Ma Lidi, Li Congrui, Li Haixia, Zhang Cheng, Deng Kan, Zhang Weijing, Xie Chuanmiao
Department of Radiology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, PR China.
Department of Diagnostic Radiology, Hunan Cancer Hospital, Central South University, Changsha, PR China.
Heliyon. 2024 May 16;10(11):e31451. doi: 10.1016/j.heliyon.2024.e31451. eCollection 2024 Jun 15.
To develop a deep learning model based on contrast-enhanced magnetic resonance imaging (MRI) data to predict post-surgical overall survival (OS) in patients with hepatocellular carcinoma (HCC).
This bi-center retrospective study included 564 surgically resected patients with HCC and divided them into training (326), testing (143), and external validation (95) cohorts. This study used a three-dimensional convolutional neural network (3D-CNN) ResNet to learn features from the pretreatment MR images (T1WIpre, late arterial phase, and portal venous phase) and got the deep learning score (DL score). Three cox regression models were established separately using the DL score (3D-CNN model), clinical features (clinical model), and a combination of above (combined model). The concordance index (C-index) was used to evaluate model performance.
We trained a 3D-CNN model to get DL score from samples. The C-index of the 3D-CNN model in predicting 5-year OS for the training, testing, and external validation cohorts were 0.746, 0.714, and 0.698, respectively, and were higher than those of the clinical model, which were 0.675, 0.674, and 0.631, respectively (P = 0.009, P = 0.204, and P = 0.092, respectively). The C-index of the combined model for testing and external validation cohorts was 0.750 and 0.723, respectively, significantly higher than the clinical model (P = 0.017, P = 0.016) and the 3D-CNN model (P = 0.029, P = 0.036).
The combined model integrating the DL score and clinical factors showed a higher predictive value than the clinical and 3D-CNN models and may be more useful in guiding clinical treatment decisions to improve the prognosis of patients with HCC.
基于对比增强磁共振成像（MRI）数据开发一种深度学习模型，以预测肝细胞癌（HCC）患者术后的总生存期（OS）。
这项双中心回顾性研究纳入了564例接受手术切除的HCC患者，并将他们分为训练组（326例）、测试组（143例）和外部验证组（95例）。本研究使用三维卷积神经网络（3D-CNN）ResNet从术前MR图像（T1WIpre、动脉晚期和门静脉期）中学习特征，并获得深度学习评分（DL评分）。分别使用DL评分（3D-CNN模型）、临床特征（临床模型）以及上述两者的组合建立了三个Cox回归模型。一致性指数（C指数）用于评估模型性能。
我们训练了一个3D-CNN模型以从样本中获得DL评分。3D-CNN模型在预测训练组、测试组和外部验证组5年OS时的C指数分别为0.746、0.714和0.698，高于临床模型的C指数，临床模型的C指数分别为0.675、0.674和0.631（P分别为0.009、0.204和0.092）。测试组和外部验证组的联合模型的C指数分别为0.750和0.723，显著高于临床模型（P = 0.017，P = 0.016）和3D-CNN模型（P = 0.029，P = 0.036）。
整合DL评分和临床因素的联合模型显示出比临床模型和3D-CNN模型更高的预测价值，可能在指导临床治疗决策以改善HCC患者的预后方面更有用。
