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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| 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. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Magnetic resonance image | Diagnostic 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. |
| Measure | Description | Time Frame |
|---|---|---|
| Presence of microvascular invasion | Postoperative histologically confirmed microvascular invasion | Through patient enrollment completion ,an average of 2 years |
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Inclusion Criteria:
Exclusion Criteria:
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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.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Zebin Chen, MD | Contact | +86 13316284086 | chenzebin_2008@126.com | |
| Jie Mei, MD | Contact | +86 15817089979 | mmjj0926@163.com |
| Name | Affiliation | Role |
|---|---|---|
| Ming Kuang, PhD | Department of Liver Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China | Study Director |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| The First Affiliated Hospital of Sun Yat-sen University | Recruiting | Guangzhou | Guangdong | 510080 | China |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 24892406 | Result | 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. | |
| 25430007 |
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| ID | Term |
|---|---|
| D006528 | Carcinoma, Hepatocellular |
| ID | Term |
|---|---|
| D000230 | Adenocarcinoma |
| D002277 | Carcinoma |
| D009375 | Neoplasms, Glandular and Epithelial |
| D009370 | Neoplasms by Histologic Type |
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| ID | Term |
|---|---|
| D008279 | Magnetic Resonance Imaging |
| ID | Term |
|---|---|
| D014054 | Tomography |
| D003952 | Diagnostic Imaging |
| D019937 | Diagnostic Techniques and Procedures |
| D003933 | Diagnosis |
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serum,tumor tissue
|
| 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: 10.1007/s00330-014-3511-4. Epub 2014 Nov 28. |
| 24475811 | Result | 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.13130444. Epub 2013 Oct 30. |
| 27138577 | Result | 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.2015.65.9128. Epub 2016 May 2. |
| 26579733 | Result | 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. |
| D009369 | Neoplasms |
| D008113 | Liver Neoplasms |
| D004067 | Digestive System Neoplasms |
| D009371 | Neoplasms by Site |
| D004066 | Digestive System Diseases |
| D008107 | Liver Diseases |