Pretreatment prediction of immunoscore in hepatocellular cancer: a radiomics-based clinical model based on Gd-EOB-DTPA-enhanced MRI imaging
Pretreatment prediction of immunoscore in hepatocellular cancer: a radiomics-based clinical model based on Gd-EOB-DTPA-enhanced MRI imaging
复制标题
肝细胞癌免疫评分的治疗前预测：基于 Gd-EOB-DTPA 增强 MRI 成像的放射组学临床模型
DOI：
10.1007/s00330-018-5986-x
复制
发表时间：
2019-08-01
影响因子：
5.9
通讯作者：
Kuang, Ming
中科院分区：
文献类型：
--
作者：
Chen, Shuling;Feng, Shiting;Kuang, Ming
目的应用免疫组化技术检测肿瘤中心和浸润边缘的CD3+和CD8 + T细胞密度。肝细胞癌（HCC）免疫评分的治疗前预测对于精确的免疫治疗非常重要。我们的目的是开发一个放射组学模型的基础上钆-乙氧基苄基-二乙烯三胺（Gd-EOB-DTPA）增强MRI的immunoscore（0 - 2与3 - 4）在HCC. Materials and methodsThe研究包括207（培训队列：n = 150;验证队列：n = 57）HCC患者进行肝切除术前Gd-EOB-DTPA增强MRI。在MRI图像的肝胆期手动描绘包括瘤内和瘤周区域的肝脏病变的感兴趣体积，从中提取并分析1044个定量特征。采用极端随机树方法选择放射组学特征构建放射组学模型。比较了三种模型在免疫评分中的预测性能：（1）仅使用肿瘤内放射组学特征（2）使用组合的肿瘤内和肿瘤周围放射组学特征（联合放射组学模型）;（3）使用临床数据和选定的组合放射组学特征（基于放射学的联合临床模型）结果联合放射组学模型对免疫评分的预测效果优于肿瘤内放射组学模型（AUC，0.904（95% CI 0.855 - 0.953）对比0.823（95% CI 0.747 - 0.899））。基于放射组学的联合临床模型在预测免疫评分方面优于联合放射组学模型（AUC，0·926（95% CI 0·884 - 0·967）vs. 0·904（95% CI 0·855 - 0·953）），尽管差异无统计学意义。结论基于MRI的联合放射组学诺模图可有效预测HCC的免疫评分，并有助于制定治疗决策。要点·Gd-EOB-DTPA增强MRI获得的放射组学有助于预测HCC的免疫评分。·联合肿瘤内和肿瘤周围放射组学在预测免疫评分方面优于仅肿瘤内放射组学。·我们开发了一种结合临床和放射组学的免疫组学图来预测肝细胞癌的免疫评分。
ObjectivesImmunoscore evaluates the density of CD3+ and CD8+ T cells in both the tumor core and invasive margin. Pretreatment prediction of immunoscore in hepatocellular cancer (HCC) is important for precision immunotherapy. We aimed to develop a radiomics model based on gadolinium-ethoxybenzyl-diethylenetriamine (Gd-EOB-DTPA)-enhanced MRI for pretreatment prediction of immunoscore (0–2 vs. 3–4) in HCC.Materials and methodsThe study included 207 (training cohort:n= 150; validation cohort:n= 57) HCC patients with hepatectomy who underwent preoperative Gd-EOB-DTPA-enhanced MRI. The volumes of interest enclosing hepatic lesions including intratumoral and peritumoral regions were manually delineated in the hepatobiliary phase of MRI images, from which 1044 quantitative features were extracted and analyzed. Extremely randomized tree method was used to select radiomics features for building radiomics model. Predicting performance in immunoscore was compared among three models: (1) using only intratumoral radiomics features (intratumoral radiomics model); (2) using combined intratumoral and peritumoral radiomics features (combined radiomics model); (3) using clinical data and selected combined radiomics features (combined radiomics-based clinical model).ResultsThe combined radiomics model showed a better predicting performance in immunoscore than intratumoral radiomics model (AUC, 0.904 (95% CI 0.855–0.953) vs. 0.823 (95% CI 0.747–0.899)). The combined radiomics-based clinical model showed an improvement over the combined radiomics model in predicting immunoscore (AUC, 0·926 (95% CI 0·884–0·967) vs. 0·904 (95% CI 0·855–0·953)), although differences were not statistically significant. Results were confirmed in validation cohort and calibration curves showed good agreement.ConclusionThe MRI-based combined radiomics nomogram is effective in predicting immunoscore in HCC and may help making treatment decisions.Key Points• Radiomics obtained from Gd-EOB-DTPA-enhanced MRI help predicting immunoscore in hepatocellular carcinoma.• Combined intratumoral and peritumoral radiomics are superior to intratumoral radiomics only in predicting immunoscore.• We developed a combined clinical and radiomicsnomogram to predict immunoscore in hepatocellular carcinoma.