European Radiology:基于AI的全自动脾分割在肝癌TACE患者风险分层中的应用

2022-08-15 网络 网络

随着人工智能领域的发展,该技术为临床提供了关于自动器官分割和体积评估的可能,可以很容易地实时整合到临床工作流程中。

肝细胞癌(HCC)是世界上最常见的原发性肝癌,在造成癌症相关死亡的疾病中排名第二。众所周知超过80%的HCC是由肝硬化引起的。因此,在做出最佳治疗决定时,必须同时评估肿瘤负荷和残余肝脏功能。

除了损害肝脏蛋白质的合成,肝硬化还导致了脾脏循环的渐进性变化。在肝硬化期间,持续的组织重组导致门静脉压力的增加,最终导致胃食管静脉曲张、腹水和脾脏体积增大。因此,脾脏体积的增大与严重的肝硬化有关。因此,脾脏体积已被确定为接受切除术或肿瘤消融术的HCC患者的一个高度敏感的预后参数。在一项针对接受经动脉化疗栓塞术(TACE)的HCC患者的初步研究中,脾脏体积被确定为一个相关的预后因素。

随着人工智能领域的发展,该技术为临床提供了关于自动器官分割和体积评估的可能,可以很容易地实时整合到临床工作流程中。因此,脾脏体积可能成为治疗计划和TACE后随访的一个容易评估和随时可用的预后因素。

近日,发表在European Radiology杂志的一项研究建立了一个基于CT图像的用于全自动脾脏体积评估的深度学习算法,验证了总脾脏体积作为一种新的影像学生存预测指标物的临床价值,并研究了其评估接受TACE的HCC患者肝功能失调预测指标的作用。

项回顾性研究包括2010年-2020年间在本机构三级护理中心接受初次TACE的327名治疗无效的HCC患者。在前100个连续病例上训练并验证了卷积神经网络的脾脏分割。然后,使用该算法评估了所有327名患者的SV,评估了SV与生存率之间的相关性,以及TACE期间肝功能失调的风险。 

该算法在训练和验证期间显示Sørensen Dice评分为0.96分。在其余227名使用该算法评估的患者中,223名患者(98.2%)的脾脏分割得到了视觉认可,4名患者(1.8%)的脾脏分割失败,需要人工重新评估。平均SV为551毫升。与低SV相比,高SV患者的生存期(10.9个月)明显较低(22.0个月,P = 0.001)。此外,TACE后出现肝功能失调的患者,其SV值明显较高(P < 0.001)。 


 算法性能的代表性图像(从左到右是脾脏上部、中部和下部的图像)。A完美的分割,B可接受的分割(中间有轻微的分割错误),C差的分割(上部有主要的分割错误,有吻合的肝脏和脾脏现象)

本研究表明,本研究提出的深度学习算法可以对接受TACE的HCC患者进行全自动的脾脏体积评估。与已有的脾脏体积的二维估计相比,本研究的算法提供了更精确的脾脏体积评估,在预测生存率方面显示出更明显的优越性,在识别有肝脏失代偿风险的患者方面显示出高度的敏感性。因此,真正的脾脏体积可以作为一个额外的成像生物标志物,在每次CT检查中无需额外的扫描及处理就可以完全自动获得。

 

原文出处:

Lukas Müller,Roman Kloeckner,Aline Mähringer-Kunz,et al.Fully automated AI-based splenic segmentation for predicting survival and estimating the risk of hepatic decompensation in TACE patients with HCC.DOI:10.1007/s00330-022-08737-z

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    2023-01-05 shanyongle
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    2022-10-23 feather89
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    2023-02-02 grace5700
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