Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma

Hepatocellular Carcinoma Clinical Trial

Official title:

Development of a Machine Learning-based Model for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT03198975
• Other study ID #: HCC10
• Status: Recruiting
• Phase: N/A
• First received: June 22, 2017
• Last updated: June 22, 2017
• Start date: June 23, 2017
• Est. completion date: July 31, 2017

Study information

• Verified date: June 2017
• Source: Sun Yat-sen University
• Contact: Zebin Chen, MD
• Phone: +86 13316284086
• Email: chenzebin_2008[@]126.com
• Is FDA regulated: No
• Study type: Observational

Clinical 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. 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.

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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 40
• Est. completion date: July 31, 2017
• Est. primary completion date: July 31, 2017
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years to 80 Years

Eligibility

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;

Related Conditions & MeSH terms

• Carcinoma
• Carcinoma, Hepatocellular
• Hepatocellular Carcinoma

Intervention

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.

Locations

Country	Name	City	State
China	The First Affiliated Hospital of Sun Yat-sen University	Guangzhou	Guangdong

Sponsors (1)

Lead Sponsor	Collaborator
Ming Kuang

Country where clinical trial is conducted

China, 

References & 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 imag — View Citation

Gillies 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. — View Citation

Huang 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.201 — View Citation

Woo 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.131304 — View Citation

Zhang 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: — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Presence of microvascular invasion	Postoperative histologically confirmed microvascular invasion	Through patient enrollment completion ,an average of 2 years