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Spatiotemporal Co-existence of Female Thyroid and Breast Cancers in Hangzhou, China.

Fei X, Christakos G, Lou Z, Ren Y, Liu Q, Wu J - Sci Rep (2016)

Bottom Line: The spatiotemporal co-existence of TC and BC distributions was investigated using the integrative disease predictability (IDP) criterion: if TC-BC association is part of the disease mapping knowledge bases, it should yield improved space-time incidence predictions.Improved TC (BC) incidence predictions were generated when integrating both TC and BC data than when using only TC (BC) data.The strength of TC-BC association was measured by the IDP coefficients and incidence prediction accuracy.

View Article: PubMed Central - PubMed

Affiliation: College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, China.

ABSTRACT
Thyroid and breast cancers (TC, BC) are common female malignant tumors worldwide. Studies suggest that TC patients have a higher BC risk, and vice versa. However, it has not been investigated quantitatively if there is an association between the space-time TC and BC incidence distributions at the population level. This work aims to answer this question. 5358 TC and 8784 BC (female) cases were diagnosed in Hangzhou (China, 2008-2012). Pearson and Spearman rank correlation coefficients of the TC and BC incidences were high, and their patterns were geographically similar. The spatiotemporal co-existence of TC and BC distributions was investigated using the integrative disease predictability (IDP) criterion: if TC-BC association is part of the disease mapping knowledge bases, it should yield improved space-time incidence predictions. Improved TC (BC) incidence predictions were generated when integrating both TC and BC data than when using only TC (BC) data. IDP consistently demonstrated the spatiotemporal co-existence of TC and BC distributions throughout Hangzhou (2008-2012), which means that when the population experiences high incidences of one kind of cancer attention should be paid to the other kind of cancer too. The strength of TC-BC association was measured by the IDP coefficients and incidence prediction accuracy.

No MeSH data available.


Related in: MedlinePlus

The distribution of prediction errors through Bayesian maximum entropy techniques.(a) only TC incidence were used as hard data to predict TC incidence, (b) TC incidence were used as hard data and BC incidence were used as soft data to predict TC incidence, (c) only BC incidence were used as hard data to predict BC incidence and (d) BC incidence were used as hard data and TC incidence were used as soft data to predict BC incidence.
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f4: The distribution of prediction errors through Bayesian maximum entropy techniques.(a) only TC incidence were used as hard data to predict TC incidence, (b) TC incidence were used as hard data and BC incidence were used as soft data to predict TC incidence, (c) only BC incidence were used as hard data to predict BC incidence and (d) BC incidence were used as hard data and TC incidence were used as soft data to predict BC incidence.

Mentions: Figure 4 displays the frequency graphs of incidence prediction errors derived by the BME technique in the following cases: (a) only TC incidence values were used as hard data to predict TC incidence; (b) TC incidence were used as hard data and BC incidence information was used as soft data to predict TC incidence; (c) only BC incidence values were used as hard data to predict BC incidence; and (d) BC incidence values were used as hard data and TC incidence information was used as soft data to predict BC incidence.


Spatiotemporal Co-existence of Female Thyroid and Breast Cancers in Hangzhou, China.

Fei X, Christakos G, Lou Z, Ren Y, Liu Q, Wu J - Sci Rep (2016)

The distribution of prediction errors through Bayesian maximum entropy techniques.(a) only TC incidence were used as hard data to predict TC incidence, (b) TC incidence were used as hard data and BC incidence were used as soft data to predict TC incidence, (c) only BC incidence were used as hard data to predict BC incidence and (d) BC incidence were used as hard data and TC incidence were used as soft data to predict BC incidence.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4920092&req=5

f4: The distribution of prediction errors through Bayesian maximum entropy techniques.(a) only TC incidence were used as hard data to predict TC incidence, (b) TC incidence were used as hard data and BC incidence were used as soft data to predict TC incidence, (c) only BC incidence were used as hard data to predict BC incidence and (d) BC incidence were used as hard data and TC incidence were used as soft data to predict BC incidence.
Mentions: Figure 4 displays the frequency graphs of incidence prediction errors derived by the BME technique in the following cases: (a) only TC incidence values were used as hard data to predict TC incidence; (b) TC incidence were used as hard data and BC incidence information was used as soft data to predict TC incidence; (c) only BC incidence values were used as hard data to predict BC incidence; and (d) BC incidence values were used as hard data and TC incidence information was used as soft data to predict BC incidence.

Bottom Line: The spatiotemporal co-existence of TC and BC distributions was investigated using the integrative disease predictability (IDP) criterion: if TC-BC association is part of the disease mapping knowledge bases, it should yield improved space-time incidence predictions.Improved TC (BC) incidence predictions were generated when integrating both TC and BC data than when using only TC (BC) data.The strength of TC-BC association was measured by the IDP coefficients and incidence prediction accuracy.

View Article: PubMed Central - PubMed

Affiliation: College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, China.

ABSTRACT
Thyroid and breast cancers (TC, BC) are common female malignant tumors worldwide. Studies suggest that TC patients have a higher BC risk, and vice versa. However, it has not been investigated quantitatively if there is an association between the space-time TC and BC incidence distributions at the population level. This work aims to answer this question. 5358 TC and 8784 BC (female) cases were diagnosed in Hangzhou (China, 2008-2012). Pearson and Spearman rank correlation coefficients of the TC and BC incidences were high, and their patterns were geographically similar. The spatiotemporal co-existence of TC and BC distributions was investigated using the integrative disease predictability (IDP) criterion: if TC-BC association is part of the disease mapping knowledge bases, it should yield improved space-time incidence predictions. Improved TC (BC) incidence predictions were generated when integrating both TC and BC data than when using only TC (BC) data. IDP consistently demonstrated the spatiotemporal co-existence of TC and BC distributions throughout Hangzhou (2008-2012), which means that when the population experiences high incidences of one kind of cancer attention should be paid to the other kind of cancer too. The strength of TC-BC association was measured by the IDP coefficients and incidence prediction accuracy.

No MeSH data available.


Related in: MedlinePlus