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Preoperative Prediction of Cytokeratin 19 Expression for Hepatocellular Carcinoma with Deep Learning Radiomics Based on Gadoxetic Acid-Enhanced Magnetic Resonance Imaging
Journal of Hepatocellular Carcinoma ( IF 4.2 ) Pub Date : 2021-07-22 , DOI: 10.2147/jhc.s313879 Yuying Chen 1 , Jia Chen 2 , Yu Zhang 3 , Zhi Lin 1 , Meng Wang 1 , Lifei Huang 2 , Mengqi Huang 1 , Mimi Tang 1 , Xiaoqi Zhou 1 , Zhenpeng Peng 1 , Bingsheng Huang 2 , Shi-Ting Feng 1
Journal of Hepatocellular Carcinoma ( IF 4.2 ) Pub Date : 2021-07-22 , DOI: 10.2147/jhc.s313879 Yuying Chen 1 , Jia Chen 2 , Yu Zhang 3 , Zhi Lin 1 , Meng Wang 1 , Lifei Huang 2 , Mengqi Huang 1 , Mimi Tang 1 , Xiaoqi Zhou 1 , Zhenpeng Peng 1 , Bingsheng Huang 2 , Shi-Ting Feng 1
Affiliation
Purpose: Cytokeratin 19 (CK19) expression is a proven independent prognostic predictor of hepatocellular carcinoma (HCC). This study aimed to develop and validate the performance of a deep learning radiomics (DLR) model for CK19 identification in HCC based on preoperative gadoxetic acid-enhanced magnetic resonance imaging (MRI).
Patients and Methods: A total of 141 surgically confirmed HCCs with preoperative gadoxetic acid-enhanced MRI from two institutions were included. Prediction models were established based on hepatobiliary phase (HBP) images using a training set (n=102) and validated using time-independent (n=19) and external (n=20) test sets. A receiver operating characteristic curve was used to evaluate the performance for CK19 prediction. Recurrence-free survival (RFS) was also analyzed by incorporating the CK19 expression and other factors.
Results: For predicting CK19 expression, the area under the curve (AUC) of the DLR model was 0.820 (95% confidence interval [CI]: 0.732– 0.907, P< 0.001) with sensitivity, specificity, accuracy of 0.800, 0.766, and 0.775, respectively, and reached 0.781 in the external test set. Combined with alpha fetoprotein, the AUC increased to 0.833 (95% CI: 0.753– 0.912, P< 0.001) and the sensitivity was 0.960. Intratumoral hemorrhage and peritumoral hypointensity on HBP were independent risk factors for HCC recurrence by multivariate analysis. Based on predicted CK19 expression and the independent risk factors, a nomogram was developed to predict RFS and achieved C-index of 0.707.
Conclusion: This study successfully established and verified an optimal DLR model for preoperative prediction of CK19-positive HCCs based on gadoxetic acid-enhanced MRI. The prediction of CK19 expression in HCC using a non-invasive method can help inform preoperative planning.
Keywords: hepatocellular carcinoma, gadoxetic acid, magnetic resonance imaging, cytokeratin 19, deep learning radiomics
中文翻译：
基于钆塞酸增强磁共振成像的深度学习放射组学对肝细胞癌细胞角蛋白 19 表达的术前预测
目的：细胞角蛋白 19 (CK19) 的表达被证明是肝细胞癌 (HCC) 的独立预后预测因子。本研究旨在开发和验证基于术前钆塞酸增强磁共振成像 (MRI) 的 HCC CK19 识别的深度学习放射组学 (DLR) 模型的性能。
患者和方法：共纳入来自两个机构的 141 例手术证实的 HCC，术前钆塞酸增强 MRI。使用训练集 (n=102) 基于肝胆期 (HBP) 图像建立预测模型，并使用与时间无关的 (n=19) 和外部 (n=20) 测试集进行验证。受试者工作特征曲线用于评估 CK19 预测的性能。还通过结合 CK19 表达和其他因素来分析无复发生存期 (RFS)。
结果：对于预测 CK19 表达，DLR 模型的曲线下面积 (AUC) 为 0.820（95% 置信区间 [CI]：0.732–0.907，P< 0.001)，灵敏度、特异性、准确度分别为 0.800、0.766 和 0.775，在外部测试集中达到 0.781。结合甲胎蛋白，AUC 增加到 0.833（95% CI：0.753–0.912，P< 0.001），敏感性为 0.960。多因素分析显示，瘤内出血和瘤周HBP低信号是HCC复发的独立危险因素。基于预测的 CK19 表达和独立危险因素，开发了一个列线图来预测 RFS，并实现了 0.707 的 C 指数。
结论：本研究成功建立并验证了基于钆塞酸增强 MRI 的 CK19 阳性 HCC 术前预测的最佳 DLR 模型。使用非侵入性方法预测 HCC 中 CK19 的表达有助于为术前计划提供信息。
关键词：肝细胞癌，钆塞酸，磁共振成像，细胞角蛋白19，深度学习放射组学
更新日期：2021-07-21
Patients and Methods: A total of 141 surgically confirmed HCCs with preoperative gadoxetic acid-enhanced MRI from two institutions were included. Prediction models were established based on hepatobiliary phase (HBP) images using a training set (n=102) and validated using time-independent (n=19) and external (n=20) test sets. A receiver operating characteristic curve was used to evaluate the performance for CK19 prediction. Recurrence-free survival (RFS) was also analyzed by incorporating the CK19 expression and other factors.
Results: For predicting CK19 expression, the area under the curve (AUC) of the DLR model was 0.820 (95% confidence interval [CI]: 0.732– 0.907, P< 0.001) with sensitivity, specificity, accuracy of 0.800, 0.766, and 0.775, respectively, and reached 0.781 in the external test set. Combined with alpha fetoprotein, the AUC increased to 0.833 (95% CI: 0.753– 0.912, P< 0.001) and the sensitivity was 0.960. Intratumoral hemorrhage and peritumoral hypointensity on HBP were independent risk factors for HCC recurrence by multivariate analysis. Based on predicted CK19 expression and the independent risk factors, a nomogram was developed to predict RFS and achieved C-index of 0.707.
Conclusion: This study successfully established and verified an optimal DLR model for preoperative prediction of CK19-positive HCCs based on gadoxetic acid-enhanced MRI. The prediction of CK19 expression in HCC using a non-invasive method can help inform preoperative planning.
Keywords: hepatocellular carcinoma, gadoxetic acid, magnetic resonance imaging, cytokeratin 19, deep learning radiomics
中文翻译：
基于钆塞酸增强磁共振成像的深度学习放射组学对肝细胞癌细胞角蛋白 19 表达的术前预测
目的：细胞角蛋白 19 (CK19) 的表达被证明是肝细胞癌 (HCC) 的独立预后预测因子。本研究旨在开发和验证基于术前钆塞酸增强磁共振成像 (MRI) 的 HCC CK19 识别的深度学习放射组学 (DLR) 模型的性能。
患者和方法：共纳入来自两个机构的 141 例手术证实的 HCC，术前钆塞酸增强 MRI。使用训练集 (n=102) 基于肝胆期 (HBP) 图像建立预测模型，并使用与时间无关的 (n=19) 和外部 (n=20) 测试集进行验证。受试者工作特征曲线用于评估 CK19 预测的性能。还通过结合 CK19 表达和其他因素来分析无复发生存期 (RFS)。
结果：对于预测 CK19 表达，DLR 模型的曲线下面积 (AUC) 为 0.820（95% 置信区间 [CI]：0.732–0.907，P< 0.001)，灵敏度、特异性、准确度分别为 0.800、0.766 和 0.775，在外部测试集中达到 0.781。结合甲胎蛋白，AUC 增加到 0.833（95% CI：0.753–0.912，P< 0.001），敏感性为 0.960。多因素分析显示，瘤内出血和瘤周HBP低信号是HCC复发的独立危险因素。基于预测的 CK19 表达和独立危险因素，开发了一个列线图来预测 RFS，并实现了 0.707 的 C 指数。
结论：本研究成功建立并验证了基于钆塞酸增强 MRI 的 CK19 阳性 HCC 术前预测的最佳 DLR 模型。使用非侵入性方法预测 HCC 中 CK19 的表达有助于为术前计划提供信息。
关键词：肝细胞癌，钆塞酸，磁共振成像，细胞角蛋白19，深度学习放射组学
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