ESOI/EuSoMII Annual Meeting 2016
Patient Workflow: from A to Z
Imaging informatics in oncology
October 6-8, 2016
Registration is open now! Please click here for your registration!
Abstract Submission deadline extended!
Abstract Submission period is from June 13, 2016 until July 18, 2016.
First Prize for Best Oral Presentation:
1.000 Euro for fellowship in selected training center + Certificate
Second Prize for Best Oral Presentation:
Ivitation to Invest in the Youth programme of ECR 2017 incl. registration to ECR + Hotel Voucher (approx 350 Euro for ECR 2016) + Certificate
You may find more details about the congress here.
ESOI/EORTC Autumn Workshop 2016
Imaging in assessing response to cancer therapy
November 9-11, 2016
Registration is open now! Please click here to register!
You may find more details about the workshop here.
ESOI Webinars 2016
ESOI started in May 2015 to offer a series of educational webinars with various topics related to oncologic imaging and has continued with another series in 2016.
All topics and how/where to register will be announced shortly.
ESOI has planned 11 educational webinars for 2016. Each webinar will last approximately 60 minutes (50 min. presentation and 10 min. for discussion/questions).
Registration for the first 2 online educational webinars will be free of charge and will be open to every interested participant. Webinar 3-11 will be free of charge but only open to active ESOI members. Membership is offered for 20,00 euro to full and associate members and for 10,00 euro to residents.
The online educational webinars of ESOI are kindly supported by Bracco.
Upcoming Webinars 2016
7. Staging of ovarian cancer - providing a GPS to patient management
July 25, 2016 - 18:00 CEST
Hedvig Hricak, New York/US
In 2016 an estimated 22,280 women will be diagnosed with ovarian cancer in the United States, approximately 85% of them presenting with advanced-stage disease. In the developed world, ovarian cancer has the highest mortality rate among all gynecological cancers. High-grade serous ovarian carcinoma (HGSOC) is the most common histological subtype of ovarian cancer with high relapse rates and an average 5-year survival rate of around 40%. Early ovarian cancer is treated with comprehensive staging laparotomy, which includes transabdominal hysterectomy and bilateral salpingo-oophorectomy (TAH/BSO), omentectomy, retroperitoneal lymph node sampling, peritoneal and diaphragmatic biopsies and cytology of peritoneal washings. Standard initial therapy for HGSOC consists of primary cytoreductive surgery, or ‘debulking,’ followed by adjuvant chemotherapy (platinum and taxane-based chemotherapy). Patients with non-resectable disease may benefit from neoadjuvant (preoperative) chemotherapy before debulking.
In the management of ovarian cancer, cross-sectional imaging has become critical for tumor characterization as well as treatment selection and planning (identifying difficult-to-reach tumor deposits or inoperable disease for which neoadjuvant chemotherapy is indicated). It has also become invaluable for monitoring treatment response, detecting recurrent disease and, depending on tumor size and location, choosing between secondary cytoreduction and chemotherapy. This lecture will review the key clinically relevant imaging findings for ovarian cancer detection and staging and discuss how imaging--and predictive models that incorporate imaging—may inform patient management. It will also consider how developments in molecular medicine, molecular imaging and the emerging field of radiogenomics may improve the detection and management of ovarian cancer in the future.
- To refresh radiologists’ knowledge of the roles of different cross-sectional imaging modalities in the detection, staging, and pre- and post-treatment evaluation of ovarian cancer.
- To become familiar with key imaging findings and their implications for patient management.
- To become aware of predictive models that incorporate imaging findings and can be used to better inform patient management.
- To become aware of areas of research that may lead to improvements in ovarian cancer detection and evaluation.