Document Type : Original Research Paper

Authors

• O. Dehghantanha ¹
• N. Mehrshad ¹
• R. Bakhshali ²
• A.R. Sebzari ³

¹ Department of Electrical and Computer Engineering, University of Birjand, Birjand, Iran.

² Omid Cancer Center, Ahvaz, Iran.

³ Department of Internal Medicine, School of Medicine, Cellular and Molecular Research Center, Valiasr Hospital, Birjand University of Medical Sciences, Birjand, Iran.

Abstract

Background and Objectives: Understanding the heterogeneity of breast cancer is crucial for improving treatment strategies. This study investigates the application of K-Means and Hierarchical Clustering to a local dataset of breast cancer patients from Iranmehr Hospital, Birjand, Iran, with the primary goal of identifying potential patient subgroups based on their clinical and treatment characteristics for knowledge discovery. The potential of these subgroups to inform future research on personalized treatment approaches is explored.
Methods: A retrospective dataset comprising pathological and clinical information was analyzed using K-Means and Agglomerative Hierarchical Clustering to identify patient subgroups. The optimal number of clusters was consistently determined to be two (k=2) for both methods based on rigorous internal validation metrics (Elbow Method, Silhouette Analysis, Calinski-Harabasz Index, and Largest Jump Analysis for Hierarchical Clustering). Statistical tests (ANOVA and Chi-squared) were employed to assess significant differences in features across the identified clusters from both K-Means and Hierarchical analyses, providing insights into the key factors differentiating these groups. Internal cluster validity was assessed using Silhouette Score and Calinski-Harabasz Index.
Results: The K-Means analysis identified two clusters exhibiting significant differences in characteristics such as age, chemotherapy session intensity, menopausal status, nodal involvement, and biomarker expression (ER, PR, HER2, Ki67). The Hierarchical Clustering also yielded two clusters with varying characteristics, and a comparison between the two methods highlighted both similarities and differences in the identified patient stratifications. The overall agreement between K-Means and Hierarchical Clustering was quantified by an Adjusted Rand Index (ARI) of 0.4697.
Conclusion: Both K-Means and Hierarchical Clustering effectively revealed potential patient subgroups within the studied dataset, highlighting the heterogeneity of breast cancer presentation and treatment at a local level These clusters exhibited statistically significant differences across key clinical and treatment features. Future research is needed to validate these findings in larger, multi-center studies, explore the clinical significance of these subgroups in terms of treatment outcomes, and compare the effectiveness of different clustering methodologies for this purpose.

Keywords

• Breast Cancer
• Knowledge Discovery
• Clustering
• K-Means Clustering
• Hierarchical Clustering

Main Subjects

• Artificial Intelligence

Open Access

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Publisher

Shahid Rajaee Teacher Training University