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Related Experiment Video

Updated: Sep 26, 2025

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
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Data-driven treatment pathways mining for early breast cancer using cSPADE algorithm and system clustering.

Qing Yang1, Ting Luo2, Wei Zhang3

  • 1Institute of Hospital Management, West China Hospital, Sichuan University, Chengdu, China.

The International Journal of Health Planning and Management
|April 21, 2022
PubMed
Summary
This summary is machine-generated.

This study successfully identified early breast cancer treatment pathways using the cSPADE algorithm and system clustering. The findings reveal robust treatment sequences, aiding in the optimization of cancer care models.

Keywords:
breast cancerclinical pathwaycluster analysisdata miningsequential pattern mining

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Area of Science:

  • Oncology
  • Data Science
  • Bioinformatics

Background:

  • Extracting cancer treatment pathways is complex due to data's multidimensional and chronological nature.
  • Identifying effective treatment sequences is crucial for optimizing early breast cancer patient care.

Purpose of the Study:

  • To evaluate the efficacy of the cSPADE algorithm combined with system clustering for identifying early breast cancer treatment pathways.
  • To determine if this data mining approach can effectively mine temporal relationships in treatment modalities.

Main Methods:

  • Applied data mining to electronic medical records of 6891 early breast cancer patients.
  • Employed a three-stage mining process: cSPADE for treatment stage determination, system clustering for plan extraction, and cSPADE for sequence pattern mining.
  • Utilized Kolmogorov-Smirnov test and correlation analysis for cross-validation of treatment pathway sequence rules.

Main Results:

  • Discovered 55 sequence rules for early breast cancer treatment, including specific chemotherapy regimens for neoadjuvant, postoperative, and non-surgical cases.
  • Cross-validation via 5-fold, Pearson, and Spearman tests showed high correlation coefficients (>0.89) at p < 0.05.
  • Kolmogorov-Smirnov tests indicated no significant differences in sequence distributions, confirming robustness.

Conclusions:

  • The cSPADE algorithm and system clustering effectively identify temporal relationships in breast cancer treatment modalities.
  • This method enables hierarchical and vertical mining of breast cancer treatment models, revealing real-world treatment behaviors.
  • The validated treatment pathway rules provide a valuable reference for optimizing early breast cancer care pathways.