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Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma

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ClinicalTrials.gov Identifier: NCT03198975
Recruitment Status : Recruiting
First Posted : June 26, 2017
Last Update Posted : June 26, 2017
Sponsor:
Information provided by (Responsible Party):
Ming Kuang, Sun Yat-sen University

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.

Condition or disease Intervention/treatment
Hepatocellular Carcinoma Diagnostic Test: Magnetic resonance image

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 Type : Observational
Estimated Enrollment : 40 participants
Observational Model: Cohort
Time Perspective: Prospective
Official Title: Development of a Machine Learning-based Model for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma
Actual Study Start Date : June 23, 2017
Estimated Primary Completion Date : July 31, 2017
Estimated Study Completion Date : July 31, 2017

Group/Cohort Intervention/treatment
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.
Diagnostic Test: Magnetic resonance image
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.




Primary Outcome Measures :
  1. Presence of microvascular invasion [ Time Frame: Through patient enrollment completion ,an average of 2 years ]
    Postoperative histologically confirmed microvascular invasion


Biospecimen Retention:   Samples With DNA
serum,tumor tissue


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Ages Eligible for Study:   18 Years to 80 Years   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Sampling Method:   Probability Sample
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.
Criteria

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;

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT03198975


Contacts
Contact: Zebin Chen, MD +86 13316284086 chenzebin_2008@126.com
Contact: Jie Mei, MD +86 15817089979 mmjj0926@163.com

Locations
China, Guangdong
The First Affiliated Hospital of Sun Yat-sen University Recruiting
Guangzhou, Guangdong, China, 510080
Contact: Chen Zebin    +8615017581009    chenzebin_2008@126.com   
Sponsors and Collaborators
Ming Kuang
Investigators
Study Director: Ming Kuang, PhD Department of Liver Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China

Publications of Results:

Responsible Party: Ming Kuang, Professor, Sun Yat-sen University
ClinicalTrials.gov Identifier: NCT03198975     History of Changes
Other Study ID Numbers: HCC10
First Posted: June 26, 2017    Key Record Dates
Last Update Posted: June 26, 2017
Last Verified: June 2017
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: No

Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No

Keywords provided by Ming Kuang, Sun Yat-sen University:
magnetic resonance image features
pathological differentiation
machine learning-based model

Additional relevant MeSH terms:
Carcinoma
Carcinoma, Hepatocellular
Neoplasms, Glandular and Epithelial
Neoplasms by Histologic Type
Neoplasms
Adenocarcinoma
Liver Neoplasms
Digestive System Neoplasms
Neoplasms by Site
Digestive System Diseases
Liver Diseases