Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University, 58 Zhong Shan Road 2, Guangzhou, 510080, China.
Department of Radiology, The First Affiliated Hospital of Sun Yat-sen University, 58 Zhong Shan Road 2, Guangzhou, 510080, China.
Eur Radiol. 2019 Aug;29(8):4177-4187. doi: 10.1007/s00330-018-5986-x. Epub 2019 Jan 21.
Immunoscore 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.
The 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).
The 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.
The MRI-based combined radiomics nomogram is effective in predicting immunoscore in HCC and may help making treatment decisions.
• 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.
免疫评分评估肿瘤核心和浸润边缘处 CD3+和 CD8+T 细胞的密度。在肝细胞癌（HCC）中，免疫评分的术前预测对于精准免疫治疗很重要。本研究旨在建立基于钆塞酸二钠（Gd-EOB-DTPA）增强 MRI 的影像组学模型，以预测 HCC 的免疫评分（0-2 与 3-4）。
本研究纳入了 207 例接受术前 Gd-EOB-DTPA 增强 MRI 检查并接受肝切除术的 HCC 患者（训练队列：n=150；验证队列：n=57）。手动在 MRI 肝胆期图像上勾画包括肿瘤内和肿瘤周围区域的感兴趣区，从中提取并分析了 1044 个定量特征。使用极端随机树法筛选影像组学特征，以建立影像组学模型。比较了三种模型在免疫评分预测中的性能：（1）仅使用肿瘤内影像组学特征（肿瘤内影像组学模型）；（2）使用肿瘤内和肿瘤周围联合影像组学特征（联合影像组学模型）；（3）使用临床数据和选择的联合影像组学特征（联合影像组学基础临床模型）。
联合影像组学模型在预测免疫评分方面的表现优于肿瘤内影像组学模型（AUC：0.904（95%CI 0.855-0.953）比 0.823（95%CI 0.747-0.899））。联合影像组学基础临床模型在预测免疫评分方面优于联合影像组学模型（AUC：0.926（95%CI 0.884-0.967）比 0.904（95%CI 0.855-0.953）），尽管差异无统计学意义。验证队列的结果得到了证实，校准曲线显示出良好的一致性。
基于 MRI 的联合影像组学列线图可有效预测 HCC 的免疫评分，并有助于制定治疗决策。
• 钆塞酸二钠增强 MRI 获得的影像组学有助于预测肝细胞癌的免疫评分。• 肿瘤内和肿瘤周围联合影像组学在预测免疫评分方面优于仅肿瘤内影像组学。• 我们开发了一种联合临床和影像组学列线图来预测肝细胞癌的免疫评分。
