2018
DOI: 10.1186/s12885-018-4791-x
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Development and validation of a new predictive model for breast cancer survival in New Zealand and comparison to the Nottingham prognostic index

Abstract: 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 … Show more

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Cited by 15 publications
(4 citation statements)
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References 53 publications
(65 reference statements)
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“…We adapted WGCNA to identify the five screened genes from SE-associated genes that were crucial hub genes of osteosarcoma. Prognostic models based on some cancer-related genes (Long et al, 2018;Zuo et al, 2019), long non-coding RNAs (Liu et al, 2020;Yang et al, 2020;, or other biomarkers (Park et al, 2017;Elwood et al, 2018) for improving patient prognosis attract considerable attention. For instance, Zhu et al (2020) constructed a seven-gene signature associated with osteosarcoma energy metabolism for predicting the prognosis of osteosarcoma through WGCNA and Lasso Cox regression analysis.…”
Section: Introductionmentioning
confidence: 99%
“…We adapted WGCNA to identify the five screened genes from SE-associated genes that were crucial hub genes of osteosarcoma. Prognostic models based on some cancer-related genes (Long et al, 2018;Zuo et al, 2019), long non-coding RNAs (Liu et al, 2020;Yang et al, 2020;, or other biomarkers (Park et al, 2017;Elwood et al, 2018) for improving patient prognosis attract considerable attention. For instance, Zhu et al (2020) constructed a seven-gene signature associated with osteosarcoma energy metabolism for predicting the prognosis of osteosarcoma through WGCNA and Lasso Cox regression analysis.…”
Section: Introductionmentioning
confidence: 99%
“…There has been a growing inclination in recent literature toward the use of FDG-PET/CT as a non-invasive hybrid imaging tool for forecasting treatment response in women with newly diagnosed breast cancer, especially in the context of neoadjuvant systemic therapy [21][22][23][24][25][26][27][28][29][30][31][32][33][34]. A vast majority of the existing models for treatment response prediction in breast cancer have traditionally leaned toward linear statistical methodologies, notably the Cox proportional hazards analysis.…”
Section: Discussionmentioning
confidence: 99%
“…Recent investigations have spotlighted the utility of baseline FDG-PET/CT for predicting outcomes in melanoma and non-small-cell lung cancer (NSCLC) patients [18][19][20]. In the context of breast cancer, there has also been a rising interest in recent literature in using FDG-PET/CT as hybrid imaging to predict response to treatment, particularly in the context of neoadjuvant systemic therapy [21][22][23][24][25][26][27][28][29][30][31][32][33][34]. Despite a significant heterogeneity of variables defined as outcomes, most of the published models predicting treatment response in breast cancer have predominantly relied on linear statistical approaches, such as Cox proportional hazards analysis.…”
Section: Introductionmentioning
confidence: 99%
“…Second, we did not include the effect of treatment in our model. Previous studies have suggested that treatment (e.g., radiotherapy alone versus chemoradiotherapy) should not be included in prediction models as a covariate unless the treatment data are derived from randomized clinical trials; otherwise, it may introduce bias (e.g., multicollinearity), as treatments are highly dependent on patient clinical characteristics in real-world clinical practice [17,[48][49][50][51][52]. Given this information, we excluded treatment in our models.…”
Section: Discussionmentioning
confidence: 99%