Hepatocellular CarcinomaNCT03198975
Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma
An observational study of Hepatocellular Carcinoma, run by Ming Kuang at 1 site in China.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 to 80 years
- Conditions the study is for
- Hepatocellular Carcinoma
- What is being tested?
- Magnetic resonance image
- How many people
- 40 participants
- Where
- 1 siteChina
- Study dates
- 23 Jun 2017to 31 Jul 2017
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 80 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: 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.
Who may be able to join
- 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;
Who cannot join
- Combined hepatocellular-cholangiocarcinoma;
- With extra-hepatic metastasis or macrovascular invasion;
- With incomplete clinical and imaging data;
- Non-radical resection;
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
The First Affiliated Hospital of Sun Yat-sen University
Guangzhou, Guangdong, 510080
Chen Zebin, +8615017581009, chenzebin_2008@126.com
About this study
Official title: Development of a Machine Learning-based Model for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma
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.
Read the detailed description
Histologically-diagnosed primary HCC after curative hepatectomy. The magnetic resonance image will be imported into the imaging management software (GE healthcare Analysis-Kit software),and the tumor lesions will manually delineated by two independent radiologists and then reconstruct into three-dimensional images for feature extraction. The radiomic textural features including grayscale histogram, transform matrix, wavelet transform and filter transformation are automatically extracted by the Analysis-Kit software.The high-throughput extracted features will be then selected by the univariate analysis, and a prediction model will be developed based on machine learning algorithm in a training set in which patients were collected from a retrospective study. And in the present study, an independent validation set will be collected and used to validate the prediction accuracy of the model.
- Conditions
- Hepatocellular Carcinoma
- Being tested
- Magnetic resonance image
- Keywords
- magnetic resonance image features, pathological differentiation, machine learning-based model
- Medical subject headings
- Carcinoma, Hepatocellular
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Magnetic resonance imageDiagnostic test
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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Diagnostic Test: Magnetic resonance image |
What is being measured
The primary outcome is the main question the study is designed to answer.
Presence of microvascular invasion
Postoperative histologically confirmed microvascular invasion
Measured: Through patient enrollment completion ,an average of 2 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted26 Jun 2017
- Study start23 Jun 2017
- Primary completion31 Jul 2017 (estimated)
- Study completion31 Jul 2017 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (5)Papers the sponsor has linked to this study.
- Other trials for Hepatocellular CarcinomaStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 40 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Ming Kuang
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Ming Kuang, Sun Yat-sen University
- Investigators
- Ming Kuang, PhD, Study director, Department of Liver Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT03198975
- Sponsor's protocol ID
- HCC10
- Status last verified
- Jun 2017
- First submitted
- 22 Jun 2017
- Last updated
- 26 Jun 2017
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
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.
PubMed 24892406Zhang 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.
PubMed 25430007Woo 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.
PubMed 24475811Huang 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.
PubMed 27138577Gillies 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.
PubMed 26579733
Study at a glance
From the registry record, last updated by the sponsor on 26 Jun 2017. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development of a Machine Learning-based Model for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma
- ClinicalTrials.gov ID
- NCT03198975
- Acronym / sponsor ID
- HCC10
- Conditions
- Hepatocellular Carcinoma
- Keywords
- magnetic resonance image features, pathological differentiation, machine learning-based model
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 40 participants (estimated)
- Lead sponsor
- Ming KuangUniversity, hospital or other
- Study start
- 23 Jun 2017 (actual)
- Primary completion
- 31 Jul 2017 (estimated)
- Study completion
- 31 Jul 2017 (estimated)
- First posted
- 26 Jun 2017 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
Histologically-diagnosed primary HCC after curative hepatectomy. The magnetic resonance image will be imported into the imaging management software (GE healthcare Analysis-Kit software),and the tumor lesions will manually delineated by two independent radiologists and then reconstruct into three-dimensional images for feature extraction. The radiomic textural features including grayscale histogram, transform matrix, wavelet transform and filter transformation are automatically extracted by the Analysis-Kit software.The high-throughput extracted features will be then selected by the univariate analysis, and a prediction model will be developed based on machine learning algorithm in a training set in which patients were collected from a retrospective study. And in the present study, an independent validation set will be collected and used to validate the prediction accuracy of the model.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Preoperative imaging features | – | Diagnostic Test: Magnetic resonance imageIn 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. |
Who may qualify
- Ages
- 18 to 80 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- 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.
Key inclusion criteria (5 listed)
- 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;
Key exclusion criteria (4 listed)
- Combined hepatocellular-cholangiocarcinoma;
- With extra-hepatic metastasis or macrovascular invasion;
- With incomplete clinical and imaging data;
- Non-radical resection;
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Magnetic resonance imageHistologically-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. | Diagnostic test | Preoperative imaging features |
Primary outcome
Presence of microvascular invasion
Time frame: Through patient enrollment completion ,an average of 2 years
Postoperative histologically confirmed microvascular invasion
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 26 Jun 2017.
- Results publication: PubMed 24892406Aerts 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.
- Results publication: PubMed 25430007Zhang 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.
- Results publication: PubMed 24475811Woo 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.
- Results publication: PubMed 27138577Huang 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.
- Results publication: PubMed 26579733Gillies 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.
- Individual participant dataSharing planned: No
Related trials for Hepatocellular Carcinoma
RecruitingObservational study
Real-world Evaluation of the HistoSonics Edison System for Treatment of Liver Tumors Across Multidisciplinary Users (BOOMBOX: Master Study)
Liver Neoplasms, Primary Liver Cancer
Recruiting
The Vanguard Study: Testing a New Way to Screen for Cancer
Bladder Carcinoma, Breast Carcinoma
Active, not recruitingPhase 1
Prospective Exploratory Study of FAPi PET/CT With Histopathology Validation in Patients With Various Cancers
Bladder Carcinoma, Cervical Carcinoma
RecruitingObservational study
A Study to Evaluate Patient-Reported Satisfaction, Effectiveness, and Safety of Atezolizumab in Participants Treated in Routine Clinical Practice
Lung Cancer, Hepatocellular Carcinoma
RecruitingPhase 2
At-Home Cancer Directed Therapy Versus in Clinic for the Treatment of Patients With Advanced Cancer
Advanced Anal Carcinoma, Advanced Biliary Tract Carcinoma
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Ming Kuang, PhD study director
Department of Liver Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Hepatocellular Carcinoma, run by Ming Kuang at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 26 Jun 2017. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Zebin Chen, MD (+86 13316284086, chenzebin_2008@126.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Presence of microvascular invasion (time frame: Through patient enrollment completion ,an average of 2 years). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT03198975, which also lists 5 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 26 Jun 2017.