Share：
Share this content in WeChat
X
[Chinese][PDF]822
• Clinical Article •
CEMRI-based intratumoral and peritumoral radiomics for predicting the degree of pathological differentiation of hepatocellular carcinoma
LU Yujie  GU Wenhao  XU Dabo  LIU Haifeng  XING Wei 

Cite this article as: LU Y J, GU W H, XU D B, et al. CEMRI-based intratumoral and peritumoral radiomics for predicting the degree of pathological differentiation of hepatocellular carcinoma[J]. Chin J Magn Reson Imaging, 2025, 16(3): 51-57. DOI:10.12015/issn.1674-8034.2025.03.008.


[Abstract] Objective To develop and validate intratumoral and multiregion peritumoral radiomics models based on contrast-enhanced magnetic resonance imaging (CEMRI) for predicting pathological differentiation in hepatocellular carcinoma (HCC) patients.Materials and Methods A total of 213 HCC patients diagnosed between January 2020 and July 2023 at the Third Affiliated Hospital of Soochow University was included in the retrospective study, comprising 62 poorly differentiated HCC (pHCC) and 161 non-poorly differentiated HCCs (npHCC). The HCCs were randomly divided into training (149 patients, 156 HCCs) and validation (64 patients, 67 HCCs) cohorts at a 7∶3 ratio. The ITK-SNAP software delineated the region of interest (ROI) on arterial, portal vein, and delayed phase images, while PyRadiomics software extracted 3045 radiomic features. Feature selection was carried out using Spearman rank correlation, least absolute shrinkage and selection operator (LASSO), and maximum relevance-minimum redundancy (mRMR) approaches, followed by support vector machine algorithm to build Intratumoral, 5 mm peritumoral (Peri_5mm), 10 mm peritumoral (Peri_10mm), and Intratumoral + 10 mm peritumoral (IntraPeri) models. The predictive performance of these models was assessed using the area under the curve (AUC) of receiver operating characteristic and decision curve analysis (DCA).Results The Intratumoral, Peri_5mm, Peri_10mm, and IntraPeri models consisted of 10, 17, 11, and 12 features, respectively. In the Intratumoral model, the AUC values for predicting pHCC in the training and validation cohorts were 0.92 and 0.93, respectively. The Peri_10mm model exhibited higher AUCs compared to the Peri_5mm model: 0.88 versus 0.82 in the training cohort and 0.90 versus 0.85 in the validation cohort. The IntraPeri model demonstrated superior performance with AUC values of 0.95 and 0.95 in the training and validation cohorts, respectively. DCA suggested that the Intratumoral, Peri_5mm, and Peri_10mm models provided notable clinical benefits, with the IntraPeri model being the most optimal.Conclusions The IntraPeri model based on CEMRI can accurately predict HCC differentiation and has good clinical benefits.
[Keywords] hepatocellular carcinoma；pathological differentiation；magnetic resonance imaging；intratumoral；peritumoral；radiomics

LU Yujie1, 2   GU Wenhao2   XU Dabo2   LIU Haifeng3   XING Wei1, 3*  

1 Medical College of Yangzhou University, Yangzhou 225009, China

2 Department of Radiology, The First People's Hospital of Taicang, Suzhou 215400, China

3 Department of Radiology, the Third Affiliated Hospital of Soochow University, Changzhou 213000, China

Corresponding author: XING W, Email: suzhxingwei@suda.edu.cn

Conflicts of interest   None.

Received  2024-08-31
Accepted  2025-02-27
DOI: 10.12015/issn.1674-8034.2025.03.008
Cite this article as: LU Y J, GU W H, XU D B, et al. CEMRI-based intratumoral and peritumoral radiomics for predicting the degree of pathological differentiation of hepatocellular carcinoma[J]. Chin J Magn Reson Imaging, 2025, 16(3): 51-57. DOI:10.12015/issn.1674-8034.2025.03.008.

[1]
HAN B F, ZHENG R S, ZENG H M, et al. Cancer incidence and mortality in China, 2022[J]. J Natl Cancer Cent, 2024, 4(1): 47-53. DOI: 10.1016/j.jncc.2024.01.006.
[2]
LIU H F, WANG M, WANG Q, et al. Multiparametric MRI-based intratumoral and peritumoral radiomics for predicting the pathological differentiation of hepatocellular carcinoma[J/OL]. Insights Imaging, 2024, 15(1): 97 [2025-02-15]. https://pubmed.ncbi.nlm.nih.gov/38536542/. DOI: 10.1186/s13244-024-01623-w.
[3]
MO Z Y, CHEN P Y, LIN J, et al. Pre-operative MRI features predict early post-operative recurrence of hepatocellular carcinoma with different degrees of pathological differentiation[J]. Radiol Med, 2023, 128(3): 261-273. DOI: 10.1007/s11547-023-01601-0.
[4]
SHINKAWA H, TANAKA S, KABATA D, et al. The prognostic impact of tumor differentiation on recurrence and survival after resection of hepatocellular carcinoma is dependent on tumor size[J]. Liver Cancer, 2021, 10(5): 461-472. DOI: 10.1159/000517992.
[5]
SINGAL A G, LLOVET J M, YARCHOAN M, et al. AASLD Practice Guidance on prevention, diagnosis, and treatment of hepatocellular carcinoma[J]. Hepatology, 2023, 78(6): 1922-1965. DOI: 10.1097/HEP.0000000000000466.
[6]
European Association for the Study of the Liver. EASL Clinical Practice Guidelines on the management of hepatocellular carcinoma[J]. J Hepatol, 2025, 82(2): 315-374. DOI: 10.1016/j.jhep.2024.08.028.
[7]
CALDERARO J, ZIOL M, PARADIS V, et al. Molecular and histological correlations in liver cancer[J]. J Hepatol, 2019, 71(3): 616-630. DOI: 10.1016/j.jhep.2019.06.001.
[8]
PARK I, LEE H B, KIM N, et al. Uncovering gene expression signatures and diagnostic–Biomarkers in hepatocellular carcinoma through multinomial logistic regression analysis[J]. J Biotechnol, 2024, 395: 31-43. DOI: 10.1016/j.jbiotec.2024.09.003.
[9]
DU J S, HSU S H, WANG S N. The current and prospective adjuvant therapies for hepatocellular carcinoma[J/OL]. Cancers, 2024, 16(7): 1422 [2025-02-15]. https://pubmed.ncbi.nlm.nih.gov/38611100/. DOI: 10.3390/cancers16071422.
[10]
KLEINER D E. Hepatocellular carcinoma: liver biopsy in the balance[J]. Hepatology, 2018, 68(1): 13-15. DOI: 10.1002/hep.29831.
[11]
LEHRICH B M, ZHANG J, MONGA S P, et al. Battle of the biopsies: Role of tissue and liquid biopsy in hepatocellular carcinoma[J]. J Hepatol, 2024, 80(3): 515-530. DOI: 10.1016/j.jhep.2023.11.030.
[12]
XIA T Y, ZHOU Z H, MENG X P, et al. Predicting microvascular invasion in hepatocellular carcinoma using CT-based radiomics model[J/OL]. Radiology, 2023, 307(4): e222729 [2025-02-15]. https://pubmed.ncbi.nlm.nih.gov/37097141/. DOI: 10.1148/radiol.222729.
[13]
CANDITA G, ROSSI S, CWIKLINSKA K, et al. Imaging diagnosis of hepatocellular carcinoma: a state-of-the-art review[J/OL]. Diagnostics, 2023, 13(4): 625 [2025-02-15]. https://pubmed.ncbi.nlm.nih.gov/36832113/. DOI: 10.3390/diagnostics13040625.
[14]
LIU H F, LU Y, WANG Q, et al. Machine learning-based CEMRI radiomics integrating LI-RADS features achieves optimal evaluation of hepatocellular carcinoma differentiation[J]. J Hepatocell Carcinoma, 2023, 10: 2103-2115. DOI: 10.2147/JHC.S434895.
[15]
AMELI S, VENKATESH B A, SHAGHAGHI M, et al. Role of MRI-derived radiomics features in determining degree of tumor differentiation of hepatocellular carcinoma[J/OL]. Diagnostics, 2022, 12(10): 2386 [2025-02-15]. https://pubmed.ncbi.nlm.nih.gov/36292074/. DOI: 10.3390/diagnostics12102386.
[16]
MAO Y F, WANG J C, ZHU Y, et al. Gd-EOB-DTPA-enhanced MRI radiomic features for predicting histological grade of hepatocellular carcinoma[J]. Hepatobiliary Surg Nutr, 2022, 11(1): 13-24. DOI: 10.21037/hbsn-19-870.
[17]
YANG Y, ZHANG S, CUI C, et al. Multiphase MRI-based radiomics for predicting histological grade of hepatocellular carcinoma[J]. J Magn Reson Imaging, 2024, 60(5): 2117-2127. DOI: 10.1002/jmri.29289.
[18]
LI N, WAN X T, ZHANG H, et al. Tumor and peritumor radiomics analysis based on contrast-enhanced CT for predicting early and late recurrence of hepatocellular carcinoma after liver resection[J/OL]. BMC Cancer, 2022, 22(1): 664 [2025-02-15]. https://pubmed.ncbi.nlm.nih.gov/35715783/. DOI: 10.1186/s12885-022-09743-6.
[19]
HU F, ZHANG Y H, LI M, et al. Preoperative prediction of microvascular invasion risk grades in hepatocellular carcinoma based on tumor and peritumor dual-region radiomics signatures[J/OL]. Front Oncol, 2022, 12: 853336 [2025-02-15]. https://pubmed.ncbi.nlm.nih.gov/35392229/. DOI: 10.3389/fonc.2022.853336.
[20]
GAO L M, XIONG M L, CHEN X J, et al. Multi-region radiomic analysis based on multi-sequence MRI can preoperatively predict microvascular invasion in hepatocellular carcinoma[J/OL]. Front Oncol, 2022, 12: 818681 [2025-02-15]. https://pubmed.ncbi.nlm.nih.gov/35574328/. DOI: 10.3389/fonc.2022.818681.
[21]
HE H L, WANG Q, LIU L, et al. Peritumoral edema in preoperative magnetic resonance imaging is an independent prognostic factor for hepatocellular carcinoma[J]. Clin Imaging, 2021, 75: 143-149. DOI: 10.1016/j.clinimag.2021.01.022.
[22]
SUN K, SHI L T, QIU J F, et al. Multi-phase contrast-enhanced magnetic resonance image-based radiomics-combined machine learning reveals microscopic ultra-early hepatocellular carcinoma lesions[J]. Eur J Nucl Med Mol Imaging, 2022, 49(8): 2917-2928. DOI: 10.1007/s00259-022-05742-8.
[23]
YANG Y, FAN W J, GU T, et al. Radiomic features of multi-ROI and multi-phase MRI for the prediction of microvascular invasion in solitary hepatocellular carcinoma[J/OL]. Front Oncol, 2021, 11: 756216 [2025-02-15]. https://pubmed.ncbi.nlm.nih.gov/34692547/. DOI: 10.3389/fonc.2021.756216.
[24]
CHONG H H, YANG L, SHENG R F, et al. Multi-scale and multi-parametric radiomics of gadoxetate disodium-enhanced MRI predicts microvascular invasion and outcome in patients with solitary hepatocellular carcinoma≤5 cm[J]. Eur Radiol, 2021, 31(7): 4824-4838. DOI: 10.1007/s00330-020-07601-2.
[25]
ZHAO Y, WU J J, ZHANG Q H, et al. Radiomics analysis based on multiparametric MRI for predicting early recurrence in hepatocellular carcinoma after partial hepatectomy[J]. J Magn Reson Imaging, 2021, 53(4): 1066-1079. DOI: 10.1002/jmri.27424.
[26]
XU X, ZHANG H L, LIU Q P, et al. Radiomic analysis of contrast-enhanced CT predicts microvascular invasion and outcome in hepatocellular carcinoma[J]. J Hepatol, 2019, 70(6): 1133-1144. DOI: 10.1016/j.jhep.2019.02.023.
[27]
MOKRANE F Z, LU L, VAVASSEUR A, et al. Radiomics machine-learning signature for diagnosis of hepatocellular carcinoma in cirrhotic patients with indeterminate liver nodules[J]. Eur Radiol, 2020, 30(1): 558-570. DOI: 10.1007/s00330-019-06347-w.
[28]
LIU N, WU Y K, TAO Y Y, et al. Differentiation of hepatocellular carcinoma from intrahepatic cholangiocarcinoma through MRI radiomics[J/OL]. Cancers, 2023, 15(22): 5373 [2025-02-15]. https://pubmed.ncbi.nlm.nih.gov/38001633/. DOI: 10.3390/cancers15225373.
[29]
HECTORS S J, LEWIS S, BESA C, et al. MRI radiomics features predict immuno-oncological characteristics of hepatocellular carcinoma[J]. Eur Radiol, 2020, 30(7): 3759-3769. DOI: 10.1007/s00330-020-06675-2.
[30]
YANG X Z, YUAN C W, ZHANG Y H, et al. Magnetic resonance radiomics signatures for predicting poorly differentiated hepatocellular carcinoma: A SQUIRE-compliant study[J/OL]. Medicine, 2021, 100(19): e25838 [2025-02-15]. https://pubmed.ncbi.nlm.nih.gov/34106622/. DOI: 10.1097/MD.0000000000025838.
[31]
BRANCATO V, GARBINO N, SALVATORE M, et al. MRI-based radiomic features help identify lesions and predict histopathological grade of hepatocellular carcinoma[J/OL]. Diagnostics, 2022, 12(5): 1085 [2025-02-15]. https://pubmed.ncbi.nlm.nih.gov/35626241/. DOI: 10.3390/diagnostics12051085.
[32]
KOCAK B, BAESSLER B, BAKAS S, et al. CheckList for EvaluAtion of radiomics research (CLEAR): a step-by-step reporting guideline for authors and reviewers endorsed by ESR and EuSoMII[J/OL]. Insights Imaging, 2023, 14(1): 75 [2025-02-15]. https://pubmed.ncbi.nlm.nih.gov/37142815/. DOI: 10.1186/s13244-023-01415-8.
[33]
MUKHERJEE S, PATRA A, KHASAWNEH H, et al. Radiomics-based machine-learning models can detect pancreatic cancer on prediagnostic computed tomography scans at a substantial lead time before clinical diagnosis[J]. Gastroenterology, 2022, 163(5): 1435-1446. DOI: 10.1053/j.gastro.2022.06.066.
[34]
ZHENG Y L, ZHOU D, LIU H, et al. CT-based radiomics analysis of different machine learning models for differentiating benign and malignant parotid tumors[J]. Eur Radiol, 2022, 32(10): 6953-6964. DOI: 10.1007/s00330-022-08830-3.
[35]
YANG J W, DONG X, JIN S Z, et al. Radiomics model of dynamic contrast-enhanced MRI for evaluating vessels encapsulating tumor clusters and microvascular invasion in hepatocellular carcinoma[J]. Acad Radiol, 2025, 32(1): 146-156. DOI: 10.1016/j.acra.2024.07.007.
[36]
ZHANG T C, LEI X J, JIA W L, et al. Peritumor tertiary lymphoid structures are associated with infiltrating neutrophils and inferior prognosis in hepatocellular carcinoma[J]. Cancer Med, 2023, 12(3): 3068-3078. DOI: 10.1002/cam4.5227.
[37]
JI G W, ZHU F P, XU Q, et al. Radiomic features at contrast-enhanced CT predict recurrence in early stage hepatocellular carcinoma: a multi-institutional study[J]. Radiology, 2020, 294(3): 568-579. DOI: 10.1148/radiol.2020191470.
[38]
WANG C, WU F, WANG F, et al. The association between tumor radiomic analysis and peritumor habitat-derived radiomic analysis on gadoxetate disodium-enhanced MRI with microvascular invasion in hepatocellular carcinoma[J]. J Magn Reson Imag, 2025, 61(3): 1428-1439. DOI: 10.1002/jmri.29523.
[39]
RENNE S L, SARCOGNATO S, SACCHI D, et al. Hepatocellular carcinoma: a clinical and pathological overview[J]. Pathologica, 2021, 113(3): 203-217. DOI: 10.32074/1591-951X-295.
[40]
WANG F, CHENG M, DU B B, et al. Predicting microvascular invasion in small (≤ 5cm) hepatocellular carcinomas using radiomics-based peritumoral analysis[J/OL]. Insights Imag, 2024, 15(1): 90 [2025-02-15]. https://pubmed.ncbi.nlm.nih.gov/38530498/. DOI: 10.1186/s13244-024-01649-0.
[41]
ZHAO Y, ZHANG J, WANG N, et al. Intratumoral and peritumoral radiomics based on contrast-enhanced MRI for preoperatively predicting treatment response of transarterial chemoembolization in hepatocellular carcinoma[J/OL]. BMC Cancer, 2023, 23(1): 1026 [2025-02-15]. https://pubmed.ncbi.nlm.nih.gov/37875815/. DOI: 10.1186/s12885-023-11491-0.

PREV Diagnostic value of radiomics for axillary lymph node metastasis in breast cancer: A Meta-analysis
NEXT Predicting vessels encapsulating tumor clusters in hepatocellular carcinoma using combination clinical biomarkers and MR features nomogram
  


LINK


Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn