Erkan Bilgin, Ezel Yaltirik Bilgin, Ahmet Bayrak, Sahap Torenek
{"title":"利用CT成像预测乳腺癌患者转移性腋窝淋巴结囊外浸润的先进放射组学。","authors":"Erkan Bilgin, Ezel Yaltirik Bilgin, Ahmet Bayrak, Sahap Torenek","doi":"10.29271/jcpsp.2025.04.415","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>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.</p><p><strong>Study design: </strong>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.</p><p><strong>Methodology: </strong> 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.</p><p><strong>Results: </strong> 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.</p><p><strong>Conclusion: </strong> 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.</p><p><strong>Key words: </strong> Radiomics, Breast cancer, Axillary lymph node involvement, Extracapsular invasion, Computed tomography, Predictive modelling.</p>","PeriodicalId":54905,"journal":{"name":"Jcpsp-Journal of the College of Physicians and Surgeons Pakistan","volume":"35 4","pages":"415-419"},"PeriodicalIF":0.7000,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Advanced Radiomics for Predicting Extracapsular Invasion of Metastatic Axillary Lymph Nodes in Breast Cancer Patients Using CT Imaging.\",\"authors\":\"Erkan Bilgin, Ezel Yaltirik Bilgin, Ahmet Bayrak, Sahap Torenek\",\"doi\":\"10.29271/jcpsp.2025.04.415\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>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.</p><p><strong>Study design: </strong>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.</p><p><strong>Methodology: </strong> 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.</p><p><strong>Results: </strong> 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.</p><p><strong>Conclusion: </strong> 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.</p><p><strong>Key words: </strong> Radiomics, Breast cancer, Axillary lymph node involvement, Extracapsular invasion, Computed tomography, Predictive modelling.</p>\",\"PeriodicalId\":54905,\"journal\":{\"name\":\"Jcpsp-Journal of the College of Physicians and Surgeons Pakistan\",\"volume\":\"35 4\",\"pages\":\"415-419\"},\"PeriodicalIF\":0.7000,\"publicationDate\":\"2025-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Jcpsp-Journal of the College of Physicians and Surgeons Pakistan\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.29271/jcpsp.2025.04.415\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"MEDICINE, GENERAL & INTERNAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Jcpsp-Journal of the College of Physicians and Surgeons Pakistan","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.29271/jcpsp.2025.04.415","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MEDICINE, GENERAL & INTERNAL","Score":null,"Total":0}
Advanced Radiomics for Predicting Extracapsular Invasion of Metastatic Axillary Lymph Nodes in Breast Cancer Patients Using CT Imaging.
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.
期刊介绍：
Journal of College of Physicians and Surgeons Pakistan (JCPSP), is the prestigious, peer reviewed monthly biomedical journal of the country published regularly since 1991.
Established with the primary aim of promotion and dissemination of medical research and contributed by scholars of biomedical sciences from Pakistan and abroad, it carries original research papers, , case reports, review articles, articles on medical education, commentaries, short communication, new technology, editorials and letters to the editor. It covers the core biomedical health science subjects, basic medical sciences and emerging community problems, prepared in accordance with the “Uniform requirements for submission to bio-medical journals” laid down by International Committee of Medical Journals Editors (ICMJE). All publications of JCPSP are peer reviewed by subject specialists from Pakistan and locally and abroad.
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