Jinglai Lin3,4,5
1Department of Urology, Zhongshan Hospital, Fudan University, Shanghai, China.
Nature communications
|February 6, 2025
概括
深度学习模型使用计算机断层扫描 (CT) 扫描准确预测质恶性和攻击性. 这些人工智能工具超过了放射学家和放射学家,帮助治疗瘤的治疗决策.
科学领域:
- 放射学中的人工智能
- 医学成像分析 医学成像分析
- 在瘤学瘤学.
背景情况:
- 病理的不确定性使得偶发性质的治疗决策变得复杂.
- 准确诊断良性vs恶性和侵略性vs惰性脏瘤对于最佳的患者管理至关重要.
- 当前的诊断方法往往缺乏明确的准确性,导致不理想的治疗选择.
研究的目的:
- 开发和验证深度学习模型,以非侵入性预测脏质的恶性和攻击性.
- 将这些深度学习模型的性能与人类专家 (放射学家) 和传统计算方法 (放射学) 的性能进行比较.
- 提高质的诊断准确度,从而指导更好的治疗选择.
主要方法:
- 分析了来自4,557名患者的13,261个术前多相计算机断层扫描 (CT) 卷.
- 开发两个卷积神经网络 (CNN):一个用于恶性瘤预测,另一个用于攻击性差异化.
- 对开发的CNN模型进行前性测试,并与放射学模型和肺部计量得分名录进行比较.
主要成果:
- 恶性瘤预测模型在前性测试组中实现了0.871的曲线下的面积 (AUC),超过了七位经验丰富的放射科医生.
- 攻击性差异化模型在前性测试组中实现了0.783的AUC.
- 这两种深度学习模型的性能都比放射学模型和神经测量得分名录相比更好.
结论:
- 深度学习模型可以从手术前的CT图像中准确而非侵入性地预测恶性瘤的可能性和质的攻击性.
- 这些由人工智能驱动的工具比目前的诊断方法,包括放射学和专家放射学家,提供了显著的进步.
- 开发的模型有可能提高治疗选择并改善质患者的治疗结果.
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