BackgroundBreast cancer is the most common cancer in women worldwide, with a great diversity in outcomes among individual patients. The ability to accurately predict a breast cancer outcome is important to patients, physicians, researchers, and policy makers. Many models have been developed and tested in different settings. We systematically reviewed the prognostic models developed and/or validated for patients with breast cancer.MethodsWe conducted a systematic search in four electronic databases and some oncology websites, and a manual search in the bibliographies of the included studies. We identified original studies that were published prior to 1st January 2017, and presented the development and/or validation of models based mainly on clinico-pathological factors to predict mortality and/or recurrence in female breast cancer patients.ResultsFrom the 96 articles selected from 4095 citations found, we identified 58 models, which predicted mortality (n = 28), recurrence (n = 23), or both (n = 7). The most frequently used predictors were nodal status (n = 49), tumour size (n = 42), tumour grade (n = 29), age at diagnosis (n = 24), and oestrogen receptor status (n = 21). Models were developed in Europe (n = 25), Asia (n = 13), North America (n = 12), and Australia (n = 1) between 1982 and 2016. Models were validated in the development cohorts (n = 43) and/or independent populations (n = 17), by comparing the predicted outcomes with the observed outcomes (n = 55) and/or with the outcomes estimated by other models (n = 32), or the outcomes estimated by individual prognostic factors (n = 8). The most commonly used methods were: Cox proportional hazards regression for model development (n = 32); the absolute differences between the predicted and observed outcomes (n = 30) for calibration; and C-index/AUC (n = 44) for discrimination.Overall, the models performed well in the development cohorts but less accurately in some independent populations, particularly in patients with high risk and young and elderly patients. An exception is the Nottingham Prognostic Index, which retains its predicting ability in most independent populations.ConclusionsMany prognostic models have been developed for breast cancer, but only a few have been validated widely in different settings. Importantly, their performance was suboptimal in independent populations, particularly in patients with high risk and in young and elderly patients.Electronic supplementary materialThe online version of this article (10.1186/s12885-019-5442-6) contains supplementary material, which is available to authorized users.

Background: Women diagnosed with breast cancer, their doctors, and their families, would find a valid estimate of her prognosis helpful in planning treatment and support. Assessing prognosis is complex as many factors influence it. Several predictive models have been produced, but none has been developed or tested on patients in New Zealand (NZ). Aim: We aimed to develop and validate a NZ predictive model (NZPM) for breast cancer, and compare its performance to a widely used UK-developed model, the Nottingham Prognostic Index (NPI). Methods: We developed a model to predict 10-year breast cancer-specific survival, using data collected prospectively in the largest population-based breast cancer registry in NZ (Auckland, 9182 patients), and assessed its performance in this data set (internal validation) and in an independent NZ population-based series of 2625 patients in Waikato (external validation). The data included all women with primary invasive breast cancer diagnosed from 1 June 2000 to 30 June 2014, with follow-up to death or to 31 December 2014. We used multivariate Cox proportional hazards regression to assess predictors and to estimate the probability of breast cancer mortality within 10 years, and therefore 10-year survival, for each patient. We assessed observed survival by the Kaplan-Meier method. We assessed discrimination by the C-statistic, and calibration by comparing predicted and observed survival rates for patients in 10 groups ordered by predicted 10-year survival. We compared this NZPM with the NPI in the validation data set. Results: The final NZPM used continuous variables of age, tumor size, and number of positive lymph nodes, and categorical variables of ethnicity, tumor stage, tumor grade, ER and PR receptors, HER2 status, and histologic type of tumor. Discrimination was good: C-statistics were 0.84 for internal validity and 0.83 for independent external validity. For calibration, for both internal and external validity, the predicted 10-year survival probabilities in 10 groups of patients, ordered by predicted survival, were all within the 95% confidence intervals (CI) of the observed Kaplan-Meier survival probabilities. The NZPM showed good discrimination even within the prognostic groups defined by the NPI. Conclusion: These results for the NZPM show good internal and external validity, transportability, potential clinical value, and its clear superiority over the NPI. Further research will assess other potential predictors, other outcomes, performance in specific subgroups of patients, and compare the NZPM to other models, which have been developed in other countries and have not yet been tested in NZ.

Background Parity is associated with decreased risk of invasive ovarian cancer; however, the relationship between incomplete pregnancies and invasive ovarian cancer risk is unclear. This relationship was examined using 15 case-control studies from the Ovarian Cancer Association Consortium (OCAC). Histotype-specific associations, which have not been examined previously with large sample sizes, were also evaluated. Methods A pooled analysis of 10,470 invasive epithelial ovarian cancer cases and 16,942 controls was conducted. Odds ratios and 95% confidence intervals for the association between incomplete pregnancies and invasive epithelial ovarian cancer were estimated using logistic regression. All models were conditioned on OCAC study, race/ethnicity, age, and education level, and adjusted for number of complete pregnancies, oral contraceptive use, and history of breastfeeding. The same approach was used for histotype-specific analyses. Results Ever having an incomplete pregnancy was associated with a 16% reduction in ovarian cancer risk (OR = 0.84, 95% CI = 0.79 to 0.89). There was a trend of decreasing risk with increasing number of incomplete pregnancies (two-sided Ptrend <.001). An inverse association was observed for all major histotypes; it was strongest for clear cell ovarian cancer. Conclusions Incomplete pregnancies are associated with a reduced risk of invasive epithelial ovarian cancer. Pregnancy, including incomplete pregnancy, was associated with a greater reduction in risk of clear cell ovarian cancer, but the result was broadly consistent across histotypes. Future work should focus on understanding the mechanisms underlying this reduced risk.

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Background: Combined oral contraceptive use is associated with a decreased risk of invasive epithelial ovarian cancer (ovarian cancer). There is suggestive evidence of an inverse association between progestin-only contraceptive use and ovarian cancer risk, but previous studies have been underpowered. Methods: The current study used primary data from 7,977 women with ovarian cancer and 11,820 control women in seven case–control studies from the Ovarian Cancer Association Consortium to evaluate the association between use of depot-medroxyprogesterone acetate (DMPA), an injectable progestin-only contraceptive, and ovarian cancer risk. Logistic models were fit to determine the association between ever use of DMPA and ovarian cancer risk overall and by histotype. A systematic review of the association between DMPA use and ovarian cancer risk was conducted. Results: Ever use of DMPA was associated with a 35% decreased risk of ovarian cancer overall (OR, 0.65; 95% confidence interval, 0.50–0.85). There was a statistically significant trend of decreasing risk with increasing duration of use (Ptrend < 0.001). The systematic review yielded six studies, four of which showed an inverse association and two showed increased risk. Conclusions: DMPA use appears to be associated with a decreased risk of ovarian cancer in a duration-dependent manner based on the preponderance of evidence. Further study of the mechanism through which DMPA use is associated with ovarian cancer is warranted. Impact: The results of this study are of particular interest given the rise in popularity of progestin-releasing intrauterine devices that have a substantially lower progestin dose than that in DMPA, but may have a stronger local effect.

A series of experiments were performed to investigate the effect of ignition energy (Eig) and hydrogen addition on the laminar burning velocity (Su0), ignition delay time (tdelay), and flame rising time (trising) of lean methane−air mixtures. The mixtures at three different equivalence ratios (ϕ) of 0.6, 0.7, and 0.8 with varying hydrogen volume fractions from 0 to 50% were centrally ignited in a constant volume combustion chamber by a pair of pin-to-pin electrodes at a spark gap of 2.0 mm. In situ ignition energy (Eig ∼2.4 mJ ÷ 58 mJ) was calculated by integration of the product of current and voltage between positive and negative electrodes. The result revealed that the Su0 value increases non-linearly with increasing hydrogen fraction at three equivalence ratios of 0.6, 0.7, and 0.8, by which the increasing slope of Su0 changes from gradual to drastic when the hydrogen fraction is greater than 20%. tdelay and trising decrease quickly with increasing hydrogen fraction; however, trising drops faster than tdelay at ϕ = 0.6 and 0.7, and the reverse is true at ϕ = 0.8. Furthermore, tdelay transition is observed when Eig > Eig,critical, by which tdelay drastically drops in the pre-transition and gradually decreases in the post-transition. These results may be relevant to spark ignition engines operated under lean-burn conditions.

The effect of risk factors on ovarian cancer differs by histotype, and the prevalence of such risk factors varies by race/ethnicity. It is not clear how ovarian cancer incidence has changed over time by histotype and race/ethnicity. We used the Surveillance, Epidemiology, and End Results Program (SEER-12) 1992-2019 data to examine the trend of ovarian cancer incidence for three histotypes (high-grade serous N=19,691, endometrioid N=3,212, and clear cell N=3,275) and four racial/ethnic groups (Asian/Pacific Islander, Hispanic, non-Hispanic Black and non-Hispanic White). Joinpoint and age-period-cohort analyses were conducted to analyze ovarian cancer incidence trends. High-grade serous cancer was the most common histotype, but its incidence has significantly decreased over time for all racial/ethnic groups; the decrease was largest for non-Hispanic White women (average annual percent change AAPC during 2010-2019= -6.1, 95% confidence interval (CI) -8.0 to -4.2). Conversely, clear cell cancer was most common in the Asian/Pacific Islanders, and its incidence has increased over time, particularly among Hispanic and Asian/Pacific Islander women (AAPC during 2010-2019=2.8, 95% CI 0.8 to 4.7, and AAPC=1.5, 95% CI 0.7 to 2.2, respectively). Endometrioid cancer incidence has decreased in non-Hispanic White but increased in Hispanic women (AAPC during 2010-2019= -1.3, 95% CI -1.9 to -0.8, and AAPC=3.6, 95% CI 1.0 to 6.3, respectively). The differential incidence trends by histotype and race/ethnicity underscore the need to monitor incidence and risk factor trends across different groups and develop targeted preventive interventions to reduce the burden of ovarian cancer and disparity by race/ethnicity.

BackgroundThe only available predictive models for the outcome of breast cancer patients in New Zealand (NZ) are based on data in other countries. We aimed to develop and validate a predictive model using NZ data for this population, and compare its performance to a widely used overseas model, the Nottingham Prognostic Index (NPI).MethodsWe developed a model to predict 10-year breast cancer-specific survival, using data collected prospectively in the largest population-based regional breast cancer registry in NZ (Auckland, 9182 patients), and assessed its performance in this data set (internal validation) and in an independent NZ population-based series of 2625 patients in Waikato (external validation). The data included all women with primary invasive breast cancer diagnosed from 1 June 2000 to 30 June 2014, with follow up to death or Dec 31, 2014. We used multivariate Cox proportional hazards regression to assess predictors and to calculate predicted 10-year breast cancer mortality, and therefore survival, probability for each patient. We assessed observed survival by the Kaplan Meier method. We assessed discrimination by the C statistic, and calibration by comparing predicted and observed survival rates for patients in 10 groups ordered by predicted 10-year survival. We compared this NZ model with the Nottingham Prognostic Index (NPI) in this validation data set.ResultsDiscrimination was good: C statistics were 0.84 for internal validity and 0.83 for an independent external validity. For calibration, for both internal and external validity the predicted 10-year survival probabilities in all groups of patients, ordered by predicted survival, were within the 95% confidence intervals (CI) of the observed Kaplan-Meier survival probabilities. The NZ model showed good discrimination even within the prognostic groups defined by the NPI.ConclusionsThese results for the New Zealand model show good internal and external validity, transportability, and potential clinical value of the model, and its clear superiority over the NPI. Further research is needed to assess other potential predictors, to assess the model’s performance in specific subgroups of patients, and to compare it to other models, which have been developed in other countries and have not yet been tested in NZ.Electronic supplementary materialThe online version of this article (10.1186/s12885-018-4791-x) contains supplementary material, which is available to authorized users.