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Deep learning provides a new computed tomography-based prognostic biomarker for recurrence prediction in high-grade serous ovarian cancer
Wang, Shuo1,6; Liu, Zhenyu1,6; Rong, Yu5; Zhou, Bin4; Bai, Yan3; Wei, Wei3; Wei, Wei1; Wang, Meiyun3; Guo, Yingkun2; Tian, Jie1,6,7
Source PublicationRadiotherapy and Oncology
2018-11
Issue132Pages:171-177
Abstract

Background and purpose: Recurrence is the main risk for high-grade serous ovarian cancer (HGSOC) and
few prognostic biomarkers were reported. In this study, we proposed a novel deep learning (DL) method
to extract prognostic biomarkers from preoperative computed tomography (CT) images, aiming at providing
a non-invasive recurrence prediction model in HGSOC.
Materials and methods: We enrolled 245 patients with HGSOC from two hospitals, which included a
feature-learning cohort (n = 102), a primary cohort (n = 49) and two independent validation cohorts from
two hospitals (n = 49 and n = 45). We trained a novel DL network in 8917 CT images from the featurelearning
cohort to extract the prognostic biomarkers (DL feature) of HGSOC. Afterward, a DL-CPH model
incorporating the DL feature and Cox proportional hazard (Cox-PH) regression was developed to predict
the individual recurrence risk and 3-year recurrence probability of patients.
Results: In the two validation cohorts, the concordance-index of the DL-CPH model was 0.713 and 0.694.
Kaplan–Meier’s analysis clearly identified two patient groups with high and low recurrence risk
(p = 0.0038 and 0.0164). The 3-year recurrence prediction was also effective (AUC = 0.772 and 0.825),
which was validated by the good calibration and decision curve analysis. Moreover, the DL feature
demonstrated stronger prognostic value than clinical characteristics.
Conclusions: The DL method extracts effective CT-based prognostic biomarkers for HGSOC, and provides a
non-invasive and preoperative model for individualized recurrence prediction in HGSOC. In addition, the
DL-CPH model provides a new prognostic analysis method that can utilize CT data without follow-up for
prognostic biomarker extraction.

KeywordDeep Learning High-grade Serous Ovarian Cancer Recurrence Prognosis Computed Tomography Artificial Intelligence Semi-supervised Learning Auto Encoder Unsupervised Learning
DOI10.1016/j.radonc.2018.10.019
Indexed BySCI
Language英语
WOS IDWOS:000460111700025
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23572
Collection学术期刊
中国科学院分子影像重点实验室
Corresponding AuthorWang, Meiyun; Guo, Yingkun; Tian, Jie
Affiliation1.CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
2.Department of Radiology, Key Laboratory of Birth Defects and Related Diseases of Women and Children of Ministry of Education, West China Second University Hospital, Sichuan University, Sichuan, China
3.Department of Radiology, Henan Provincial People’s Hospital, Henan, China
4.Key Laboratory of Birth Defects and Related Diseases of Women and Children of Ministry of Education, West China Second University Hospital, Sichuan University, Sichuan, China
5.Department of Radiology, Key Laboratory of Intelligent Medical Image Analysis and Precision Diagnosis in Guizhou Province, Guizhou Provincial People's Hospital, Guizhou, China
6.University of Chinese Academy of Sciences, Beijing, China
7.Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing, 100191, china
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Wang, Shuo,Liu, Zhenyu,Rong, Yu,et al. Deep learning provides a new computed tomography-based prognostic biomarker for recurrence prediction in high-grade serous ovarian cancer[J]. Radiotherapy and Oncology,2018(132):171-177.
APA Wang, Shuo.,Liu, Zhenyu.,Rong, Yu.,Zhou, Bin.,Bai, Yan.,...&Tian, Jie.(2018).Deep learning provides a new computed tomography-based prognostic biomarker for recurrence prediction in high-grade serous ovarian cancer.Radiotherapy and Oncology(132),171-177.
MLA Wang, Shuo,et al."Deep learning provides a new computed tomography-based prognostic biomarker for recurrence prediction in high-grade serous ovarian cancer".Radiotherapy and Oncology .132(2018):171-177.
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