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Review
. 2024 Feb 25;14(1):4566.
doi: 10.1038/s41598-024-55230-2.

Comparing the current short-term cancer incidence prediction models in Brazil with state-of-the-art time-series models

Daniel Bouzon Nagem Assad  1   2 , Patricia Gomes Ferreira da Costa  3 , Thaís Spiegel  3 , Javier Cara  4 , Miguel Ortega-Mier  4 , Alfredo Monteiro Scaff  5
Affiliations
Review

Comparing the current short-term cancer incidence prediction models in Brazil with state-of-the-art time-series models

Daniel Bouzon Nagem Assad et al. Sci Rep. .

Abstract

The World Health Organization has highlighted that cancer was the second-highest cause of death in 2019. This research aims to present the current forecasting techniques found in the literature, applied to predict time-series cancer incidence and then, compare these results with the current methodology adopted by the Instituto Nacional do Câncer (INCA) in Brazil. A set of univariate time-series approaches is proposed to aid decision-makers in monitoring and organizing cancer prevention and control actions. Additionally, this can guide oncological research towards more accurate estimates that align with the expected demand. Forecasting techniques were applied to real data from seven types of cancer in a Brazilian district. Each method was evaluated by comparing its fit with real data using the root mean square error, and we also assessed the quality of noise to identify biased models. Notably, three methods proposed in this research have never been applied to cancer prediction before. The data were collected from the INCA website, and the forecast methods were implemented using the R language. Conducting a literature review, it was possible to draw comparisons previous works worldwide to illustrate that cancer prediction is often focused on breast and lung cancers, typically utilizing a limited number of time-series models to find the best fit for each case. Additionally, in comparison to the current method applied in Brazil, it has been shown that employing more generalized forecast techniques can provide more reliable predictions. By evaluating the noise in the current method, this research shown that the existing prediction model is biased toward two of the studied cancers Comparing error results between the mentioned approaches and the current technique, it has been shown that the current method applied by INCA underperforms in six out of seven types of cancer tested. Moreover, this research identified that the current method can produce a biased prediction for two of the seven cancers evaluated. Therefore, it is suggested that the methods evaluated in this work should be integrated into the INCA cancer forecast methodology to provide reliable predictions for Brazilian healthcare professionals, decision-makers, and oncological researchers.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
ICD-10 Mortality rate by 100,000 inhabitants considering world population-adjusted by cancer type.
Figure 2
Figure 2
Hyndman and Athanasopoulos ETS equations.
Figure 3
Figure 3
ICD-10 Incidence rate-ajusted (IRa) by cancer type.
Figure 4
Figure 4
All cancer types noise evaluation using INCA’s current model.
Figure 5
Figure 5
Breast cancer noise evaluation by model.
Figure 6
Figure 6
Colorectal cancer noise evaluation by model.
Figure 7
Figure 7
Prostate cancer noise evaluation by model.
Figure 8
Figure 8
Lung cancer noise evaluation by model.
Figure 9
Figure 9
Cervical cancer noise evaluation by model.
Figure 10
Figure 10
Head and Neck cancer noise evaluation by model.
Figure 11
Figure 11
Childhood cancer noise evaluation by model.
Figure 12
Figure 12
NNAR breast cancer IRa prediction values.
Figure 13
Figure 13
KF colorectal cancer IRa fitted and prediction values.
Figure 14
Figure 14
ARIMA prostate cancer IRa fitted and prediction values.
Figure 15
Figure 15
TBATS lung cancer IRa fitted and prediction values.
Figure 16
Figure 16
KF cervical cancer IRa fitted and prediction.
Figure 17
Figure 17
Current method head and neck cancer IRa fitted and prediction values.
Figure 18
Figure 18
KF Childhood cancer IRa fitted and prediction values.
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