Department of Diagnostic and Interventional Radiology, University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckst. 1, 55131, Mainz, Germany.
Department of Internal Medicine I, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany.
Eur Radiol. 2022 Sep;32(9):6302-6313. doi: 10.1007/s00330-022-08737-z. Epub 2022 Apr 8.
OBJECTIVES: Splenic volume (SV) was proposed as a relevant prognostic factor for patients with hepatocellular carcinoma (HCC). We trained a deep-learning algorithm to fully automatically assess SV based on computed tomography (CT) scans. Then, we investigated SV as a prognostic factor for patients with HCC undergoing transarterial chemoembolization (TACE). METHODS: This retrospective study included 327 treatment-naïve patients with HCC undergoing initial TACE at our tertiary care center between 2010 and 2020. A convolutional neural network was trained and validated on the first 100 consecutive cases for spleen segmentation. Then, we used the algorithm to evaluate SV in all 327 patients. Subsequently, we evaluated correlations between SV and survival as well as the risk of hepatic decompensation during TACE. RESULTS: The algorithm showed Sørensen Dice Scores of 0.96 during both training and validation. In the remaining 227 patients assessed with the algorithm, spleen segmentation was visually approved in 223 patients (98.2%) and failed in four patients (1.8%), which required manual re-assessments. Mean SV was 551 ml. Survival was significantly lower in patients with high SV (10.9 months), compared to low SV (22.0 months, p = 0.001). In contrast, overall survival was not significantly predicted by axial and craniocaudal spleen diameter. Furthermore, patients with a hepatic decompensation after TACE had significantly higher SV (p < 0.001). CONCLUSION: Automated SV assessments showed superior survival predictions in patients with HCC undergoing TACE compared to two-dimensional spleen size estimates and identified patients at risk of hepatic decompensation. Thus, SV could serve as an automatically available, currently underappreciated imaging biomarker. KEY POINTS: • Splenic volume is a relevant prognostic factor for prediction of survival in patients with HCC undergoing TACE, and should be preferred over two-dimensional surrogates for splenic size. • Besides overall survival, progression-free survival and hepatic decompensation were significantly associated with splenic volume, making splenic volume a currently underappreciated prognostic factor prior to TACE. • Splenic volume can be fully automatically assessed using deep-learning methods; thus, it is a promising imaging biomarker easily integrable into daily radiological routine.
目的：脾脏体积（SV）被提出作为肝细胞癌（HCC）患者的相关预后因素。我们训练了一种深度学习算法，以便根据计算机断层扫描（CT）扫描对 SV 进行全自动评估。然后，我们研究了 SV 作为接受经动脉化疗栓塞（TACE）治疗的 HCC 患者的预后因素。
方法：这项回顾性研究纳入了 2010 年至 2020 年期间在我们的三级医疗中心接受初始 TACE 治疗的 327 例初治 HCC 患者。使用前 100 例连续病例对卷积神经网络进行训练和验证，以进行脾脏分割。然后，我们使用该算法对所有 327 例患者进行 SV 评估。随后，我们评估了 SV 与生存以及 TACE 期间肝功能失代偿风险之间的相关性。
结果：在训练和验证过程中，算法的 Sørensen Dice 评分均为 0.96。在使用算法评估的 227 例剩余患者中，223 例（98.2%）的脾脏分割得到了视觉认可，4 例（1.8%）失败，需要进行手动重新评估。平均 SV 为 551ml。与低 SV（22.0 个月，p=0.001）相比，SV 高的患者的生存明显较低。相反，轴向和颅尾脾脏直径并不能显著预测总生存率。此外，TACE 后肝功能失代偿的患者 SV 明显较高（p<0.001）。
结论：与二维脾脏大小估计相比，自动 SV 评估在接受 TACE 治疗的 HCC 患者中显示出更好的生存预测能力，并确定了肝功能失代偿风险的患者。因此，SV 可作为一种自动可用的、目前尚未得到充分重视的影像学生物标志物。
重点：• SV 是预测 HCC 患者 TACE 后生存的一个相关预后因素，应优于二维脾脏大小的替代物。• 除了总生存率之外，无进展生存率和肝功能失代偿与 SV 显著相关，使 SV 成为 TACE 前一个目前尚未得到充分重视的预后因素。• 可以使用深度学习方法对 SV 进行全自动评估；因此，它是一种很有前途的成像生物标志物，很容易整合到日常放射学常规中。
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