Predictive Cancer Model
Conditions
Brief 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.
Interventions
OTHERRadiomic Algorithm
Different radiomic and machine learning strategies for radiomic features extraction, sorting features and model constriction
Sponsors
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
FEMALE
Age
22 Years to 99 Years
Healthy volunteers
No
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Platinum sensitivity | 9 years | Platinum sensitivity |
Countries
China
Contacts
Primary ContactRuilin Lei, phD
Outcome results
None listed