Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma
Development of a Machine Learning-based Model for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma
1 other identifier
observational
40
1 country
1
Brief Summary
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.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P25-P50 for all trials
Started Jun 2017
Shorter than P25 for all trials
1 active site
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
First Submitted
Initial submission to the registry
June 22, 2017
CompletedStudy Start
First participant enrolled
June 23, 2017
CompletedFirst Posted
Study publicly available on registry
June 26, 2017
CompletedPrimary Completion
Last participant's last visit for primary outcome
July 31, 2017
CompletedStudy Completion
Last participant's last visit for all outcomes
July 31, 2017
CompletedJune 26, 2017
June 1, 2017
1 month
June 22, 2017
June 22, 2017
Conditions
Keywords
Outcome Measures
Primary Outcomes (1)
Presence of microvascular invasion
Postoperative histologically confirmed microvascular invasion
Through patient enrollment completion ,an average of 2 years
Study Arms (1)
Preoperative imaging features
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.
Interventions
Histologically-diagnosed primary HCC after curative hepatectomy. The magnetic resonance image will be imported into the software ,and the radiomic textural features will be automatically extracted by the Analysis-Kit software.The high-throughput extracted features will be then selected and a prediction model will be developed in the training set in which patients were collected from a retrospective study. In this project, an independent validation set will be collected and used to validate the prediction accuracy of the model.
Eligibility Criteria
Between June 2017 and July 2017，all patients who will undergo curative resection (R0 resection) at the First Affiliated Hospital of Sun YatSen University in Guangzhou, China, for HCC based on the modified WHO classification of tumors of the digestive system, are considered for inclusion. By the eligibility criteria stated below, MVI presentative rate is 30-42% in chinese HCC population as reported, we retrospectively collected about 80 patients for training and an estimated 40 patients will be needed for validation set of this study.
You may qualify if:
- 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;
You may not qualify if:
- Combined hepatocellular-cholangiocarcinoma;
- With extra-hepatic metastasis or macrovascular invasion;
- With incomplete clinical and imaging data;
- Non-radical resection;
Contact the study team to confirm eligibility.
Sponsors & Collaborators
- Ming Kuanglead
Study Sites (1)
The First Affiliated Hospital of Sun Yat-sen University
Guangzhou, Guangdong, 510080, China
Related Publications (5)
Aerts 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: 24892406RESULTZhang 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: 25430007RESULTWoo 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: 24475811RESULTHuang 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: 27138577RESULTGillies 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: 26579733RESULT
Biospecimen
serum,tumor tissue
MeSH Terms
Conditions
Interventions
Condition Hierarchy (Ancestors)
Intervention Hierarchy (Ancestors)
Study Officials
- STUDY DIRECTOR
Ming Kuang, PhD
Department of Liver Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
Central Study Contacts
Study Design
- Study Type
- observational
- Observational Model
- COHORT
- Time Perspective
- PROSPECTIVE
- Sponsor Type
- OTHER
- Responsible Party
- SPONSOR INVESTIGATOR
- PI Title
- Professor
Study Record Dates
First Submitted
June 22, 2017
First Posted
June 26, 2017
Study Start
June 23, 2017
Primary Completion
July 31, 2017
Study Completion
July 31, 2017
Last Updated
June 26, 2017
Record last verified: 2017-06
Data Sharing
- IPD Sharing
- Will not share