Development of survival predictors for high-grade serous ovarian cancer based on stable radiomic features from computed tomography images

iScience

Pengyuan Liu¹  Weiguo Lu¹  Bingjian Lu²  Zhiwu Wang³  Yan Lu⁴  Jiaqi Hu⁵  Chunhui Zhao⁵  Chengcai Zheng⁵  Xiaodong Cheng⁵  Ruocheng Zuo⁵ 

[1] Cancer Center, Zhejiang University, Hangzhou, Zhejiang 310013, China;Department of Critical Care Medicine, the First Affiliated Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325000, China;Department of Chemoradiotherapy, Tangshan People’s Hospital, Tangshan, Hebei 063000, China;Women’s Reproductive Health Key Laboratory of Zhejiang Province, Women’s Hospital and Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310006, China;Zhejiang Provincial Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Department of Gynecologic Oncology, Women’s Hospital and Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310006, China;

关键词: Radiology;    Medical imaging;    Cancer;   

DOI  :  

来源: DOAJ

【 摘 要 】

Summary: Less than 35% of advanced patients with high-grade serous ovarian cancer (HGSOC) survive for 5 years after diagnosis. Here, we developed radiomics-based models to predict HGSOC clinical outcomes using preoperative contrast-enhanced computed tomography (CECT) images. 891 radiomics features were extracted between primary, metastatic, or lymphatic lesions from preoperative venous phase CECT images of 217 patients with HGSOC. A heuristic method, Frequency Appearance in Multiple Univariate preScreening (FAMUS), was proposed to identify stable and task-relevant radiomic features. Using FAMUS, we constructed predictive models of overall survival and disease-free survival in patients with HGSOC based on these stable radiomic features. According to their CT images, patients with HGSOC can be accurately stratified into high-risk or low-risk groups for cancer-related death within 2-6 years or for likely recurrence within 1-5 years. These radiomic models provide convincing and reliable non-invasive markers for individualized prognostic evaluation and clinical decision-making for patients with HGSOC.

【 授权许可】

Unknown