Predicting future cancer burden in the United States by artificial neural networks.
Future Oncol
; 17(2): 159-168, 2021 Jan.
Article
in En
| MEDLINE
| ID: mdl-33305617
ABSTRACT
Aims:
To capture the complex relationships between risk factors and cancer incidences in the US and predict future cancer burden. Materials &methods:
Two artificial neural network (ANN) algorithms were adopted a multilayer feed-forward network (MLFFNN) and a nonlinear autoregressive network with eXogenous inputs (NARX). Data on the incidence of the four most common tumors (breast, colorectal, lung and prostate) from 1992 to 2016 (available from National Cancer Institute online datasets) were used for training and validation, and data until 2050 were predicted.Results:
The rapid decreasing trend of prostate cancer incidence started in 2010 will continue until 2018-2019; it will then slow down and reach a plateau after 2050, with several differences among ethnicities. The incidence of breast cancer will reach a plateau in 2030, whereas colorectal cancer incidence will reach a minimum value of 35 per 100,000 in 2030. As for lung cancer, the incidence will decrease from 50 per 100,000 (2017) to 31 per 100,000 in 2030 and 26 per 100,000 in 2050.Conclusion:
This up-to-date prediction of cancer burden in the US could be a crucial resource for planning and evaluation of cancer-control programs.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Neoplasms
Type of study:
Incidence_studies
/
Prognostic_studies
/
Risk_factors_studies
Limits:
Humans
Country/Region as subject:
America do norte
Language:
En
Journal:
Future Oncol
Year:
2021
Type:
Article
Affiliation country:
Italy