114027
论文已发表
注册即可获取德孚的最新动态
IF 收录期刊
- 3.4 Breast Cancer (Dove Med Press)
- 3.2 Clin Epidemiol
- 2.6 Cancer Manag Res
- 2.9 Infect Drug Resist
- 3.7 Clin Interv Aging
- 5.1 Drug Des Dev Ther
- 3.1 Int J Chronic Obstr
- 6.6 Int J Nanomed
- 2.6 Int J Women's Health
- 2.9 Neuropsych Dis Treat
- 2.8 OncoTargets Ther
- 2.0 Patient Prefer Adher
- 2.2 Ther Clin Risk Manag
- 2.5 J Pain Res
- 3.0 Diabet Metab Synd Ob
- 3.2 Psychol Res Behav Ma
- 3.4 Nat Sci Sleep
- 1.8 Pharmgenomics Pers Med
- 2.0 Risk Manag Healthc Policy
- 4.1 J Inflamm Res
- 2.0 Int J Gen Med
- 3.4 J Hepatocell Carcinoma
- 3.0 J Asthma Allergy
- 2.2 Clin Cosmet Investig Dermatol
- 2.4 J Multidiscip Healthc
已发表论文
CT 增强扫描（CECT）中肿瘤内及肿瘤周的影像组学特征：预测侵袭性肝细胞癌亚型及 2 年复发情况
Authors Ruan F, Li X, Feng L, Jiang S, Li Z, Long L
Received 24 June 2025
Accepted for publication 3 December 2025
Published 21 December 2025 Volume 2025:12 Pages 2875—2891
DOI https://doi.org/10.2147/JHC.S549301
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 3
Editor who approved publication: Dr Hop Tran Cao
Fengqiu Ruan,1,* Xuan Li,1,* Lijuan Feng,1 Shengchen Jiang,1 Zhiming Li,1 Liling Long1– 3
1Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China; 2Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor, Guangxi Medical University, Ministry of Education, Nanning, People’s Republic of China; 3Guangxi Key Laboratory of Immunology and Metabolism for Liver Diseases, Nanning, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Liling Long, Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530000, People’s Republic of China, Email cjr.longliling@vip.163.com
Purpose: To evaluate whether radiomic features from contrast-enhanced computed tomography (CECT) of peritumoral regions can be used to preoperatively predict proliferative hepatocellular carcinoma (PHCC).
Patients and Methods: Preoperative CT scans from 486 patients with hepatocellular carcinoma (HCC) were retrospectively analyzed and split into training (n = 252), testing (n = 109), and validation (n = 125) cohorts. Radiomic features were extracted from intra- and peritumoral regions (peri-3 mm, peri-5 mm, and peri-10 mm) on arterial phase (AP) and portal venous phase (PVP) images using PyRadiomics. Features were selected with LASSO regression and 10-fold cross-validation, and a radiomics score (Radscore) was calculated as a weighted sum of selected features. Patients were classified into high- and low-risk groups using the optimal Youden’s index cutoff. Recurrence-free survival (RFS) was analyzed with Kaplan–Meier curves, feature contributions were quantified using SHapley Additive exPlanations (SHAP), and model performance was assessed by area under the curve (AUC).
Results: The Naive Bayes model using peri-5 mm features achieved the highest mean AUC (0.739) and accuracy (0.802), with AUCs of 0.839 and 0.639 in internal and external validation. In the test set, combining intra- and peritumoral features improved the AUC to 0.849 (95% CI: 0.773– 0.924; sensitivity: 0.974; specificity: 0.606). In the validation set, AP, PVP, and their combined models achieved AUCs of 0.699, 0.672, and 0.695, respectively. SHAP highlighted in the Naive Bayes model that the increased inhomogeneity of the texture grayscale of the peritumoral tissue in the PVP may be associated with more aggressive HCC subtypes. Multivariable analysis identified rim-APHE (OR = 22.667), mosaic architecture (OR = 5.904), and intratumoral hemorrhage (OR = 4.897) as independent risk factors for PHCC (all p < 0.05). PHCC showed significantly worse RFS than non-PHCC (p < 0.0001). Radscore effectively stratified early recurrence risk (p < 0.0001).
Conclusion: Radiomic analysis of intratumoral and peri-5 mm enhancement features enables accurate preoperative PHCC identification and may inform intensified postoperative surveillance and adjuvant therapy.
Keywords: hepatocellular carcinoma, peritumoral radiomics, computed tomography, early recurrence
1Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, People’s Republic of China; 2Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor, Guangxi Medical University, Ministry of Education, Nanning, People’s Republic of China; 3Guangxi Key Laboratory of Immunology and Metabolism for Liver Diseases, Nanning, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Liling Long, Department of Radiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530000, People’s Republic of China, Email cjr.longliling@vip.163.com
Purpose: To evaluate whether radiomic features from contrast-enhanced computed tomography (CECT) of peritumoral regions can be used to preoperatively predict proliferative hepatocellular carcinoma (PHCC).
Patients and Methods: Preoperative CT scans from 486 patients with hepatocellular carcinoma (HCC) were retrospectively analyzed and split into training (n = 252), testing (n = 109), and validation (n = 125) cohorts. Radiomic features were extracted from intra- and peritumoral regions (peri-3 mm, peri-5 mm, and peri-10 mm) on arterial phase (AP) and portal venous phase (PVP) images using PyRadiomics. Features were selected with LASSO regression and 10-fold cross-validation, and a radiomics score (Radscore) was calculated as a weighted sum of selected features. Patients were classified into high- and low-risk groups using the optimal Youden’s index cutoff. Recurrence-free survival (RFS) was analyzed with Kaplan–Meier curves, feature contributions were quantified using SHapley Additive exPlanations (SHAP), and model performance was assessed by area under the curve (AUC).
Results: The Naive Bayes model using peri-5 mm features achieved the highest mean AUC (0.739) and accuracy (0.802), with AUCs of 0.839 and 0.639 in internal and external validation. In the test set, combining intra- and peritumoral features improved the AUC to 0.849 (95% CI: 0.773– 0.924; sensitivity: 0.974; specificity: 0.606). In the validation set, AP, PVP, and their combined models achieved AUCs of 0.699, 0.672, and 0.695, respectively. SHAP highlighted in the Naive Bayes model that the increased inhomogeneity of the texture grayscale of the peritumoral tissue in the PVP may be associated with more aggressive HCC subtypes. Multivariable analysis identified rim-APHE (OR = 22.667), mosaic architecture (OR = 5.904), and intratumoral hemorrhage (OR = 4.897) as independent risk factors for PHCC (all p < 0.05). PHCC showed significantly worse RFS than non-PHCC (p < 0.0001). Radscore effectively stratified early recurrence risk (p < 0.0001).
Conclusion: Radiomic analysis of intratumoral and peri-5 mm enhancement features enables accurate preoperative PHCC identification and may inform intensified postoperative surveillance and adjuvant therapy.
Keywords: hepatocellular carcinoma, peritumoral radiomics, computed tomography, early recurrence