Volume 26, Issue 4 (2023)
mjms 2023, 26(4): 7-15 |
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Jahangiri M, Kazemnejad A. Classifying Breast Tumors as Malignant or Benign Using Digitized Images of Fine Needle Aspiration Samples of Breast Mass Tissue: An Application of Classification Tree Algorithms. mjms 2023; 26 (4) :7-15
URL: http://mjms.modares.ac.ir/article-30-77391-en.html
URL: http://mjms.modares.ac.ir/article-30-77391-en.html
1- Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University,Tehran, Iran
2- Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University,Tehran, Iran ,kazem_an@modares.ac.ir
2- Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University,Tehran, Iran ,
Abstract: (1286 Views)
Introduction: Breast cancer represents a major public health issue worldwide, highlighting the critical role of early detection in facilitating effective treatment. Fine needle aspiration (FNA) serves as a minimally invasive method for obtaining cellular material from breast masses for subsequent analysis. Nonetheless, pathologists' assessment of FNA samples may be characterized by subjectivity and protracted evaluation times, leading to variability in diagnostic results. Integrating machine learning algorithms, including classification tree models, can potentially improve the consistency and precision of breast tumor classification. Using computational capabilities and sophisticated machine learning methodologies, these models can proficiently categorize digitized images of FNA samples as malignant or benign.
Methods: We used classification tree algorithms such as CART, Ctree, Evtree, QUEST, CRUISE, and GUIDE to distinguish between malignant and benign tumors in the Wisconsin Breast Cancer Dataset (WBCD). The models' performance was evaluated using accuracy metrics, such as sensitivity, specificity, false positive and negative rates, positive and negative predictive values, Youden's Index, accuracy, positive and negative likelihood ratios, diagnostic odds ratios, and AUC (area under the ROC curve).
Results: The results showed that the CRUISE algorithm showed excellent diagnostic performance in distinguishing between malignant and benign tumors.
Conclusion: The results emphasize the critical role of integrating machine learning models into clinical practice to assist pathologists, improve diagnostic outcomes, and reduce subjectivity in cancer classification.
Methods: We used classification tree algorithms such as CART, Ctree, Evtree, QUEST, CRUISE, and GUIDE to distinguish between malignant and benign tumors in the Wisconsin Breast Cancer Dataset (WBCD). The models' performance was evaluated using accuracy metrics, such as sensitivity, specificity, false positive and negative rates, positive and negative predictive values, Youden's Index, accuracy, positive and negative likelihood ratios, diagnostic odds ratios, and AUC (area under the ROC curve).
Results: The results showed that the CRUISE algorithm showed excellent diagnostic performance in distinguishing between malignant and benign tumors.
Conclusion: The results emphasize the critical role of integrating machine learning models into clinical practice to assist pathologists, improve diagnostic outcomes, and reduce subjectivity in cancer classification.
Keywords: Breast cancer, benign tumor, malignant tumor, prediction, diagnostic scheme, classification trees
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