基于人工智能的磁共振成像放射组学有助于评估单发性肝细胞癌的预后。

Magnetic resonance imaging radiomics based on artificial intelligence is helpful to evaluate the prognosis of single hepatocellular carcinoma.

作者信息

Zhou Jing, Yang Daofeng, Tang Hao

机构信息

Department of Infectious Diseases, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

出版信息

Heliyon. 2025 Jan 7;11(1):e41735. doi: 10.1016/j.heliyon.2025.e41735. eCollection 2025 Jan 15.

DOI:10.1016/j.heliyon.2025.e41735

PMID:39866463

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11761343/

Abstract

BACKGROUND

Previous studies mostly use single-type features to establish a prediction model. We aim to develop a comprehensive prediction model that effectively identify patients with poor prognosis for single hepatocellular carcinoma (HCC) based on artificial intelligence (AI). : 236 single HCC patients were studied to establish a comprehensive prediction model. We collected the basic information of patients and used AI to extract the features of magnetic resonance (MR) images.

RESULTS

The clinical model based on linear regression (LR) algorithm (AUC: 0.658, 95%CI: 0.5021-0.8137), the radiomics model and deep transfer learning (DTL) model based on light gradient-boosting machine (Light GBM) algorithm (AUC: 0.761, 95%CI: 0.6326-0.8886 and AUC: 0.784, 95%CI: 0.6587-0.9087, respectively) were the optimal prediction models. A comparison revealed that the integrated nomogram had the largest area under the receiver operating characteristic curve (AUC) (all P < 0.05). In the training cohort, the integrated nomogram was predictive of recurrence-free survival (RFS) as well as overall survival (OS) (C-index: 0.735 and 0.712, P < 0.001). In the test cohort, the integrated nomogram also can predict RFS and OS (C-index: 0.718 and 0.740, P < 0.001) in patients.

CONCLUSION

The integrated nomogram composed of signatures in the prediction models can not only predict the postoperative recurrence of single HCC patients but also stratify the risk of OS after the operation.

摘要

背景

以往研究大多使用单一类型特征来建立预测模型。我们旨在基于人工智能（AI）开发一种综合预测模型，以有效识别单发性肝细胞癌（HCC）预后不良的患者。对236名单发性HCC患者进行研究以建立综合预测模型。我们收集了患者的基本信息，并使用AI提取磁共振（MR）图像的特征。

结果

基于线性回归（LR）算法的临床模型（AUC：0.658，95%CI：0.5021 - 0.8137）、基于轻梯度提升机（Light GBM）算法的影像组学模型和深度迁移学习（DTL）模型（AUC分别为：0.761，95%CI：0.6326 - 0.8886和0.784，95%CI：0.6587 - 0.9087）是最优预测模型。比较显示，综合列线图在受试者操作特征曲线下面积（AUC）最大（所有P < 0.05）。在训练队列中，综合列线图可预测无复发生存期（RFS）以及总生存期（OS）（C指数：0.735和0.712，P < 0.001）。在测试队列中，综合列线图也能预测患者的RFS和OS（C指数：0.718和0.740，P < 0.001）。