Improving Spatiotemporal Breast Cancer Assessment and Prediction in Hangzhou City, China

Abstract Breast cancer (BC) is the main cause of death of female cancer patients in China. Mainstream mapping techniques, like spatiotemporal ordinary kriging (STOK), generate disease incidence maps that improve our understanding of disease distribution. Yet, the implementation of these techniques e...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Zhaohan Lou, Xufeng Fei, George Christakos, Jianbo Yan, Jiaping Wu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/1df1b5f7c72c49b89f998747ac967911
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:1df1b5f7c72c49b89f998747ac967911
record_format	dspace
spelling	oai:doaj.org-article:1df1b5f7c72c49b89f998747ac9679112021-12-02T12:32:00ZImproving Spatiotemporal Breast Cancer Assessment and Prediction in Hangzhou City, China10.1038/s41598-017-03524-z2045-2322https://doaj.org/article/1df1b5f7c72c49b89f998747ac9679112017-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-03524-zhttps://doaj.org/toc/2045-2322Abstract Breast cancer (BC) is the main cause of death of female cancer patients in China. Mainstream mapping techniques, like spatiotemporal ordinary kriging (STOK), generate disease incidence maps that improve our understanding of disease distribution. Yet, the implementation of these techniques experiences substantive and technical complications (due mainly to the different characteristics of space and time). A new spatiotemporal projection (STP) technique that is free of the above complications was implemented to model the space-time distribution of BC incidence in Hangzhou city and to estimate incidence values at locations-times for which no BC data exist. For comparison, both the STP and the STOK techniques were used to generate BC incidence maps in Hangzhou. STP performed considerably better than STOK in terms of generating more accurate incidence maps showing a closer similarity to the observed incidence distribution, and providing an improved assessment of the space-time BC correlation structure. In sum, the inter-connections between space, time, BC incidence and spread velocity established by STP allow a more realistic representation of the actual incidence distribution, and generate incidence maps that are more accurate and more informative, at a lower computational cost and involving fewer approximations than the incidence maps produced by mainstream space-time techniques.Zhaohan LouXufeng FeiGeorge ChristakosJianbo YanJiaping WuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-11 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Zhaohan Lou Xufeng Fei George Christakos Jianbo Yan Jiaping Wu Improving Spatiotemporal Breast Cancer Assessment and Prediction in Hangzhou City, China
description	Abstract Breast cancer (BC) is the main cause of death of female cancer patients in China. Mainstream mapping techniques, like spatiotemporal ordinary kriging (STOK), generate disease incidence maps that improve our understanding of disease distribution. Yet, the implementation of these techniques experiences substantive and technical complications (due mainly to the different characteristics of space and time). A new spatiotemporal projection (STP) technique that is free of the above complications was implemented to model the space-time distribution of BC incidence in Hangzhou city and to estimate incidence values at locations-times for which no BC data exist. For comparison, both the STP and the STOK techniques were used to generate BC incidence maps in Hangzhou. STP performed considerably better than STOK in terms of generating more accurate incidence maps showing a closer similarity to the observed incidence distribution, and providing an improved assessment of the space-time BC correlation structure. In sum, the inter-connections between space, time, BC incidence and spread velocity established by STP allow a more realistic representation of the actual incidence distribution, and generate incidence maps that are more accurate and more informative, at a lower computational cost and involving fewer approximations than the incidence maps produced by mainstream space-time techniques.
format	article
author	Zhaohan Lou Xufeng Fei George Christakos Jianbo Yan Jiaping Wu
author_facet	Zhaohan Lou Xufeng Fei George Christakos Jianbo Yan Jiaping Wu
author_sort	Zhaohan Lou
title	Improving Spatiotemporal Breast Cancer Assessment and Prediction in Hangzhou City, China
title_short	Improving Spatiotemporal Breast Cancer Assessment and Prediction in Hangzhou City, China
title_full	Improving Spatiotemporal Breast Cancer Assessment and Prediction in Hangzhou City, China
title_fullStr	Improving Spatiotemporal Breast Cancer Assessment and Prediction in Hangzhou City, China
title_full_unstemmed	Improving Spatiotemporal Breast Cancer Assessment and Prediction in Hangzhou City, China
title_sort	improving spatiotemporal breast cancer assessment and prediction in hangzhou city, china
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/1df1b5f7c72c49b89f998747ac967911
work_keys_str_mv	AT zhaohanlou improvingspatiotemporalbreastcancerassessmentandpredictioninhangzhoucitychina AT xufengfei improvingspatiotemporalbreastcancerassessmentandpredictioninhangzhoucitychina AT georgechristakos improvingspatiotemporalbreastcancerassessmentandpredictioninhangzhoucitychina AT jianboyan improvingspatiotemporalbreastcancerassessmentandpredictioninhangzhoucitychina AT jiapingwu improvingspatiotemporalbreastcancerassessmentandpredictioninhangzhoucitychina
_version_	1718394204360540160

Improving Spatiotemporal Breast Cancer Assessment and Prediction in Hangzhou City, China

Ejemplares similares