Department of Nuclear Medicine, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China.
Department of Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China.
Eur Radiol. 2024 Jan;34(1):402-410. doi: 10.1007/s00330-023-09999-x. Epub 2023 Aug 8.
To evaluate the prognostic value of radiomics features based on late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) images in patients with cardiac amyloidosis (CA).
This retrospective study included 120 CA patients undergoing CMR at three institutions. Radiomics features were extracted from global and three different segments (base, mid-ventricular, and apex) of left ventricular (LV) on short-axis LGE images. Primary endpoint was all-cause mortality. The predictive performance of the radiomics features and semi-quantitative and quantitative LGE parameters were compared by ROC. The AUC was used to observe whether Rad-score had an incremental value for clinical stage. The Kaplan-Meier curve was used to further stratify the risk of CA patients.
During a median follow-up of 12.9 months, 30% (40/120) patients died. There was no significant difference in the predictive performance of the radiomics model in different LV sections in the validation set (AUCs of the global, basal, middle, and apical radiomics model were 0.75, 0.77, 0.76, and 0.77, respectively; all p > 0.05). The predictive performance of the Rad-score of the base-LV was better than that of the LGE total enhancement mass (AUC:0.77 vs. 0.54, p < 0.001) and LGE extent (AUC: 0.77 vs. 0.53, p = 0.004). Rad-score combined with Mayo stage had better predictive performance than Mayo stage alone (AUC: 0.86 vs. 0.81, p = 0.03). Rad-score (≥ 0.66) contributed to the risk stratification of all-cause mortality in CA.
Compared to quantitative LGE parameters, radiomics can better predict all-cause mortality in CA, while the combination of radiomics and Mayo stage could provide higher predictive accuracy.
Radiomics analysis provides incremental value and improved risk stratification for all-cause mortality in patients with cardiac amyloidosis.
• Radiomics in LV-base was superior to LGE semi-quantitative and quantitative parameters for predicting all-cause mortality in CA. • Rad-score combined with Mayo stage had better predictive performance than Mayo stage alone or radiomics alone. • Rad-score ≥ 0.66 was associated with a significantly increased risk of all-cause mortality in CA patients.
评估基于钆延迟增强(LGE)心脏磁共振(CMR)图像的影像组学特征对心脏淀粉样变性(CA)患者的预后价值。
这项回顾性研究纳入了在三个机构接受 CMR 检查的 120 例 CA 患者。从左心室(LV)短轴 LGE 图像的整体和三个不同节段(基底、中-心室和心尖)提取影像组学特征。主要终点是全因死亡率。通过 ROC 比较影像组学特征和半定量及定量 LGE 参数的预测性能。AUC 用于观察 Rad-score 是否对临床分期有增量价值。Kaplan-Meier 曲线进一步对 CA 患者的风险进行分层。
在中位随访 12.9 个月期间,30%(40/120)的患者死亡。在验证组中,不同 LV 节段的影像组学模型的预测性能无显著差异(整体、基底、中间和心尖影像组学模型的 AUC 分别为 0.75、0.77、0.76 和 0.77;均 p>0.05)。基底部 LV 的 Rad-score 的预测性能优于 LGE 总强化质量(AUC:0.77 与 0.54,p<0.001)和 LGE 范围(AUC:0.77 与 0.53,p=0.004)。Rad-score 联合 Mayo 分期比 Mayo 分期单独具有更好的预测性能(AUC:0.86 与 0.81,p=0.03)。Rad-score(≥0.66)有助于 CA 患者全因死亡率的风险分层。
与定量 LGE 参数相比,影像组学可更好地预测 CA 患者的全因死亡率,而影像组学与 Mayo 分期的结合可提供更高的预测准确性。
影像组学分析为心脏淀粉样变性患者的全因死亡率提供了附加价值和改善的风险分层。
左心室基底部的放射组学优于 CA 中 LGE 半定量和定量参数,可预测全因死亡率。
Rad-score 联合 Mayo 分期比 Mayo 分期或放射组学单独具有更好的预测性能。
Rad-score≥0.66 与 CA 患者全因死亡率显著增加相关。