Can Staging Magnetic Resonance Imaging (MRI) Features Prognosticate Patients Presenting With Endometrial Cancer?

NCT03543215UNKNOWNOBSERVATIONAL

Summary

Aim: Assess the value of MRI features in predicting prognosis in patients with endometrial cancer This study will examine the MRI features of women with confirmed endometrial cancer to see if textural features can prognosticate patients.

Key Facts

Lead Sponsor

Imperial College London

Enrollment

500

Start Date

2017-07-13

Completion Date

2021-12-10

Study Type

OBSERVATIONAL

Official Title

Can Staging Magnetic Resonance Imaging (MRI) Features Prognosticate Patients Presenting With Endometrial Cancer?

Interventions

Radiomics: Analysis of textural features of imaging (MRI and Ultrasound)

Conditions

Endometrial CancerEndometrial Neoplasms

Eligibility

Age Range

18 Years+

Sex

FEMALE

Inclusion Criteria:

* All women presenting with a confirmed diagnosis of endometrial cancer
* Reviewed at the Specialist Gynaecology Oncology MDT
* Have MR Imaging and Hysterectomy specimens available for review.

Exclusion criteria:

* Anyone lacking capacity.
* \<18years old.
* Pregnant.
* No MR Imaging available for review -- No pathology specimen available for review

Outcome Measures

Primary Outcomes

To assess the value of MRI features in predicting prognosis in patients with endometrial cancer using progression free survival (PFS) and overall survival (OS) as end points

Assess features of pelvic MRIs (including (i) tumour image intensity, (II) shape, (III) texture and (IV) multiscale wavelet) and create a mathematical algorithm to predict prognostic outcome (5 year survival).

Time frame: 8 years (3 year data collection and 5 year follow up).

Secondary Outcomes

Assess the correlation between imaging features and histopathological features.

Assess textural features of pelvic MRI in conjunction with histopathology (type of tumour, grade LVSI, 'aggressiveness' of cancer

Time frame: 3 years

Locations

Queen Charlotte and Hammersmith Hospital, London, United Kingdom

Linked Diseases

Endometrial Neoplasms

Endometrial Neoplasms — a type of gynecological cancer.

Linked Papers

Weibull parametric model for survival analysis in women with endometrial cancer using clinical and T2-weighted MRI radiomic features

AbstractBackgroundSemiparametric survival analysis such as the Cox proportional hazards (CPH) regression model is commonly employed in endometrial cancer (EC) study. Although this method does not need to know the baseline hazard function, it cannot estimate event time ratio (ETR) which measures relative increase or decrease in survival time. To estimate ETR, the Weibull parametric model needs to be applied. The objective of this study is to develop and evaluate the Weibull parametric model for EC patients’ survival analysis.MethodsTraining (n = 411) and testing (n = 80) datasets from EC patients were retrospectively collected to investigate this problem. To determine the optimal CPH model from the training dataset, a bi-level model selection with minimax concave penalty was applied to select clinical and radiomic features which were obtained from T2-weighted MRI images. After the CPH model was built, model diagnostic was carried out to evaluate the proportional hazard assumption with Schoenfeld test. Survival data were fitted into a Weibull model and hazard ratio (HR) and ETR were calculated from the model. Brier score and time-dependent area under the receiver operating characteristic curve (AUC) were compared between CPH and Weibull models. Goodness of the fit was measured with Kolmogorov-Smirnov (KS) statistic.ResultsAlthough the proportional hazard assumption holds for fitting EC survival data, the linearity of the model assumption is suspicious as there are trends in the age and cancer grade predictors. The result also showed that there was a significant relation between the EC survival data and the Weibull distribution. Finally, it showed that Weibull model has a larger AUC value than CPH model in general, and it also has smaller Brier score value for EC survival prediction using both training and testing datasets, suggesting that it is more accurate to use the Weibull model for EC survival analysis.ConclusionsThe Weibull parametric model for EC survival analysis allows simultaneous characterization of the treatment effect in terms of the hazard ratio and the event time ratio (ETR), which is likely to be better understood. This method can be extended to study progression free survival and disease specific survival.Trial registrationClinicalTrials.gov NCT03543215,https://clinicaltrials.gov/, date of registration: 30th June 2017.

Linked Investigators

Andrea G Rockall