Clinical trial NCT04212910: Stratifying Endometrial Cancer Patients Using a PET/MRI Prognostic Model
Official title: Stratifying Endometrial Cancer Patients Using a PET/MRI Prognostic Model
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Endometrium Cancer
Keywords: endometrium
cancer
PET-MRI
stratification
Lead sponsor: IRCCS San Raffaele
Sponsor class: OTHER
First posted: 2019-12-30
Enrollment: 101
Sex: FEMALE
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: CASE_CONTROL
Time perspective: CROSS_SECTIONAL
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients with biopsy proven endometrial cancer presenting to the Institution's Gynecology Unit.

Brief summary: Endometrial cancer (EC) is the most common gynecological malignancy in high-income countries. Prognosis and treatment are dictated by cancer histological subtype and grade coupled with surgical staging as described by Surgical International Federation of Gynecology and Obstetrics (FIGO) staging system. Surgery is the elective standard treatment and used for staging of EC. The purpose of this study is to collect images from a simultaneous PET/MRI study from which to extrapolate a preoperative, non-invasive, prognostic model.

Detailed description: Magnetic resonance imaging (MRI) is the most frequently used imaging tool in preoperative staging of endometrial cancer. FDG-PET has demonstrated higher sensitivity than MRI in detecting nodal involvement and metastatic disease and is currently used in adjunction to MRI. Distinct MRI and FDG-PET parameters correlate well with tumor aggressiveness and can and have been used as prognostic factors. The Investigators believe that the combination of data from these two imaging techniques, from which different derived tumor features can be extrapolated, can stratify patients in high-risk and low-risk groups already preoperatively. The aim of combined techniques is to collect large amounts of data leveraging advantages of both modalities (MR's strengths in local staging and PET's strengths in nodal staging) in a single scan from which to extrapolate a preoperative, non-invasive, prognostic model.

Eligibility criteria: Inclusion Criteria:

* histopathological confirmation of a primary endometrial cancer
* age \> 18 years
* no contraindication to surgery (comorbidity, contraindication or lack of consent)
* no contraindication to preoperative imaging
* visible tumor at imaging
* signed inform consent.

Exclusion Criteria:

* patients unsuitable for surgical intervention (comorbidity, contraindication or lack of consent, poor performance status)
* age \< 18 years, c) inability to complete the needed imaging examination (ie, severe claustrophobia)
* any additional medical condition that may significantly interfere with study compliance
* all the contraindication for MRI (ie, pacemaker).

Primary outcomes:
measure: MRI evaluation
timeFrame: 0-2 years
measure: MRI imaging
timeFrame: 0-2 years
measure: MRI imaging evaluation
timeFrame: 0-2 years
measure: DCE-MRI perfusion parameters
timeFrame: 0-2 years
measure: MRI
timeFrame: 0-2 years
measure: PET imaging evaluation
timeFrame: 0-2 years
measure: PET imaging
timeFrame: 0-2 years
Secondary outcomes:
measure: Measure tumor angiogenesis
timeFrame: 0-2 years
measure: Correlate PET and MRI derived functional and morphological parameters with histology
timeFrame: 0-5 years
Other outcomes:
Locations:
city: Milano
country: Italy
facility: IRCCS Ospedale San Raffaele
Study officials:
References:
citation: Kitajima K, Kita M, Suzuki K, Senda M, Nakamoto Y, Sugimura K. Prognostic significance of SUVmax (maximum standardized uptake value) measured by [(1)(8)F]FDG PET/CT in endometrial cancer. Eur J Nucl Med Mol Imaging. 2012 May;39(5):840-5. doi: 10.1007/s00259-011-2057-9. Epub 2012 Feb 17.
citation: Nakamura K, Kodama J, Okumura Y, Hongo A, Kanazawa S, Hiramatsu Y. The SUVmax of 18F-FDG PET correlates with histological grade in endometrial cancer. Int J Gynecol Cancer. 2010 Jan;20(1):110-5. doi: 10.1111/IGC.0b013e3181c3a288.