Advanced Radiomics for Predicting Extracapsular Invasion of Metastatic Axillary Lymph Nodes in Breast Cancer Patients Using CT Imaging | [Journal of the College of Physicians and Surgeons Pakistan • 2025]

Author(s):

1. Erkan Bilgin: Department of Radiology, Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital, Yenimahalle, Ankara, Turkiye

2. Ezel Yaltirik Bilgin: Department of Radiology, Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital, Yenimahalle, Ankara, Turkiye

3. Ahmet Bayrak: Department of Radiology, Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital, Yenimahalle, Ankara, Turkiye

4. Sahap Torenek: Department of Radiology, Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital, Yenimahalle, Ankara, Turkiye

Abstract:

To evaluate the e cacy of radiomics features extracted from computed tomography (CT) images in predicting extracapsular invasion (ECI) of metastatic axillary lymph nodes in breast cancer patients. Study Design: Observational study. Place and Duration of the Study: Department of Radiology, Dr. Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital, Yenimahalle, Ankara, Turkiye, from January 2019 to 2024. Methodology: Female patients diagnosed with breast cancer and axillary lymph node involvement were retrospectively reviewed. Highdimensional radiomics features were extracted from CT images, including morphology, histogram, gray level co-occurrence matrix (GLCM), gray level run length matrix (GLRLM), neighbouring gray tone di erence matrix (NGTDM), and gray level size zone matrix (GLSZM) features. Advanced statistical methods, including the Mann-Whitney U test, LASSO, and ANOVA, were employed to identify signi cant predictors of ECI. Logistic regression models were developed, and their performance was evaluated using ROC curve analysis. Results: The study identi ed 39 radiomics features signi cantly associated with ECI (p <0.05). Integrating multiple radiomics features, the combined model demonstrated adequate diagnostic performance. The model explained 57.8% of the variance in ECI status according to the Nagelkerke R-square statistic. Individual feature models' predictive power was lower than the combined model. Conclusion: Radiomics features derived from CT images provide a powerful non-invasive tool for predicting ECI in metastatic axillary lymph nodes due to breast cancer. The combined model's superior performance underscores the importance of a multifaceted approach in medical imaging analysis. These ndings highlight the potential for radiomics to enhance prognostic assessments and guide personalised treatment strategies in breast cancer management.

Page(s): 415-419

DOI: 10.29271/jcpsp.2025.04.415

Published: Journal: Journal of the College of Physicians and Surgeons Pakistan, Volume: 35, Issue: 04, Year: 2025

Keywords:

Computed tomography , Breast Cancer , Axillary lymph node involvement , Radiomics , Extracapsular invasion , Predictive modelling

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