The performance of the Nottingham Prognosis Index and the adjuvant online decision making tool for prognosis in early-stage breast cancer patients

Rejali, Mehri; Tazhibi, Mehdi; Mokarian, Fariborz; Gharanjik, Nazjamal; Mokarian, Reyhane

doi:10.4103/2008-7802.166503

Cited by 12 publications

(9 citation statements)

References 20 publications

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“…Jung et al (2013) [44]LowModerateModerateLow22Kindts et al (2016) [135]ModerateModerateModerateLow23Kollias et al (1999) [31]ModerateModerateLowModerate24Kuo et al (2012) [62]LowLowModerateLow25Laas et al (2015) [68]ModerateModerateModerateLow26Lende et al (2010) [136]LowModerateLowLow27M. Liu et al (2010) [74]LowModerateLowLow28Maishman et al (2015) [137]ModerateModerateLowLow29Megha et al (2010) [70]ModerateModerateLowLow30Miao et al (2016) [138]ModerateHighLowLow31Michaelson et al (2011) [42]LowHighLowModerate32Mojir Sheibani et al (2013) [65]LowLowModerateLow33Mook et al (2009) [64]LowModerateModerateLow34Okugawa et al (2009) [52]LowLowLowLow35Olivotto et al (2005) [45]LowHighLowLow36Plakhins et al (2013) [63]ModerateModerateLowLow37Quintyne et al (2013) [60]LowModerateModerateLow38Rejali et al (2015) [54]Moderate…”

Section: Resultsmentioning

confidence: 99%

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Prognostic models for breast cancer: a systematic review

2019

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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.

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Section: Resultsmentioning

confidence: 99%

“…At the outset, a patient will be classified into one of three prognostic groups based on their NPI score: good prognostic group (PI< 3.4), moderate prognostic group (3.4 ≤ PI≤5.4), and poor prognostic group (PI> 5.4) [ 53 ]. Some validation studies of the NPI further divided the samples into six smaller prognostic groups [ 47 , 54 ].…”

Section: Resultsmentioning

confidence: 99%

Prognostic models for breast cancer: a systematic review

2019

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“…The average times to censor were 9.2 (4.8 SDs) and 9.5 (4.9) years, for each set, respectively. The resulting Bayesian model and data from METABRIC were used to integrate COVs (including age at the moment of diagnosis (AGE), CS, type of carcinoma (TC), the NPI, 32 and treatment), whole-genome GE profiles, copy number variants (CNVs), and interactions between these omics and treatments. The NPI consists of a well-validated prognostic score that takes information from tumor size, grade, and nodal involvement, specifically NPI= (0.417 × size)+(0.76 × lymph-node stage)+(0–82 × tumor grade), typically ranging between 2 and 7; 33 the higher the score the shorter the lifespan prognosis for the patient.…”

Section: Methodsmentioning

confidence: 99%

Prediction of years of life after diagnosis of breast cancer using omics and omic-by-treatment interactions

et al. 2017

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Breast cancer (BC) is the second most common type of cancer and a major cause of death for women. Commonly, BC patients are assigned to risk groups based on the combination of prognostic and prediction factors (eg, patient age, tumor size, tumor grade, hormone receptor status, etc). Although this approach is able to identify risk groups with different prognosis, patients are highly heterogeneous in their response to treatments. To improve the prediction of BC patients, we extended clinical models (including prognostic and prediction factors with whole-omic data) to integrate omics profiles for gene expression and copy number variants (CNVs). We describe a modeling framework that is able to incorporate clinical risk factors, high-dimensional omics profiles, and interactions between omics and non-omic factors (eg, treatment). We used the proposed modeling framework and data from METABRIC (Molecular Taxonomy of Breast Cancer Consortium) to assess the impact on the accuracy of BC patient survival predictions when omics and omic-by-treatment interactions are being considered. Our analysis shows that omics and omic-by-treatment interactions explain a sizable fraction of the variance on survival time that is not explained by commonly used clinical covariates. The sizable interaction effects observed, together with the increase in prediction accuracy, suggest that whole-omic profiles could be used to improve prognosis prediction among BC patients.

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“…The earliest data reported in the published studies came from 1970 [ 42 ] and most recent data from 2014 [ 43 ] (see Fig. 2 ).…”

Section: Resultsmentioning

confidence: 99%

“…One study [ 2 ] compared two datasets: from Finland and the USA. Single studies reported data from each of Canada [ 42 ], Japan [ 35 ], South Korea [ 32 ], Hong Kong [ 27 ] and Iran [ 43 ]. Study reporting quality was generally assessed as satisfactory, and risk of bias in relation to the study survival estimates was considered to be low for all included studies (see Additional file 1 : Appendix 3).…”

Section: Resultsmentioning

confidence: 99%

Survival estimates stratified by the Nottingham Prognostic Index for early breast cancer: a systematic review and meta-analysis of observational studies

et al. 2018

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BackgroundEstimates of survival for women diagnosed with early staged breast cancer are available based on stratification into prognostic categories defined using the Nottingham Prognostic Index (NPI). This review aimed to identify and summarize the estimated survival statistics from separate sources in the literature and to explore the extent of between-study heterogeneity in survival estimates.MethodsObservational studies in women diagnosed with early and locally advanced breast cancer reporting overall survival by NPI category were identified using a systematic literature search. An exploratory meta-analysis was conducted to describe survival estimates and assess between-study heterogeneity.ResultsTwenty-eight studies were identified. Nineteen studies with sufficient data on overall survival were included in meta-analysis. A high level of heterogeneity in survival estimates was evident with I2 values in the range of 90 to 98%.ConclusionsThe substantial differences between studies in the relationship between NPI categories and survival at 5 and 10 years poses challenges for use of this prognostic score in both clinical settings and in decision-analytic model-based economic evaluations.Electronic supplementary materialThe online version of this article (10.1186/s13643-018-0803-9) contains supplementary material, which is available to authorized users.

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The performance of the Nottingham Prognosis Index and the adjuvant online decision making tool for prognosis in early-stage breast cancer patients

Cited by 12 publications

References 20 publications

Prognostic models for breast cancer: a systematic review

Prognostic models for breast cancer: a systematic review

Prediction of years of life after diagnosis of breast cancer using omics and omic-by-treatment interactions

Survival estimates stratified by the Nottingham Prognostic Index for early breast cancer: a systematic review and meta-analysis of observational studies

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