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Related Experiment Video

Updated: Mar 8, 2026

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
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On Cluster Structures of Finnish Cancer Incidence Data.

Tommi Huhtinen1, Milla Laurikkala1, Sirpa Heinävaara2

  • 1Department of Mathematics and Systems Analysis, School of Science, Aalto University, Espoo, Finland.

Cancer Control : Journal of the Moffitt Cancer Center
|March 7, 2026
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Summary

This study analyzed Finnish cancer incidence trends from 1963-2023 using clustering. Key cancer types, like breast, cervical, and prostate, often formed distinct clusters, suggesting unique epidemiological patterns.

Keywords:
agglomerative hierarchical clusteringcancer incidenceincidence patternrisk factorwestern lifestyle

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • Global cancer burden is rising, influenced by population aging and lifestyle factors.
  • Cancer incidence rates are dynamic, affected by aging, lifestyle, and diagnostic changes.
  • Understanding temporal trends in cancer incidence is crucial for public health strategies.

Purpose of the Study:

  • To identify and analyze cluster structures in Finnish cancer incidence data from 1963 to 2023.
  • To investigate similarities and dissimilarities in cancer incidence trends over time.
  • To gain insights into Finnish cancer epidemiology through cluster analysis.

Main Methods:

  • Utilized a proximity measure based on curve shape for trend analysis.
  • Employed agglomerative hierarchical clustering with the average linkage method.
  • Analyzed cancer incidence data stratified by age and sex for 12 subgroups.

Main Results:

  • Identified distinct cluster structures for 12 age and sex subgroups.
  • Cancers with national screening (breast, cervical) or testing (prostate) often formed separate clusters.
  • Melanoma and lung/tracheal cancers also frequently separated, potentially due to lifestyle factors.

Conclusions:

  • The proposed proximity measure is effective for analyzing cancer incidence trends.
  • Cluster analysis provides valuable insights into Finnish cancer epidemiology.
  • Identified clusters highlight potential links between screening, testing, lifestyle, and cancer trends.