Clinical trial · Observational
Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma
Development of a Machine Learning-based Model for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
Microvascular invasion (MVI) has been well demonstrated as an unfavorable prognostic factor for hepatocellular carcinoma (HCC), and patients with MVI have a high risk of tumor recurrence after curative hepatectomy. Currently, the diagnosis of MVI is determined on the postoperative histologic examination, which greatly limits its influence on preoperative decision making. Therefore, we constructed this prospective study to develop a machine learning-based model for preoperative prediction of MVI by extracting high-dimensional magnetic resonance (MR) image features.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Hepatocellular Carcinoma | Hepatocellular Carcinoma | ONTOLOGY_EXACT | 0.98 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Magnetic resonance image | Diagnostic Test | — | UNRESOLVED |
Design
Arms and outcomes
Arms (1)
- label
- Preoperative imaging features
- description
- In this project, there is only one study group which comprises of patients with Hepatocellular Carcinoma (HCC) who will undergo preoperative Gd-EOB-DTPA enhanced magnetic resonance image.
- interventionNames
- Diagnostic Test: Magnetic resonance image
Primary outcomes (1)
- measure
- Presence of microvascular invasion
- timeFrame
- Through patient enrollment completion ,an average of 2 years
- description
- Postoperative histologically confirmed microvascular invasion
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
- Maximum age
- 80 Years
Show eligibility criteria text
Inclusion Criteria: * Asian patients aged 18～80 years old; * HCC without macroscopic vascular invasion according to imaging findings; * Child Pugh A-B stage; * Receipt of preoperative Gd-EOB-DTPA enhanced MR imaging of the abdomen within one month before surgery; * Histologically-diagnosed primary HCC after curative hepatectomy; Exclusion Criteria: * Combined hepatocellular-cholangiocarcinoma; * With extra-hepatic metastasis or macrovascular invasion; * With incomplete clinical and imaging data; * Non-radical resection;
References
Publications (5)
- RESULTAerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, Bussink J, Monshouwer R, Haibe-Kains B, Rietveld D, Hoebers F, Rietbergen MM, Leemans CR, Dekker A, Quackenbush J, Gillies RJ, Lambin P. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014 Jun 3;5:4006. doi: 10.1038/ncomms5006. PMID 24892406
- RESULTZhang YD, Wang Q, Wu CJ, Wang XN, Zhang J, Liu H, Liu XS, Shi HB. The histogram analysis of diffusion-weighted intravoxel incoherent motion (IVIM) imaging for differentiating the gleason grade of prostate cancer. Eur Radiol. 2015 Apr;25(4):994-1004. doi: 10.1007/s00330-014-3511-4. Epub 2014 Nov 28. PMID 25430007
- RESULTWoo S, Lee JM, Yoon JH, Joo I, Han JK, Choi BI. Intravoxel incoherent motion diffusion-weighted MR imaging of hepatocellular carcinoma: correlation with enhancement degree and histologic grade. Radiology. 2014 Mar;270(3):758-67. doi: 10.1148/radiol.13130444. Epub 2013 Oct 30. PMID 24475811
- RESULTHuang YQ, Liang CH, He L, Tian J, Liang CS, Chen X, Ma ZL, Liu ZY. Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer. J Clin Oncol. 2016 Jun 20;34(18):2157-64. doi: 10.1200/JCO.2015.65.9128. Epub 2016 May 2. PMID 27138577
- RESULTGillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures, They Are Data. Radiology. 2016 Feb;278(2):563-77. doi: 10.1148/radiol.2015151169. Epub 2015 Nov 18. PMID 26579733