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基于深度学习的MRI放射学基于自动细分来预测肝细胞癌的早期复发

Hong Wei¹, Tianying Zheng¹, Xiaolan Zhang²

¹Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China.

Insights into imaging

|May 19, 2024

概括

使用MRI放射学的深度学习 (DL) 模型可以预测手术后早期肝细胞癌 (HCC) 复发. 这种混合方法在风险分层方面优于目前的分阶段系统.

科学领域:

放射学放射学是一门学科.
人工智能的人工智能
在瘤学瘤学.

背景情况:

手术切除后肝细胞癌 (HCC) 复发仍然是一个重大的临床挑战.
准确预测早期复发对于个性化治疗策略和改善患者治疗结果至关重要.

研究的目的:

评估深度学习 (DL) 基于自动分段的MRI放射性特征与临床放射学数据相结合的有效性,以预测治疗切除后单个HCC的早期复发.
将这种混合模型的预测性能与BCLC和CNLC等既有分期系统进行比较.

主要方法:

在治疗性肝切除术前,对434名单一HCC患者进行了反差增强MRI后期研究.
在6个MRI序列上使用3D U-net DL算法对肝脏和HCC进行自动细分.
从瘤,瘤边缘扩展和肝脏中提取放射性特征;构建具有最佳放射性特征和手术前特征的混合Cox回归模型.

主要成果:

最优的放射性特征包括5毫米瘤边缘延伸和肝脏特征的HCC (训练C指数,0.696).
混合模型将这种特征与边缘动脉相超增强 (APHE) 和不完整的瘤囊相结合,实现了0.706.0的验证C指数.
混合模型与BCLC (0.550) 和CNLC (0.635) 系统相比,表现出优异的2年时间依赖的AUC (0.743),有效地将患者分为不同的风险组.

结论:

一个整合DL基放射学,边缘APHE和不完整瘤囊的术前成像模型准确地预测了单个HCC的早期术后复发.
这种先进的模型提供了个性化风险评估的潜力,指导HCC患者的术后管理.

关键词:

人工智能的人工智能是人工智能.肝细胞癌 (肝细胞癌)机器学习是机器学习.磁共振成像技术磁共振成像技术复发情况复发情况

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