NCT04511481

Deep Learning Magnetic Resonance Imaging Radiomic Predict Platinum-sensitive in Patients With Epithelial Ovarian Cancer

• Status: Unknown
• Phase: —
• Study type: Observational
• Enrollment: 93 (estimated)
• Sponsor: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University · Academic / Other
• Sex: Female
• Age: 22 Years – 99 Years
• Healthy volunteers: Not accepted

Summary

Platinum-sensitive is an important basis for the treatment of recurrent epithelial ovarian cancer (EOC) without effective methods to predict.We aimed to develop and validate the EOC deep learning system to predict the platinum-sensitive of EOC patients through analysis of enhanced magnetic resonance imaging (MRI) images before initial treatment.Ninety-three EOC patients received platinum-based chemotherapy (\>= 4 cycles) and debulking surgery from Sun Yat-sen Memorial Hospitalin China from January 2011 to January 2020 were enrolled. This deep-learning EOC signature achieved a high predictive power for platinum-sensitive, and the signature based on MRI whole volume is better than that on primary tumor area only.

Conditions

• Predictive Cancer Model

Interventions

Type	Name	Description
OTHER	Radiomic Algorithm	Different radiomic and machine learning strategies for radiomic features extraction, sorting features and model constriction

Timeline

• Start date: 2020-04-15
• Primary completion: 2020-08-03
• Completion: 2021-01-01
• First posted: 2020-08-13
• Last updated: 2020-08-13

Locations

1 site across 1 country: China