Ovarian damage from chemotherapy and current approaches to its protection

BACKGROUND: Anti-cancer therapy is often a cause of premature ovarian insufficiency and infertility since the ovarian follicle reserve is extremely sensitive to the effects of chemotherapy and radiotherapy. While oocyte, embryo and ovarian cortex cryopreservation can help some women with cancer-induced infertility achieve pregnancy, the development of effective methods to protect ovarian function during chemotherapy would be a significant advantage.OBJECTIVE AND RATIONALE: This paper critically discusses the different damaging effects of the most common chemotherapeutic compounds on the ovary, in particular, the ovarian follicles and the molecular pathways that lead to that damage. The mechanisms through which fertility-protective agents might prevent chemotherapy drug-induced follicle loss are then reviewed.SEARCH METHODS: Articles published in English were searched on PubMed up to March 2019 using the following terms: ovary, fertility preservation, chemotherapy, follicle death, adjuvant therapy, cyclophosphamide, cisplatin, doxorubicin. Inclusion and exclusion criteria were applied to the analysis of the protective agents.OUTCOMES: Recent studies reveal how chemotherapeutic drugs can affect the different cellular components of the ovary, causing rapid depletion of the ovarian follicular reserve. The three most commonly used drugs, cyclophosphamide, cisplatin and doxorubicin, cause premature ovarian insufficiency by inducing death and/or accelerated activation of primordial follicles and increased atresia of growing follicles. They also cause an increase in damage to blood vessels and the stromal compartment and increment inflammation. In the past 20 years, many compounds have been investigated as potential protective agents to counteract these adverse effects. The interactions of recently described fertility-protective agents with these damage pathways are discussed.WIDER IMPLICATIONS: Understanding the mechanisms underlying the action of chemotherapy compounds on the various components of the ovary is essential for the development of efficient and targeted pharmacological therapies that could protect and prolong female fertility. While there are increasing preclinical investigations of potential fertility preserving adjuvants, there remains a lack of approaches that are being developed and tested clinically

Endometrial Cancer Individualized Scoring System (ECISS): A machine learning-based prediction model of endometrial cancer prognosis

Objective: To establish a prognostic model for endometrial cancer (EC) that individualizes a risk and management plan per patient and disease characteristics. Methods: A multicenter retrospective study conducted in nine European gynecologic cancer centers. Women with confirmed EC between January 2008 to December 2015 were included. Demographics, disease characteristics, management, and follow-up information were collected. Cancer-specific survival (CSS) and disease-free survival (DFS) at 3 and 5 years comprise the primary outcomes of the study. Machine learning algorithms were applied to patient and disease characteristics. Model I: pretreatment model. Calculated probability was added to management variables (model II: treatment model), and the second calculated probability was added to perioperative and postoperative variables (model III). Results: Of 1150 women, 1144 were eligible for 3-year survival analysis and 860 for 5-year survival analysis. Model I, II, and III accuracies of prediction of 5-year CSS were 84.88%/85.47% (in train and test sets), 85.47%/84.88%, and 87.35%/86.05%, respectively. Model I predicted 3-year CSS at an accuracy of 91.34%/87.02%. Accuracies of models I, II, and III in predicting 5-year DFS were 74.63%/76.72%, 77.03%/76.72%, and 80.61%/77.78%, respectively. Conclusion: The Endometrial Cancer Individualized Scoring System (ECISS) is a novel machine learning tool assessing patient-specific survival probability with high accuracy

Endometrial Cancer Individualised Scoring System (ECISS): A machine learning-based prediction model of endometrial cancer prognosis

Objective: Establishing a prognostic model for endometrial cancer (EC), that individualizes risk and management plan per patient and disease characteristics. Methods: this is multicentre retrospective study conducted in 9 European gynaecologic cancer centres. Women with confirmed EC between January 2008 to December 2015 were included. Demographics, disease characteristics, management, and follow-up information were collected. Cancer-specific survival (CSS) and disease-free survival (DFS) at 3 and 5 years comprise the primary outcomes of the study. Machine learning algorithms were applied to patient and disease characteristics. Model I: pre-treatment model. Calculated probability was added to management variables (model II: treatment model), and the second calculated probability was added to perioperative and postoperative variables (model III). Results: Out of 1,150 women, 1,144 were eligible for 3-year survival analysis and 860 for 5-year survival analysis. Model I, II, and III accuracies of prediction of 5-year CSS were 84.88%/85.47% (in train and test sets), 85.47%/84.88% and 87.35%/86.05%, respectively. Model I predicted 3-year CSS at an accuracy of 91.34%/87.02%. Accuracy of model I, II and III in predicting 5-year DFS were 74.63%/76.72%, 77.03%/76.72%, and 80.61%/77.78%, respectively. Conclusion: Endometrial Cancer Individualised Scoring System (ECISS) is a novel machine learning tool assessing patient-specific survival probability with high accuracy