An Ultrasound-based Machine Learning Model for Predicting Tumor-Infiltrating Lymphocytes in Breast Cancer
Introduction Tumor-infiltrating lymphocytes (TILs) are key indicators of immune response and prognosis in breast cancer (BC). Accurate prediction of TIL levels is essential for guiding personalized treatment strategies. This study aimed to develop and evaluate machine learning models using ultrasoun...
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2025-04-01
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| Series: | Technology in Cancer Research & Treatment |
| Online Access: | https://doi.org/10.1177/15330338251334453 |
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| author | Boya Liu MM Xiangrong Gu MM Danling Xie MM Bing Zhao BM Dong Han MD Yuli Zhang BM Tao Li PhD Jingqin Fang MD, PhD |
| author_facet | Boya Liu MM Xiangrong Gu MM Danling Xie MM Bing Zhao BM Dong Han MD Yuli Zhang BM Tao Li PhD Jingqin Fang MD, PhD |
| author_sort | Boya Liu MM |
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| description | Introduction Tumor-infiltrating lymphocytes (TILs) are key indicators of immune response and prognosis in breast cancer (BC). Accurate prediction of TIL levels is essential for guiding personalized treatment strategies. This study aimed to develop and evaluate machine learning models using ultrasound-derived radiomics and clinical features to predict TIL levels in BC. Methods This retrospective study included 256 BC patients between January 2019 and August 2023, who were randomly divided into training (n = 179) and test (n = 77) cohorts. Radiomics features were extracted from the intratumor and peritumor regions in ultrasound images. Feature selection was performed using the “Boruta” package in R to iteratively remove non-significant features. Extra Trees Classifier was used to construct radiomics and clinical models. A combined radiomics-clinical (R-C) model was also developed. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and decision curve analysis (DCA) to assess clinical utility. A nomogram was created based on the best-performing model. Results A total of 1712 radiomics features were extracted from the intratumor and peritumor regions. The Boruta method selected five key features (four from the peritumor and one from the intratumor) for model construction. Clinical features, including immunohistochemistry, tumor size, shape, and echo characteristics, showed significant differences between high (≥10%) and low (<10%) TIL groups. Both the R-C and radiomics models outperformed the clinical model in the test cohort (area under the curve values of 0.869/0.838 vs 0.627, P < .05). Calibration curves and Brier scores demonstrated superior accuracy and calibration for the R-C and radiomics models. DCA revealed the highest net benefit of the R-C model at intermediate threshold probabilities. Conclusion Ultrasound-derived radiomics effectively predicts TIL levels in BC, providing valuable insights for personalized treatment and surveillance strategies. |
| format | Article |
| id | doaj-art-3a8e62d02cc741b89e4308d67015e88d |
| institution | OA Journals |
| issn | 1533-0338 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Technology in Cancer Research & Treatment |
| spelling | doaj-art-3a8e62d02cc741b89e4308d67015e88d2025-08-20T01:48:25ZengSAGE PublishingTechnology in Cancer Research & Treatment1533-03382025-04-012410.1177/15330338251334453An Ultrasound-based Machine Learning Model for Predicting Tumor-Infiltrating Lymphocytes in Breast CancerBoya Liu MM0Xiangrong Gu MM1Danling Xie MM2Bing Zhao BM3Dong Han MD4Yuli Zhang BM5Tao Li PhD6Jingqin Fang MD, PhD7 Department of Ultrasound Diagnosis, Wanzhou District First People's Hospital, Chongqing, China Department of Radiology, Daping Hospital, Army Medical University, Chongqing, China Department of Ultrasound Diagnosis, The Second Affiliated Hospital of the Army Medical University, Chongqing, China Department of Ultrasound, Daping Hospital, Army Medical University, Chongqing, China Department of Ultrasound, Daping Hospital, Army Medical University, Chongqing, China Department of Ultrasound, Daping Hospital, Army Medical University, Chongqing, China Department of Ultrasound, Daping Hospital, Army Medical University, Chongqing, China Department of Ultrasound, Daping Hospital, Army Medical University, Chongqing, ChinaIntroduction Tumor-infiltrating lymphocytes (TILs) are key indicators of immune response and prognosis in breast cancer (BC). Accurate prediction of TIL levels is essential for guiding personalized treatment strategies. This study aimed to develop and evaluate machine learning models using ultrasound-derived radiomics and clinical features to predict TIL levels in BC. Methods This retrospective study included 256 BC patients between January 2019 and August 2023, who were randomly divided into training (n = 179) and test (n = 77) cohorts. Radiomics features were extracted from the intratumor and peritumor regions in ultrasound images. Feature selection was performed using the “Boruta” package in R to iteratively remove non-significant features. Extra Trees Classifier was used to construct radiomics and clinical models. A combined radiomics-clinical (R-C) model was also developed. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and decision curve analysis (DCA) to assess clinical utility. A nomogram was created based on the best-performing model. Results A total of 1712 radiomics features were extracted from the intratumor and peritumor regions. The Boruta method selected five key features (four from the peritumor and one from the intratumor) for model construction. Clinical features, including immunohistochemistry, tumor size, shape, and echo characteristics, showed significant differences between high (≥10%) and low (<10%) TIL groups. Both the R-C and radiomics models outperformed the clinical model in the test cohort (area under the curve values of 0.869/0.838 vs 0.627, P < .05). Calibration curves and Brier scores demonstrated superior accuracy and calibration for the R-C and radiomics models. DCA revealed the highest net benefit of the R-C model at intermediate threshold probabilities. Conclusion Ultrasound-derived radiomics effectively predicts TIL levels in BC, providing valuable insights for personalized treatment and surveillance strategies.https://doi.org/10.1177/15330338251334453 |
| spellingShingle | Boya Liu MM Xiangrong Gu MM Danling Xie MM Bing Zhao BM Dong Han MD Yuli Zhang BM Tao Li PhD Jingqin Fang MD, PhD An Ultrasound-based Machine Learning Model for Predicting Tumor-Infiltrating Lymphocytes in Breast Cancer Technology in Cancer Research & Treatment |
| title | An Ultrasound-based Machine Learning Model for Predicting Tumor-Infiltrating Lymphocytes in Breast Cancer |
| title_full | An Ultrasound-based Machine Learning Model for Predicting Tumor-Infiltrating Lymphocytes in Breast Cancer |
| title_fullStr | An Ultrasound-based Machine Learning Model for Predicting Tumor-Infiltrating Lymphocytes in Breast Cancer |
| title_full_unstemmed | An Ultrasound-based Machine Learning Model for Predicting Tumor-Infiltrating Lymphocytes in Breast Cancer |
| title_short | An Ultrasound-based Machine Learning Model for Predicting Tumor-Infiltrating Lymphocytes in Breast Cancer |
| title_sort | ultrasound based machine learning model for predicting tumor infiltrating lymphocytes in breast cancer |
| url | https://doi.org/10.1177/15330338251334453 |
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