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

NCT04511481 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 93

Last updated 2020-08-13

No results posted yet for this study

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

OTHER

Radiomic Algorithm

Different radiomic and machine learning strategies for radiomic features extraction, sorting features and model constriction

Sponsors & Collaborators

  • Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

    lead OTHER

Eligibility

Min Age
22 Years
Max Age
99 Years
Sex
FEMALE
Healthy Volunteers
No

Timeline & Regulatory

Start
2020-04-15
Primary Completion
2020-08-03
Completion
2021-01-01

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT04511481 on ClinicalTrials.gov