Clinical trial NCT03198975: Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma
Official title: Development of a Machine Learning-based Model for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Hepatocellular Carcinoma
Keywords: magnetic resonance image features
pathological differentiation
machine learning-based model
Lead sponsor: Sun Yat-sen University
Sponsor class: OTHER
First posted: 2017-06-26
Enrollment: 40
Sex: ALL
Minimum age (years): 18
Maximum age (years): 80
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
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.

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.

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.

Eligibility 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;

Primary outcomes:
measure: Presence of microvascular invasion
timeFrame: Through patient enrollment completion ,an average of 2 years
Secondary outcomes:
Other outcomes:
Locations:
city: Guangzhou
country: China
facility: The First Affiliated Hospital of Sun Yat-sen University
state: Guangdong
Study officials:
affiliation: Department of Liver Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
name: Ming Kuang, PhD
role: STUDY_DIRECTOR
References:
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