12:55 CET
E³ 319 - Whole-body imaging in gynaecological malignancy
Oncologic Imaging Genitourinary Molecular Imaging Hybrid Imaging
Wednesday, February 27, 14:00 - 15:30
Type of session: E³ - ECR Academies: Hot Topics in GU Cancer
Topic: Oncologic Imaging, Genitourinary, Molecular Imaging, Hybrid Imaging
Moderator: L. S. Fournier (Paris/FR)

Chairperson's introduction
L. S. Fournier; Paris/FR
A. WB MRI for staging and treatment planning in ovarian cancer
V. Vandecaveye; Leuven/BE
Learning Objectives

1. To learn the MRI technique for imaging the peritoneum in advanced ovarian cancer.
2. To learn the appearances of WB-MRI in metastatic ovarian cancer.
3. To be aware of the pitfalls to interpretation.

B. PET/CT and PET/MRI in cervix and endometrial cancer: current status
L. Umutlu; Essen/DE
Learning Objectives

1. To learn the indications for use of hybrid imaging in cervix and endometrial cancer.
2. To know the strengths and weaknesses of the technique.
3. To be familiar with the role of hybrid imaging in patient prognosis.

C. Advanced imaging techniques in metastatic gynaecological cancer
E. Sala; Cambridge/GB
Learning Objectives

1. To learn about the concept and technique of texture analysis.
2. To be familiar with the key associations of biology and texture features.
3. To be familiar with the potential added value of texture analysis in image interpretation.


Tumour heterogeneity in metastatic gynaecological cancer and especially advanced ovarian cancer has been reported at the histological and genetic levels and found to be associated with adverse clinical outcomes. Classic tumour evaluation using standard CT or MRI techniques does not account for the intra- or inter-tumoural heterogeneity in advanced ovarian cancer with peritoneal carcinomatosis. As such, computational approaches in assessing tumour heterogeneity have been proposed using radiomics and radiogenomics to capitalise the whole tumour heterogeneity as opposed to single biopsy sampling. As part of radiomics, texture analysis which includes the extraction of multiple data from the images has been proposed recently to evaluate advanced ovarian tumour heterogeneity. The preliminary data suggests that it can unravel tumour heterogeneity and predict response to treatment both conventional and immunotherapy.

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