当前位置：
X-MOL 学术
›
Br. J. Cancer
›
论文详情
Our official English website, www.x-mol.net, welcomes your
feedback! (Note: you will need to create a separate account there.)
MRI-based radiomics model for preoperative prediction of 5-year survival in patients with hepatocellular carcinoma.
British Journal of Cancer ( IF 6.4 ) Pub Date : 2020-01-15 , DOI: 10.1038/s41416-019-0706-0 Xiao-Hang Wang 1 , Liu-Hua Long 1 , Yong Cui 2 , Angela Y Jia 3 , Xiang-Gao Zhu 1 , Hong-Zhi Wang 1 , Zhi Wang 4 , Chong-Ming Zhan 4 , Zhao-Hai Wang 5 , Wei-Hu Wang 1
British Journal of Cancer ( IF 6.4 ) Pub Date : 2020-01-15 , DOI: 10.1038/s41416-019-0706-0 Xiao-Hang Wang 1 , Liu-Hua Long 1 , Yong Cui 2 , Angela Y Jia 3 , Xiang-Gao Zhu 1 , Hong-Zhi Wang 1 , Zhi Wang 4 , Chong-Ming Zhan 4 , Zhao-Hai Wang 5 , Wei-Hu Wang 1
Affiliation
|
BACKGROUND
Recurrence is the major cause of mortality in patients with resected HCC. However, without a standard approach to evaluate prognosis, it is difficult to select candidates for additional therapy.
METHODS
A total of 201 patients with HCC who were followed up for at least 5 years after curative hepatectomy were enrolled in this retrospective, multicentre study. A total of 3144 radiomics features were extracted from preoperative MRI. The random forest method was used for radiomics signature building, and five-fold cross-validation was applied. A radiomics model incorporating the radiomics signature and clinical risk factors was developed.
RESULTS
Patients were divided into survivor (n = 97) and non-survivor (n = 104) groups based on the 5-year survival after surgery. The 30 most survival-related radiomics features were selected for the radiomics signature. Preoperative AFP and AST were integrated into the model as independent clinical risk factors. The model demonstrated good calibration and satisfactory discrimination, with a mean AUC of 0.9804 and 0.7578 in the training and validation sets, respectively.
CONCLUSIONS
This radiomics model is a valid method to predict 5-year survival in patients with HCC and may be used to identify patients for clinical trials of perioperative therapies and for additional surveillance.
中文翻译：
基于MRI的放射学模型可在术前预测肝细胞癌患者的5年生存率。
背景技术复发是切除的HCC患者死亡的主要原因。但是，如果没有用于评估预后的标准方法，则很难选择其他疗法的候选者。方法这项回顾性多中心研究共纳入201例肝癌患者，这些患者在根治性肝切除术后至少随访了5年。术前MRI共提取了3144个放射学特征。使用随机森林方法进行放射学签名构建，并应用五重交叉验证。建立了结合了放射学特征和临床危险因素的放射学模型。结果根据术后5年生存率将患者分为幸存者组（n = 97）和非幸存者（n = 104）。选择了30种与生存最相关的放射线特征作为放射线特征。术前AFP和AST作为独立的临床危险因素纳入模型。该模型显示出良好的校准和令人满意的辨别力，训练和验证集中的平均AUC分别为0.9804和0.7578。结论该放射学模型是预测HCC患者5年生存率的有效方法，可用于识别患者以进行围手术期治疗的临床试验和其他监测。
更新日期：2020-01-15
中文翻译：
基于MRI的放射学模型可在术前预测肝细胞癌患者的5年生存率。
背景技术复发是切除的HCC患者死亡的主要原因。但是，如果没有用于评估预后的标准方法，则很难选择其他疗法的候选者。方法这项回顾性多中心研究共纳入201例肝癌患者，这些患者在根治性肝切除术后至少随访了5年。术前MRI共提取了3144个放射学特征。使用随机森林方法进行放射学签名构建，并应用五重交叉验证。建立了结合了放射学特征和临床危险因素的放射学模型。结果根据术后5年生存率将患者分为幸存者组（n = 97）和非幸存者（n = 104）。选择了30种与生存最相关的放射线特征作为放射线特征。术前AFP和AST作为独立的临床危险因素纳入模型。该模型显示出良好的校准和令人满意的辨别力，训练和验证集中的平均AUC分别为0.9804和0.7578。结论该放射学模型是预测HCC患者5年生存率的有效方法，可用于识别患者以进行围手术期治疗的临床试验和其他监测。
京公网安备 11010802027423号