Shi-Ting Feng1, Yingmei Jia1, Bing Liao2, Bingsheng Huang3, Qian Zhou4, Xin Li5, Kaikai Wei1, Lili Chen2, Bin Li4, Wei Wang6, Shuling Chen6, Xiaofang He7, Haibo Wang4, Sui Peng4,8, Ze-Bin Chen9, Mimi Tang8, Zhihang Chen9, Yang Hou10, Zhenwei Peng11, Ming Kuang12,13. 1. Department of Radiology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China. 2. Department of Pathology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China. 3. National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China. 4. Clinical Trials Unit, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China. 5. GE Healthcare, Shanghai, China. 6. Department of Medical Ultrasonics, Division of Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China. 7. Department of Radiation Oncology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China. 8. Department of Gastroenterology and Hepatology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China. 9. Department of Liver Surgery, The First Affiliated Hospital of Sun Yat-sen University, 58 Zhong Shan Road 2, Guangzhou, 510080, China. 10. Jinan University, Guangzhou, China. 11. Department of Oncology, The First Affiliated Hospital of Sun Yat-sen University, 58 Zhong Shan Road 2, Guangzhou, 510080, China. pzhenw@mail.sysu.edu.cn. 12. Department of Medical Ultrasonics, Division of Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China. kuangminda@hotmail.com. 13. Department of Liver Surgery, The First Affiliated Hospital of Sun Yat-sen University, 58 Zhong Shan Road 2, Guangzhou, 510080, China. kuangminda@hotmail.com.
Abstract
OBJECTIVES: Preoperative prediction of microvascular invasion (MVI) in patients with hepatocellular cancer (HCC) is important for surgery strategy making. We aimed to develop and validate a combined intratumoural and peritumoural radiomics model based on gadolinium-ethoxybenzyl-diethylenetriamine (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) for preoperative prediction of MVI in primary HCC patients. METHODS: This study included a training cohort of 110 HCC patients and a validating cohort of 50 HCC patients. All the patients underwent preoperative Gd-EOB-DTPA-enhanced MRI examination and curative hepatectomy. The volumes of interest (VOIs) around the hepatic lesions including intratumoural and peritumoural regions were manually delineated in the hepatobiliary phase of MRI images, from which quantitative features were extracted and analysed. In the training cohort, machine-learning method was applied for dimensionality reduction and selection of the extracted features. RESULTS: The proportion of MVI-positive patients was 38.2% and 40.0% in the training and validation cohort, respectively. Supervised machine learning selected ten features to establish a predictive model for MVI. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity of the combined intratumoural and peritumoural radiomics model in the training and validation cohort were 0.85 (95% confidence interval (CI), 0.77-0.93), 88.2%, 76.2%, and 0.83 (95% CI, 0.71-0.95), 90.0%, 75.0%, respectively. CONCLUSIONS: We evaluate quantitative Gd-EOB-DTPA-enhanced MRI image features of both intratumoural and peritumoural regions and provide an effective radiomics-based model for the prediction of MVI in HCC patients, and may therefore help clinicians make precise decisions regarding treatment before the surgery. KEY POINTS: • An effective radiomics model for prediction of microvascular invasion in HCC patients is established. • The radiomics model is superior to the radiologist in prediction of MVI. • The radiomics model can help clinicians in pretreatment decision making.
OBJECTIVES: Preoperative prediction of microvascular invasion (MVI) in patients with hepatocellular cancer (HCC) is important for surgery strategy making. We aimed to develop and validate a combined intratumoural and peritumoural radiomics model based on gadolinium-ethoxybenzyl-diethylenetriamine (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) for preoperative prediction of MVI in primary HCC patients. METHODS: This study included a training cohort of 110 HCC patients and a validating cohort of 50 HCC patients. All the patients underwent preoperative Gd-EOB-DTPA-enhanced MRI examination and curative hepatectomy. The volumes of interest (VOIs) around the hepatic lesions including intratumoural and peritumoural regions were manually delineated in the hepatobiliary phase of MRI images, from which quantitative features were extracted and analysed. In the training cohort, machine-learning method was applied for dimensionality reduction and selection of the extracted features. RESULTS: The proportion of MVI-positive patients was 38.2% and 40.0% in the training and validation cohort, respectively. Supervised machine learning selected ten features to establish a predictive model for MVI. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity of the combined intratumoural and peritumoural radiomics model in the training and validation cohort were 0.85 (95% confidence interval (CI), 0.77-0.93), 88.2%, 76.2%, and 0.83 (95% CI, 0.71-0.95), 90.0%, 75.0%, respectively. CONCLUSIONS: We evaluate quantitative Gd-EOB-DTPA-enhanced MRI image features of both intratumoural and peritumoural regions and provide an effective radiomics-based model for the prediction of MVI in HCC patients, and may therefore help clinicians make precise decisions regarding treatment before the surgery. KEY POINTS: • An effective radiomics model for prediction of microvascular invasion in HCC patients is established. • The radiomics model is superior to the radiologist in prediction of MVI. • The radiomics model can help clinicians in pretreatment decision making.
Entities:
Keywords:
Gd-EOB-DTPA; Hepatocellular cancer; Magnetic resonance imaging; Radiomics
Authors: Paul A Yushkevich; Joseph Piven; Heather Cody Hazlett; Rachel Gimpel Smith; Sean Ho; James C Gee; Guido Gerig Journal: Neuroimage Date: 2006-03-20 Impact factor: 6.556
Authors: Vincenzo Mazzaferro; Josep M Llovet; Rosalba Miceli; Sherrie Bhoori; Marcello Schiavo; Luigi Mariani; Tiziana Camerini; Sasan Roayaie; Myron E Schwartz; Gian Luca Grazi; René Adam; Peter Neuhaus; Mauro Salizzoni; Jordi Bruix; Alejandro Forner; Luciano De Carlis; Umberto Cillo; Andrew K Burroughs; Roberto Troisi; Massimo Rossi; Giorgio E Gerunda; Jan Lerut; Jacques Belghiti; Ilka Boin; Jean Gugenheim; Fedja Rochling; Bart Van Hoek; Pietro Majno Journal: Lancet Oncol Date: 2008-12-04 Impact factor: 41.316
Authors: Eran Segal; Claude B Sirlin; Clara Ooi; Adam S Adler; Jeremy Gollub; Xin Chen; Bryan K Chan; George R Matcuk; Christopher T Barry; Howard Y Chang; Michael D Kuo Journal: Nat Biotechnol Date: 2007-05-21 Impact factor: 54.908
Authors: Sasan Roayaie; Iris N Blume; Swan N Thung; Maria Guido; Maria-Isabel Fiel; Spiros Hiotis; Daniel M Labow; Josep M Llovet; Myron E Schwartz Journal: Gastroenterology Date: 2009-06-12 Impact factor: 22.682
Authors: Seung Baek Hong; Sang Hyun Choi; So Yeon Kim; Ju Hyun Shim; Seung Soo Lee; Jae Ho Byun; Seong Ho Park; Kyung Won Kim; Suk Kim; Nam Kyung Lee Journal: Liver Cancer Date: 2021-03-11 Impact factor: 11.740