Data_Sheet_1_Diagnosis of Cardiac Amyloidosis Using a Radiomics Approach Applied to Late Gadolinium-Enhanced Cardiac Magnetic Resonance Images: A Retrospective, Multicohort, Diagnostic Study.pdf

ObjectivesTo assess the potential of a radiomics approach of late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) in the diagnosis of cardiac amyloidosis (CA).
Materials and MethodsThis retrospective study included 200 patients with biopsy-proven light-chain (AL) amyloidosis. CA was diagnosed on the basis of systemic amyloidosis confirmed with evidence of cardiac involvement by imaging and clinical biomarkers. A total of 139 patients [54 ± 8 years, 75 (54%) men] in our institution were divided into training cohort [n = 97, mean age of 53 ± 8 years, 54 (56%) men] and internal validation cohort [n = 42, mean age: 56 ± 8 years, 21 (50%) men] with a ratio of 7:3, while 61 patients [mean age: 60 ± 9 years, 42 (69%) men] from the other two institutions were enrolled for external validation. Radiomics features were extracted from global (all short-axis images from base-to-apex) left ventricular (LV) myocardium and three different segments (basal, midventricular, and apex) on short-axis LGE images using the phase-sensitive reconstruction (PSIR) sequence. The Boruta algorithm was used to select the radiomics features. This model was built using the XGBoost algorithm. The two readers performed qualitative and semiquantitative assessment of the LGE images based on the visual LGE patterns, while the quantitative assessment was measured using a dedicated semi-automatic CMR software. The diagnostic performance of the radiomics and other qualitative and quantitative parameters were compared by a receiver operating characteristic (ROC) curve analysis. A correlation between radiomics and the degree of myocardial involvement by amyloidosis was tested.
ResultsA total of 1,906 radiomics features were extracted for each LV section. No statistical significance was indicated between any two slices for diagnosing CA, and the highest area under the curve (AUC) was found in basal section {0.92 [95% confidence interval (CI), 0.86–0.97] in the LGE images in the training set, 0.89 (95% CI, 0.79–1.00) in the internal validation set, and 0.92 (95% CI, 0.85–0.99) in the external validation set}, which was superior to the visual assessment and quantitative LGE parameters. Moderate correlations between global or basal radiomics scores (Rad-scores) and Mayo stage in all patients were reported (Spearman’s Rho = 0.61, 0.62; all p < 0.01).
ConclusionA radiomics analysis of the LGE images provides incremental information compared with the visual assessment and quantitative parameters on CMR to diagnose CA. Radiomics was moderately correlated with the severity of CA. Further studies are needed to assess the prognostic significance of radiomics in patients with CA.

研究目的 评估钆延迟增强（late gadolinium enhancement, LGE）心脏磁共振（cardiac magnetic resonance, CMR）的放射组学方法在心脏淀粉样变（cardiac amyloidosis, CA）诊断中的潜力。
材料与方法 本回顾性研究纳入200例经活检证实的轻链（light-chain, AL）淀粉样变患者。心脏淀粉样变的诊断依据为系统性淀粉样变确诊，且经影像学及临床生物标志物证实存在心脏受累。本机构纳入的139例患者[年龄54±8岁，男性75例（54%）]按7:3比例分为训练队列[n=97，平均年龄53±8岁，男性54例（56%）]与内部验证队列[n=42，平均年龄56±8岁，男性21例（50%）]；另外两家机构的61例患者[平均年龄60±9岁，男性42例（69%）]被纳入外部验证队列。采用相位敏感重建（phase-sensitive reconstruction, PSIR）序列的短轴LGE图像，从全局（从基底部到心尖部的所有短轴图像）左心室（left ventricular, LV）心肌及三个不同节段（基底部、心室中部、心尖部）中提取放射组学特征。使用Boruta算法筛选放射组学特征，基于XGBoost算法构建模型。两位研究者基于视觉LGE模式对LGE图像进行定性与半定量评估，定量评估则采用专用半自动CMR软件完成。通过受试者工作特征（receiver operating characteristic, ROC）曲线分析比较放射组学及其他定性、定量参数的诊断效能。检验放射组学特征与淀粉样变心肌受累程度的相关性。
结果 每个LV节段共提取1906个放射组学特征。任意两个切片在CA诊断中的差异均无统计学意义，基底部切片的曲线下面积（area under the curve, AUC）最高{训练集LGE图像中为0.92[95%置信区间（confidence interval, CI）：0.86~0.97]，内部验证集中为0.89（95%CI：0.79~1.00），外部验证集中为0.92（95%CI：0.85~0.99）}，其诊断效能优于视觉评估及定量LGE参数。所有患者的全局或基底部放射组学评分（Rad-scores）与梅奥分期（Mayo stage）呈中等程度相关（Spearman秩相关系数=0.61、0.62；均p<0.01）。
结论 相较于CMR上的视觉评估与定量参数，LGE图像的放射组学分析可提供额外的诊断信息。放射组学评分与CA的严重程度呈中等程度相关。未来仍需进一步研究以评估放射组学在CA患者中的预后价值。