CT-based radiomics signature: a potential biomarker for preoperative prediction of early recurrence in hepatocellular carcinoma

2017 ◽  
Vol 42 (6) ◽  
pp. 1695-1704 ◽  
Author(s):  
Ying Zhou ◽  
Lan He ◽  
Yanqi Huang ◽  
Shuting Chen ◽  
Penqi Wu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Early Recurrence ◽  
Preoperative Prediction ◽  
Potential Biomarker ◽  
Radiomics Signature

scholarly journals Spleen Radiomics Signature: A Potential Biomarker for Prediction of Early and Late Recurrences of Hepatocellular Carcinoma After Resection

2021 ◽  
Vol 11 ◽  
Author(s):  
Pinxiong Li ◽  
Lei Wu ◽  
Zhenhui Li ◽  
Jiao Li ◽  
Weitao Ye ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Curative Resection ◽  
Mixed Model ◽  
Cox Regression ◽  
Predictive Performance ◽  
Late Recurrence ◽  
Early Recurrence ◽  
Cox Regression Analysis ◽  
Potential Biomarker ◽  
Radiomics Signature

ObjectivesTo explore the usefulness of spleen radiomics features based on contrast-enhanced computed tomography (CECT) in predicting early and late recurrences of hepatocellular carcinoma (HCC) patients after curative resection.MethodsThis retrospective study included 237 HCC patients who underwent CECT and curative resection between January 2006 to January 2016. Radiomic features were extracted from CECT images, and then the spleen radiomics signatures and the tumor radiomics signatures were built. Cox regression analysis was performed to identify the independent risk factors of early and late recurrences. Then, multiple models were built to predict the recurrence-free survival of HCC after resection, and the incremental value of the radiomics signature to the clinicopathologic model was assessed and validated. Kaplan–Meier survival analysis was used to assess the association of the models with RFS.ResultsThe spleen radiomics signature was independent risk factor of early recurrence of HCC. The mixed model that integrated microvascular invasion, tumor radiomics signature and spleen radiomics signature for the prediction of early recurrence achieved the highest C-index of 0.780 (95% CI: 0.728,0.831) in the primary cohort and 0.776 (95% CI: 0.716,0.836) in the validation cohort, and presented better predictive performance than clinicopathological model and combined model. In the analysis of late recurrence, the spleen radiomics signature was the only prognostic factor associated with late recurrence of HCC.ConclusionsThe identified spleen radiomics signatures are prognostic factors of both early and late recurrences of HCC patients after surgery and improve the predictive performance of model for early recurrence.

scholarly journals Preoperative Prediction for Early Recurrence Can Be as Accurate as Postoperative Assessment in Single Hepatocellular Carcinoma Patients

2020 ◽  
Vol 21 (4) ◽  
pp. 402
Author(s):  
Dong Ik Cha ◽  
Kyung Mi Jang ◽  
Seong Hyun Kim ◽  
Young Kon Kim ◽  
Honsoul Kim ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Early Recurrence ◽  
Postoperative Assessment ◽  
Preoperative Prediction

MRI‐Based Radiomics Signature: A Potential Biomarker for Identifying Glypican 3‐Positive Hepatocellular Carcinoma

2020 ◽  
Vol 52 (6) ◽  
pp. 1679-1687
Author(s):  
Dongsheng Gu ◽  
Yongsheng Xie ◽  
Jingwei Wei ◽  
Wencui Li ◽  
Zhaoxiang Ye ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Potential Biomarker ◽  
Radiomics Signature

A MRI-based radiomics model for preoperative prediction of five-year survival status in hepatocellular carcinoma.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e15596-e15596
Author(s):  
Xiao-Hang Wang ◽  
Liu-Hua Long ◽  
Yong Cui ◽  
Angela Y Jia ◽  
Xiang-Gao Zhu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Model Performance ◽  
Area Under The Curve ◽  
Clinical Risk Factor ◽  
Multivariable Logistic Regression Analysis ◽  
Survival Status ◽  
Clinical Risk ◽  
Preoperative Prediction ◽  
Magnetic Resonance Imaging Mri ◽  
Radiomics Signature

e15596 Background: Recurrence is the major cause of mortality in resected hepatocellular carcinoma (HCC) patients. However, without a standard approach to evaluate prognosis, it is difficult to select potential candidates for additional therapy. We aim to develop and evaluate a magnetic resonance imaging (MRI)-based radiomics model to predict 5-year survival status of HCC patients in the preoperative setting. Methods: A total of 201 HCC patients who were followed up for at least 5 years (unless death occurred) after curative hepatectomy were enrolled in this retrospective multicenter study. 3144 radiomics features were extracted from four conventional sequences of preoperative MRI (T1WI, T2WI, DWI and dynamic contrast-enhanced MRI). The random forest method was used for feature selection and radiomics signature building. 5-fold cross validation was used for robust estimation. A radiomics model incorporating the radiomics signature and clinical risk factors was developed. The model performance was evaluated by its discrimination and calibration. Results: Patients were divided into survivor (n = 97) and non-survivor (n = 104) groups based on survival status at 5 years from surgery. The 30 most survival-related radiomics features were selected to develop the radiomics signature. The preoperative alpha-fetoprotein level was integrated into the model as an independent clinical risk factor in multivariable logistic regression analysis (OR = 3.764; 95% CI 1.997-7.096). The radiomics model demonstrated good calibration and satisfactory discrimination, with the mean area under the curve of 0.9340 (95% CI 0.9222-0.9458) in training set and 0.7383 (95% CI 0.6914-0.7852) in validation set. Conclusions: The MRI-based radiomics model represents a valid method to predict 5-year survival status in HCC patients in the preoperative setting, and may be used to guide neoadjuvant or adjuvant treatment decisions in high-risk patients.

scholarly journals Deep Fusion Models of Multi-Phase CT and Selected Clinical Data for Preoperative Prediction of Early Recurrence in Hepatocellular Carcinoma

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 139212-139220
Author(s):  
Weibin Wang ◽  
Qingqing Chen ◽  
Yutaro Iwamoto ◽  
Panyanat Aonpong ◽  
Lanfen Lin ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Clinical Data ◽  
Early Recurrence ◽  
Preoperative Prediction ◽  
Multi Phase

scholarly journals Radiomics Signature: A potential biomarker for the prediction of survival in Advanced Hepatocellular Carcinoma

2021 ◽  
Vol 18 (11) ◽  
pp. 2276-2284
Author(s):  
Lingli Li ◽  
Xuefeng Kan ◽  
Yongjun Zhao ◽  
Bo Liang ◽  
Tianhe Ye ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Advanced Hepatocellular Carcinoma ◽  
Potential Biomarker ◽  
Prediction Of Survival ◽  
Radiomics Signature
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