115195
论文已发表
注册即可获取德孚的最新动态
IF 收录期刊
- 3.6 Breast Cancer (Dove Med Press)
- 4.3 Clin Epidemiol
- 2.6 Cancer Manag Res
- 3.2 Infect Drug Resist
- 4.1 Clin Interv Aging
- 6.1 Drug Des Dev Ther
- 4.1 Int J Chronic Obstr
- 8.7 Int J Nanomed
- 2.5 Int J Women's Health
- 3.2 Neuropsych Dis Treat
- 2.4 OncoTargets Ther
- 2.6 Patient Prefer Adher
- 2.6 Ther Clin Risk Manag
- 3.1 J Pain Res
- 3.5 Diabet Metab Synd Ob
- 4.5 Psychol Res Behav Ma
- 3.4 Nat Sci Sleep
- 2.4 Pharmgenomics Pers Med
- 2.6 Risk Manag Healthc Policy
- 4.6 J Inflamm Res
- 2.3 Int J Gen Med
- 3.9 J Hepatocell Carcinoma
- 3.3 J Asthma Allergy
- 2.5 Clin Cosmet Investig Dermatol
- 3.0 J Multidiscip Healthc
已发表论文
MRI 影像组学用于区分 HBV 相关早期肝细胞癌与癌前结节
Authors Liu J, Ding X, Zhang Y , Wang W, Zhou Z, Li H
Received 13 September 2025
Accepted for publication 31 December 2025
Published 13 January 2026 Volume 2026:13 567418
DOI https://doi.org/10.2147/JHC.S567418
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Prof. Dr. Imam Waked
Jiachen Liu,1 Xiurong Ding,2 Yanyan Zhang,1 Wei Wang,1 Zhongkai Zhou,1 Hongjun Li1
1Radiology Department, Beijing Youan Hospital, Capital Medical University, Beijing, People’s Republic of China; 2Department of Clinical Laboratory, Beijing Youan Hospital, Capital Medical University, Beijing, People’s Republic of China
Correspondence: Hongjun Li, Email lihongjun00113@126.com
Objective: To investigate the predictive value of intratumoral and peritumoral radiomic features from multiparametric MRI in differentiating HBV-associated early hepatocellular carcinoma (eHCC) from precancerous nodules.
Methods: Between January 2019 and October 2024, a total of 133 patients with eHCC and 89 patients with pre-HCC lesions who underwent preoperative MRI were enrolled in the study. Multivariate analysis identified independent predictors for eHCC used to build a clinical prediction model. Radiomics models based on multiphase MRI across multiple tumor regions (entire tumor, Peri_4mm, Peri_6mm, Peri_8mm) were developed. Features from the intratumoral and optimal peritumoral region were combined into an IntraPeri model. A nomogram was developed by combining clinical and IntraPeri model variables. Model performance was evaluated using the area under the curve (AUC).
Results: 222 patients (mean age, 57.2 years ± 11.0; 164 men) were evaluated. DWI hyperintensity, T1WI hypointensity, and a low globulin-to-lymphocyte ratio (GLR) were identified as risk factors for eHCC. 1,409 radiomic features were extracted from each ROI; 27 were retained for analysis and model development. The clinical model achieved AUCs of 0.826 and 0.798 in the training and internal validation cohorts, respectively, while the intratumoral model yielded AUC values of 0.884 and 0.838. The Peri_6mm model outperformed the Peri_8mm and Peri_4mm models. The IntraPeri model demonstrated excellent performance, achieving AUCs of 0.947 and 0.935 in the training and internal validation cohorts, respectively. The comprehensive nomogram demonstrated the highest performance with AUCs of 0.958 and 0.950.
Conclusion: Our study highlights the potential of a multiparametric MRI-based radiomics nomogram that integrates both intratumoral and peritumoral features as an effective tool for differentiating eHCC.
Keywords: early hepatocellular carcinoma, precancerous nodules, magnetic resonance imaging, intratumoral and peritumoral, radiomics
1Radiology Department, Beijing Youan Hospital, Capital Medical University, Beijing, People’s Republic of China; 2Department of Clinical Laboratory, Beijing Youan Hospital, Capital Medical University, Beijing, People’s Republic of China
Correspondence: Hongjun Li, Email lihongjun00113@126.com
Objective: To investigate the predictive value of intratumoral and peritumoral radiomic features from multiparametric MRI in differentiating HBV-associated early hepatocellular carcinoma (eHCC) from precancerous nodules.
Methods: Between January 2019 and October 2024, a total of 133 patients with eHCC and 89 patients with pre-HCC lesions who underwent preoperative MRI were enrolled in the study. Multivariate analysis identified independent predictors for eHCC used to build a clinical prediction model. Radiomics models based on multiphase MRI across multiple tumor regions (entire tumor, Peri_4mm, Peri_6mm, Peri_8mm) were developed. Features from the intratumoral and optimal peritumoral region were combined into an IntraPeri model. A nomogram was developed by combining clinical and IntraPeri model variables. Model performance was evaluated using the area under the curve (AUC).
Results: 222 patients (mean age, 57.2 years ± 11.0; 164 men) were evaluated. DWI hyperintensity, T1WI hypointensity, and a low globulin-to-lymphocyte ratio (GLR) were identified as risk factors for eHCC. 1,409 radiomic features were extracted from each ROI; 27 were retained for analysis and model development. The clinical model achieved AUCs of 0.826 and 0.798 in the training and internal validation cohorts, respectively, while the intratumoral model yielded AUC values of 0.884 and 0.838. The Peri_6mm model outperformed the Peri_8mm and Peri_4mm models. The IntraPeri model demonstrated excellent performance, achieving AUCs of 0.947 and 0.935 in the training and internal validation cohorts, respectively. The comprehensive nomogram demonstrated the highest performance with AUCs of 0.958 and 0.950.
Conclusion: Our study highlights the potential of a multiparametric MRI-based radiomics nomogram that integrates both intratumoral and peritumoral features as an effective tool for differentiating eHCC.
Keywords: early hepatocellular carcinoma, precancerous nodules, magnetic resonance imaging, intratumoral and peritumoral, radiomics