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已发表论文
基于CT的影像组学用于无创预测肝细胞癌Ki-67表达
Authors Wu M , Du Z, Xiao Y, Wang Y, Yang J, Li Z, Liu X, Yang S, An T
Received 11 July 2025
Accepted for publication 25 December 2025
Published 21 January 2026 Volume 2026:13 552997
DOI https://doi.org/10.2147/JHC.S552997
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 3
Editor who approved publication: Dr Ahmed Kaseb
Meilong Wu,1,* Zhiyong Du,1,* Ying Xiao,2 Yan Wang,3 Jintao Yang,1 Zhike Li,1 Xini Liu,1 Shizhong Yang,4 Tailai An1
1Division of Hepatobiliary and Pancreas Surgery, Department of General Surgery, Shenzhen People’s Hospital (The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, Guangdong, People’s Republic of China; 2Department of Pathology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, People’s Republic of China; 3Department of Radiology, Shenzhen People’s Hospital (The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, Guangdong, People’s Republic of China; 4Hepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Institute for Precision Medicine, Tsinghua University, Beijing, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Tailai An, Division of Hepatobiliary and Pancreas Surgery, Department of General Surgery, Shenzhen People’s Hospital (The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, Guangdong, 518002, People’s Republic of China, Email antailai@mail2.sysu.edu.cn Shizhong Yang, Hepato-Pancreato-Biliary Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, 102218, People’s Republic of China, Email hepatobiliary2002@aliyun.com
Purpose: By far, non-invasive methods assessing Ki-67 are still scarce. This study was performed to evaluate the capability of radiomics based on contrast-enhanced CT in predicting expression of Ki-67 in hepatocellular carcinoma (HCC).
Patients and Methods: HCC patients who underwent curative hepatectomy were included. The optimal Ki-67 cutoff value for prognostic stratification was determined using maximum selection rank statistics. The Least Absolute Selection and Shrinkage Operator (LASSO) regression analysis was used for dimension reduction and data screening to obtain radiomics features. The radiomics model, clinical model and combined model integrating radiomics features and clinical indicators for predicting Ki-67 were constructed. The predictive efficacy among the models was compared using C-index, and further verified through DeLong test, decision curves and clinical impact curves.
Results: The optimal cutoff value for Ki-67 was 0.25. A total of 2553 radiomics features were obtained and after stability testing (by intraclass correlation coefficient ≥ 0.75) and feature selection (LASSO), four radiomics features that were highly correlated and stable with the expression of Ki-67 were included in the model construction. Multivariate analyses revealed that alpha-fetoprotein and intratumoral necrosis or radiomics features were independent predictors of Ki-67. The clinical model, radiomics model and combined model were constructed, respectively. The C-indices of Ki-67 for clinical model, radiomics model and the combined model were 0.75, 0.82 and 0.88. DeLong test, decision curves and clinical impact curves further confirmed that the inclusion of radiomics features improved the predictive efficacy of the model.
Conclusion: The comprehensive model based on contrast-enhanced CT radiomics could non-invasively and effectively predict the expression of Ki-67, suggesting its value in clinical decision-making.
Keywords: hepatocellular carcinoma, radiomics, Ki-67, prognosis
1Division of Hepatobiliary and Pancreas Surgery, Department of General Surgery, Shenzhen People’s Hospital (The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, Guangdong, People’s Republic of China; 2Department of Pathology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, People’s Republic of China; 3Department of Radiology, Shenzhen People’s Hospital (The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, Guangdong, People’s Republic of China; 4Hepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Institute for Precision Medicine, Tsinghua University, Beijing, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Tailai An, Division of Hepatobiliary and Pancreas Surgery, Department of General Surgery, Shenzhen People’s Hospital (The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, Guangdong, 518002, People’s Republic of China, Email antailai@mail2.sysu.edu.cn Shizhong Yang, Hepato-Pancreato-Biliary Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, 102218, People’s Republic of China, Email hepatobiliary2002@aliyun.com
Purpose: By far, non-invasive methods assessing Ki-67 are still scarce. This study was performed to evaluate the capability of radiomics based on contrast-enhanced CT in predicting expression of Ki-67 in hepatocellular carcinoma (HCC).
Patients and Methods: HCC patients who underwent curative hepatectomy were included. The optimal Ki-67 cutoff value for prognostic stratification was determined using maximum selection rank statistics. The Least Absolute Selection and Shrinkage Operator (LASSO) regression analysis was used for dimension reduction and data screening to obtain radiomics features. The radiomics model, clinical model and combined model integrating radiomics features and clinical indicators for predicting Ki-67 were constructed. The predictive efficacy among the models was compared using C-index, and further verified through DeLong test, decision curves and clinical impact curves.
Results: The optimal cutoff value for Ki-67 was 0.25. A total of 2553 radiomics features were obtained and after stability testing (by intraclass correlation coefficient ≥ 0.75) and feature selection (LASSO), four radiomics features that were highly correlated and stable with the expression of Ki-67 were included in the model construction. Multivariate analyses revealed that alpha-fetoprotein and intratumoral necrosis or radiomics features were independent predictors of Ki-67. The clinical model, radiomics model and combined model were constructed, respectively. The C-indices of Ki-67 for clinical model, radiomics model and the combined model were 0.75, 0.82 and 0.88. DeLong test, decision curves and clinical impact curves further confirmed that the inclusion of radiomics features improved the predictive efficacy of the model.
Conclusion: The comprehensive model based on contrast-enhanced CT radiomics could non-invasively and effectively predict the expression of Ki-67, suggesting its value in clinical decision-making.
Keywords: hepatocellular carcinoma, radiomics, Ki-67, prognosis