Advanced Radiomics for Predicting Extracapsular Invasion of Metastatic Axillary Lymph Nodes in Breast Cancer Patients Using CT Imaging
Year: 2025
Type: article
Study design: Observational Study
Abstract: OBJECTIVE: To evaluate the efficacy 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. High- dimensional 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 difference 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 significant predictors of ECI. Logistic regression models were developed, and their performance was evaluated using ROC curve analysis. RESULTS: The study identified 39 radiomics features significantly 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 findings highlight the potential for radiomics to enhance prognostic assessments and guide personalised treatment strategies in breast cancer management. KEY WORDS: Radiomics, Breast cancer, Axillary lymph node involvement, Extracapsular invasion, Computed tomography, Predictive modelling.
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4409512933