Incremental value (IncV) evaluates the performance change between an existing risk model and a new model. Different IncV metrics do not always agree with each other. For example, compared with a prescribed-dose model, an ovarian-dose model for predicting acute ovarian failure has a slightly lower area under the receiver operating characteristic curve (AUC) but increases the area under the precision-recall curve (AP) by 48%. This phenomenon of disagreement is not uncommon, and can create confusion when assessing whether the added information improves the model prediction accuracy. In this article, we examine the analytical connections and differences between the AUC IncV (ΔAUC) and AP IncV (ΔAP). We also compare the true values of these two IncV metrics in a numerical study. Additionally, as both are semi-proper scoring rules, we compare them with a strictly proper scoring rule: the IncV of the scaled Brier score (ΔsBrS) in the numerical study. We demonstrate that ΔAUC and ΔAP are both weighted averages of the changes (from the existing model to the new one) in separating the risk score distributions between events and non-events. However, ΔAP assigns heavier weights to the changes in higher-risk regions, whereas ΔAUC weights the changes equally. Due to this difference, the two IncV metrics can disagree, and the numerical study shows that their disagreement becomes more pronounced as the event rate decreases. In the numerical study, we also find that ΔAP has a wide range, from negative to positive, but the range of ΔAUC is much smaller. In addition, ΔAP and ΔsBrS are highly consistent, but ΔAUC is negatively correlated with ΔsBrS and ΔAP when the event rate is low. ΔAUC treats the wins and losses of a new risk model equally across different risk regions. When neither the existing or new model is the true model, this equality could attenuate a superior performance of the new model for a sub-region. In contrast, ΔAP accentuates the change in the prediction accuracy for higher-risk regions.

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A relationship between the incremental values of area under the ROC curve and of area under the precision-recall curve

Breast cancer remains the most common cancer diagnosed in women and causes more lost disability-adjusted life years (DALYs) than any other cancer worldwide; however, improvements in therapies have led to increased survival and therefore a new focus on quality of life following treatment. Fertility is an important concern among cancer survivors of reproductive age. The purpose of this article is to contextualize the importance of oncofertility services for women with breast cancer and review options for fertility preservation, including oocyte/embryo cryopreservation, GnRH agonist therapy, and ovarian tissue cryopreservation. We also discuss special considerations for preimplantation genetic testing for women with germline pathogenic mutations associated with breast cancer, as well as issues related to endocrine therapy. Finally, we review barriers to accessing fertility preservation services, including cost of treatment and lack of referral to reproductive care providers or fertility preservation programs. 

A review of fertility preservation in patients with breast cancer.

Fertility Considerations in Pediatric and Adolescent Patients Undergoing Cancer Therapy.

Aim: To develop a predictive model for ovarian failure (OF) after chemotherapy in young post-pubertal women with cancer. Methods: Retrospective, monocentric cohort study including 348 patients referring to the Oncofertility Unit of San Raffaele Hospital (Milan, Italy) from August 2011 to January 2020. A predictive model was constructed by multivariate logistic regression and receiver operating characteristic analysis. Results: Data about menstrual function resumption were available for 184 patients. The best predictive model for OF was identified by the combination of age; number of chemotherapy lines; vincristine, adriamycin, ifosphamide/adriamycin, ifosphamide; capecitabine; adriamycin, bleomycine, vinblastine, doxorubicin (area under the curve= 0.906, CI 95% 0.858-0.954, p = 0.0001). Conclusions: The model predicts the probability of loss of ovarian function at cancer diagnosis and with every change of treatment.

Ovarian failure risk in post-pubertal patients with cancer: a prognostic model.

The sterilizing effect of cancer treatment depends mostly on the chemotherapy regimen and extent of radiotherapy. Prediction of long-term reproductive outcomes among cancer survivors according to chemo-radiotherapy regimen may improve pre-treatment fertility preservation counseling and future reproductive outcomes. The aim of this study was to evaluate long term reproductive outcomes in cancer survivors according to gonadotoxicity risk estimation of the chemo-radiotherapy regimens utilized. This retrospective cohort study was comprised of post-pubertal female patients referred for fertility preservation during 1997 and 2017 was performed. Eligible adult patients were addressed and asked to complete a clinical survey regarding their ovarian function, menstruation, reproductive experience and ovarian tissue auto-transplantation procedures. Results were stratified according to the gonadotoxic potential of chemotherapy and radiotherapy they received—low, moderate and high-risk, defined by the regimen used, the cumulative dose of chemotherapy administered and radiation therapy extent. A total of 120 patients were eligible for the survey. Of those, 92 patients agreed to answer the questionnaire. Data regarding chemotherapy regimen were available for 77 of the 92 patients who answered the questionnaire. Menopause symptoms were much more prevalent in patients undergoing high vs moderate and low-risk chemotherapy protocol. (51.4% vs. 27.3% and 16.7%, respectively; p < 0.05). Spontaneous pregnancy rates were also significantly lower in the high-risk compared with the low-risk gonadotoxicity regimen group (32.0% vs. 58.3% and 87.5%, respectively; p < 0.05). Patients scheduled for aggressive cancer treatment have significantly higher rates of menopause symptoms and more than double the risk of struggling to conceive spontaneously. Improving prediction of future reproductive outcomes according to treatment protocol and counseling in early stages of cancer diagnosis and treatment may contribute to a tailored fertility related consultation among cancer survivors.

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Chemotherapy-based gonadotoxicity risk evaluation as a predictor of reproductive outcomes in post-pubertal patients following ovarian tissue cryopreservation.

Summary Background Cancer treatment can cause gonadal impairment. Acute ovarian failure is defined as the permanent loss of ovarian function within 5 years of cancer diagnosis. We aimed to develop and validate risk prediction tools to provide accurate clinical guidance for paediatric patients with cancer. Methods In this cohort study, prediction models of acute ovarian failure risk were developed using eligible female US and Canadian participants in the Childhood Cancer Survivor Study (CCSS) cohort and validated in the St Jude Lifetime Cohort (SJLIFE) Study. 5-year survivors from the CCSS cohort were included if they were at least 18 years old at their most recent follow-up and had complete treatment exposure and adequate menstrual history (including age at menarche, current menstrual status, age at last menstruation, and menopausal aetiology) information available. Participants in the SJLIFE cohort were at least 10-year survivors. Participants were excluded from the prediction analysis if they had an ovarian hormone deficiency, had missing exposure information, or had indeterminate ovarian status. The outcome of acute ovarian failure was defined as permanent loss of ovarian function within 5 years of cancer diagnosis or no menarche after cancer treatment by the age of 18 years. Logistic regression, random forest, and support vector machines were used as candidate methods to develop the risk prediction models in the CCSS cohort. Prediction performance was evaluated internally (in the CCSS cohort) and externally (in the SJLIFE cohort) using the areas under the receiver operating characteristic curve (AUC) and the precision-recall curve (average precision [AP; average positive predictive value]). Findings Data from the CCSS cohort were collected for participants followed up between Nov 3, 1992, and Nov 25, 2016, and from the SJLIFE cohort for participants followed up between Oct 17, 2007, and April 16, 2012. Of 11 336 female CCSS participants, 5886 (51·9%) met all inclusion criteria for analysis. 1644 participants were identified from the SJLIFE cohort, of whom 875 (53·2%) were eligible for analysis. 353 (6·0%) of analysed CCSS participants and 50 (5·7%) of analysed SJLIFE participants had acute ovarian failure. The overall median follow-up for the CCSS cohort was 23·9 years (IQR 20·4–27·9), and for SJLIFE it was 23·9 years (19·0–30·0). The three candidate methods (logistic regression, random forest, and support vector machines) yielded similar results, and a prescribed dose model with abdominal and pelvic radiation doses and an ovarian dose model with ovarian radiation dosimetry using logistic regression were selected. Common predictors in both models were history of haematopoietic stem-cell transplantation, cumulative alkylating drug dose, and an interaction between age at cancer diagnosis and haematopoietic stem-cell transplant. External validation of the model in the SJLIFE cohort produced an estimated AUC of 0·94 (95% CI 0·90–0·98) and AP of 0·68 (95% CI 0·53–0·81) for the ovarian dose model, and AUC of 0·96 (0·94–0·97) and AP of 0·46 (0·34–0·61) for the prescribed dose model. Based on these models, an online risk calculator has been developed for clinical use. Interpretation Both acute ovarian failure risk prediction models performed well. The ovarian dose model is preferred if ovarian radiation dosimetry is available. The models, along with the online risk calculator, could help clinical discussions regarding the need for fertility preservation interventions in girls and young women newly diagnosed with cancer. Funding Canadian Institutes of Health Research, Women and Children's Health Research Institute, National Cancer Institute, and American Lebanese Syrian Associated Charities.

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Predicting acute ovarian failure in female survivors of childhood cancer: a cohort study in the Childhood Cancer Survivor Study (CCSS) and the St Jude Lifetime Cohort (SJLIFE).

Ngoc Khanh Vu

Papers

Predicting acute ovarian failure in female survivors of childhood cancer: a cohort study in the Childhood Cancer Survivor Study (CCSS) and the St Jude Lifetime Cohort (SJLIFE).