e19597 Background: Sexuality is a crucial issue influenced by social norms, shame and moral taboos and is generally not discussed in the clinical day and research community. Aim of our study was to systematically evaluate the sexual function and quality of life of cancer pts after operative and systemic treatment in GM. Methods: We assessed in a prospective setting sexual function and quality of life aspects of patients with histological proven GM after completed treatment. Validated questionnaires about sexuality (Female Sexual Function Index 19 items), quality of life (SF-12) as well an additional semi-structured questionnaire consisting of 20 items were answered by all pts at the earliest 3 months after end of treatment during cancer care follow up visit. Results: Overall 55 pts with median age 61 (range, 22-74) were evaluated including 54% ovarian, 26% breast, 13% cervical cancer, 2% endometrial- and 6% vulvar cancer pts. Overall 32.7% (n=18) of the pts stated that they have sexual problems. Based on the FSFI (<26.5) more pts (58.2%) presented sexual problems. The main reasons for an impaired sexuality were a subjective lost of attractiveness (45%; n=9); vaginal dryness (25%; n=5) followed by fear of injury (20%; n=4). 36.4% (n=20) described a change of sexuality after cancer therapy, 8 pts stated positive, 12 pts negative changes. 40% of the pts stated that they have not searched for information about sexual aspects during or after cancer therapy while 25% of the pts have asked their physicians. Patients who lived in a partnership had higher SF12 scores than singles but not different global QoL-scores. Multivariate analysis revealed ovarian, endometrial, and vulvar cancer but not age, disease stage and presence of partner to negatively affect sexual function . SF12 evaluation showed significantly higher psychological functionality with increasing age. Impaired sexuality was always associated with lower scores in SF12. Conclusions: There is a high need of sexual function assessment after cancer treatment of GM patients using validated questionnaires. Only few pts have access to information about sexuality. Strategies are warranted to improve the discussion about this relevant topic.
5558 Background: Complete resection at secondary cytoreductive surgery is associated with prolonged progression free and overall survival for patients with relapsed ovarian cancer. Secondary cytoreductive surgery has no impact on survival rates, if macroscopically tumor clearance cannot be achieved. Therefore, in order to avoid unnecessary perioperative morbidity and mortality, selection of patients who will undergo secondary tumor debulking is crucial. This study aims to improve upon the contemporary Arbeitsgemeinschaft Gynäkologische Onkologie (AGO) score by including additional clinical variables like circulating HE4 and CA125 levels to predict surgical outcome at secondary cytoreduction. Methods: A total of 90 patients underwent secondary cytoreductive surgery and were retrospectively assigned a positive AGO score. Of those patients, 62 (68.9%) achieved optimal surgical outcome at secondary debulking with 28 (31.1%) patients retaining residual tumor mass ( > 0mm). Utilizing clinical variables including circulating HE4 and CA125 levels, we implemented a machine learning workflow to predict suboptimal surgical outcome in patients despite a positive AGO score. Results: We elucidated significantly lower levels of circulating HE4 (p = 0.0038) in patients with optimal surgical outcome compared to patients that retain macroscopic residual tumor at secondary cytoreductive surgery. Moreover, machine learning algorithms trained on clinical variables (e.g. serum HE4 level, serum CA125 level, age, Risk of Ovarian Malignancy Algorithmus (ROMA) score and occurrence of peritoneal carcinomatosis) achieved a mean area under the curve (AUC) of 78.4% based on 100 consecutive executions with randomized training and test sets. Conclusions: The application of machine learning allows to further improve the prediction of patients with high likelihood of achieving optimal surgical outcome at secondary cytoreduction. In turn, it might identify patients that would benefit from amplified treatment efforts. However, machine learning relies on large amounts of data to account for biological and clinical variation and produce predictions of sufficient/adequate quality. Given this limitation, we would validate this data within the prospective multicentric cohort of patients collected within NOGGO/ENGOT HELP_ER Trial.
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