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

Sun Yat-sen University

Status: Unknown

Conditions: Predictive Cancer Model

Treatments: Other: Radiomic Algorithm

Study type: Observational

Funder types: Other

Identifiers

NCT04511481

SYSEC-KY-KS-2020-072

Details and patient eligibility

About

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.

Enrollment: 93 estimated patients

Sex: Female

Ages: 22 to 99 years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• (1)Patients with epithelial ovarian cancer (2 )Patients received platinum-based chemotherapy (>= 4 cycles) and debulking surgery

Exclusion criteria

• Patients with epithelial ovarian cancer received less than 4 cycles platinum-based chemotherapy or no debulking surgery

Trial design

93 participants in 2 patient groups

platinum-resistant group

Treatment:

Other: Radiomic Algorithm

platinum-sensitive group

Treatment:

Other: Radiomic Algorithm