机构:[1]Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an, China[2]CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China[3]School of Applied Technology, Xi’an Polytechnic University, Xi’an, China[4]School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China[5]Key Laboratory of Intelligent Medical Image Analysis and Precision Diagnosis in Guizhou Province, Department of Radiology, Guizhou Provincial People’s Hospital, Guiyang, China[6]Key Laboratory of Birth Defects and Related Diseases of Women and Children of Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, China[7]Department of Radiology, Henan Provincial People’s Hospital, Zhengzhou, China[8]Key Laboratory of Birth Defects and Related Diseases of Women and Children of Ministry of Education, Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu, China[9]Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, China
Objectives: We used radiomic analysis to establish a radiomic signature based on preoperative contrast enhanced computed tomography (CT) and explore its effectiveness as a novel recurrence risk prognostic marker for advanced high-grade serous ovarian cancer (HGSOC). Methods: This study had a retrospective multicenter (two hospitals in China) design and a radiomic analysis was performed using contrast enhanced CT in advanced HGSOC (FIGO stage III or IV) patients. We used a minimum 18-month follow-up period for all patients (median 38.8 months, range 18.8-81.8 months). All patients were divided into three cohorts according to the timing of their surgery and hospital stay: training cohort (TC) and internal validation cohort (IVC) were from one hospital, and independent external validation cohort (IEVC) was from another hospital. A total of 620 3-D radiomic features were extracted and a Lasso-Cox regression was used for feature dimension reduction and determination of radiomic signature. Finally, we combined the radiomic signature with seven common clinical variables to develop a novel nomogram using a multivariable Cox proportional hazards model. Results: A final 142 advanced HGSOC patients were enrolled. Patients were successfully divided into two groups with statistically significant differences based on radiomic signature, consisting of four radiomic features (log-rank test P = 0.001, <0.001, <0.001 for TC, IVC, and IEVC, respectively). The discrimination accuracies of radiomic signature for predicting recurrence risk within 18 months were 82.4% (95% CI, 77.8-87.0%), 77.3% (95% CI, 74.4-80.2%), and 79.7% (95% CI, 73.8-85.6%) for TC, IVC, and IEVC, respectively. Further, the discrimination accuracies of radiomic signature for predicting recurrence risk within 3 years were 83.4% (95% CI, 77.3-89.6%), 82.0% (95% CI, 78.9-85.1%), and 70.0% (95% CI, 63.6-76.4%) for TC, IVC, and IEVC, respectively. Finally, the accuracy of radiomic nomogram for predicting 18-month and 3-year recurrence risks were 84.1% (95% CI, 80.5-87.7%) and 88.9% (95% CI, 85.8-92.5%), respectively. Conclusions: Radiomic signature and radiomic nomogram may be low-cost, non-invasive means for successfully predicting risk for postoperative advanced HGSOC recurrence before or during the perioperative period. Radiomic signature is a potential prognostic marker that may allow for individualized evaluation of patients with advanced HGSOC.
基金:
National Key Research and Development Plan of China (2017YFA0205200,2016YFC0103001, YS2017YFGH000397), the National Natural Science Foundation of China (81227901, 81527805, 81772012,81720108021, 81641168), the Beijing Natural Science Foundation (7182109), Beijing Municipal Science & Technology Commission(Z171100000117023, Z161100002616022), Chinese Academy of Sciences (GJJSTD20170004, QYZDJ-SSW-JSC005), and the Henan Province Scientific and Technological Cooperation Project (152106000014).
语种:
外文
PubmedID:
中科院(CAS)分区:
出版当年[2019]版:
大类|3 区医学
小类|3 区肿瘤学
最新[2023]版:
大类|3 区医学
小类|3 区肿瘤学
第一作者:
第一作者机构:[1]Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an, China[2]CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China[3]School of Applied Technology, Xi’an Polytechnic University, Xi’an, China
共同第一作者:
通讯作者:
通讯机构:[1]Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an, China[2]CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China[4]School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China[9]Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, China
推荐引用方式(GB/T 7714):
Wei Wei,Liu Zhenyu,Rong Yu,et al.A Computed Tomography-Based Radiomic Prognostic Marker of Advanced High-Grade Serous Ovarian Cancer Recurrence: A Multicenter Study.[J].Frontiers in oncology.2019,9:255.doi:10.3389/fonc.2019.00255.
APA:
Wei Wei,Liu Zhenyu,Rong Yu,Zhou Bin,Bai Yan...&Tian Jie.(2019).A Computed Tomography-Based Radiomic Prognostic Marker of Advanced High-Grade Serous Ovarian Cancer Recurrence: A Multicenter Study..Frontiers in oncology,9,
MLA:
Wei Wei,et al."A Computed Tomography-Based Radiomic Prognostic Marker of Advanced High-Grade Serous Ovarian Cancer Recurrence: A Multicenter Study.".Frontiers in oncology 9.(2019):255