Development of survival predictors for high-grade serous ovarian cancer based on stable radiomic features from computed tomography images
Jiaqi Hu, Zhiwu Wang, Ruocheng Zuo, Chengcai Zheng, Bingjian Lü, Xiaodong Cheng, Weiguo Lü, Chunhui Zhao, Pengyuan Liu, Yan Lü
Abstract
creening (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.
Topics & Concepts
MedicineSerous fluidRadiomicsOvarian cancerUnivariate analysisRadiologySerous ovarian cancerComputed tomographyOncologyCancerInternal medicineMultivariate analysisRadiomics and Machine Learning in Medical ImagingOvarian cancer diagnosis and treatmentEndometrial and Cervical Cancer Treatments