Assessment of Tumor Infiltrating Lymphocytes in Predicting Stereotactic Ablative Radiotherapy (SABR) Response in Unresectable Breast Cancer
Background: Patients with advanced breast cancer (BC) may be treated with stereotactic ablative radiotherapy (SABR) for tumor control. Variable treatment responses are a clinical challenge and there is a need to predict tumor radiosensitivity a priori. There is evidence showing that tumor infiltrati...
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MDPI AG
2025-04-01
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| Series: | Radiation |
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| Online Access: | https://www.mdpi.com/2673-592X/5/2/11 |
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| author | Mateusz Bielecki Khadijeh Saednia Fang-I Lu Shely Kagan Danny Vesprini Katarzyna J. Jerzak Roberto Salgado Raffi Karshafian William T. Tran |
| author_facet | Mateusz Bielecki Khadijeh Saednia Fang-I Lu Shely Kagan Danny Vesprini Katarzyna J. Jerzak Roberto Salgado Raffi Karshafian William T. Tran |
| author_sort | Mateusz Bielecki |
| collection | DOAJ |
| description | Background: Patients with advanced breast cancer (BC) may be treated with stereotactic ablative radiotherapy (SABR) for tumor control. Variable treatment responses are a clinical challenge and there is a need to predict tumor radiosensitivity a priori. There is evidence showing that tumor infiltrating lymphocytes (TILs) are markers for chemotherapy response; however, this association has not yet been validated in breast radiation therapy. This pilot study investigates the computational analysis of TILs to predict SABR response in patients with inoperable BC. Methods: Patients with inoperable breast cancer (<i>n</i> = 22) were included for analysis and classified into partial response (<i>n</i> = 12) and stable disease (<i>n</i> = 10) groups. Pre-treatment tumor biopsies (<i>n</i> = 104) were prepared, digitally imaged, and underwent computational analysis. Whole slide images (WSIs) were pre-processed, and then a pre-trained convolutional neural network model (CNN) was employed to identify the regions of interest. The TILs were annotated, and spatial graph features were extracted. The clinical and spatial features were collected and analyzed using machine learning (ML) classifiers, including K-nearest neighbor (KNN), support vector machines (SVMs), and Gaussian Naïve Bayes (GNB), to predict the SABR response. The models were evaluated using receiver operator characteristics (ROCs) and area under the curve (AUC) analysis. Results: The KNN, SVM, and GNB models were implemented using clinical and graph features. Among the generated prediction models, the graph features showed higher predictive performances compared to the models containing clinical features alone. The highest-performing model, using computationally derived graph features, showed an AUC of 0.92, while the highest clinical model showed an AUC of 0.62 within unseen test sets. Conclusions: Spatial TIL models demonstrate strong potential for predicting SABR response in inoperable breast cancer. TILs indicate a higher independent predictive performance than clinical-level features alone. |
| format | Article |
| id | doaj-art-5d2c9c87c3bf46798233fd1036321125 |
| institution | OA Journals |
| issn | 2673-592X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
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| series | Radiation |
| spelling | doaj-art-5d2c9c87c3bf46798233fd10363211252025-08-20T02:21:50ZengMDPI AGRadiation2673-592X2025-04-01521110.3390/radiation5020011Assessment of Tumor Infiltrating Lymphocytes in Predicting Stereotactic Ablative Radiotherapy (SABR) Response in Unresectable Breast CancerMateusz Bielecki0Khadijeh Saednia1Fang-I Lu2Shely Kagan3Danny Vesprini4Katarzyna J. Jerzak5Roberto Salgado6Raffi Karshafian7William T. Tran8Biological Sciences Platform, Sunnybrook Research Institute, 2075 Bayview Ave., Room TB 097, Toronto, ON M4N 3M5, CanadaBiological Sciences Platform, Sunnybrook Research Institute, 2075 Bayview Ave., Room TB 097, Toronto, ON M4N 3M5, CanadaDepartment of Laboratory Medicine and Molecular Diagnostics, Sunnybrook Health Sciences Centre, Toronto, ON M4N 3M5, CanadaBiological Sciences Platform, Sunnybrook Research Institute, 2075 Bayview Ave., Room TB 097, Toronto, ON M4N 3M5, CanadaBiological Sciences Platform, Sunnybrook Research Institute, 2075 Bayview Ave., Room TB 097, Toronto, ON M4N 3M5, CanadaDivision of Medical Oncology and Hematology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, Toronto, ON M4N 3M5, CanadaDepartment of Pathology, ZAS Hospitals, 2020 Antwerp, BelgiumDepartment of Physics, Toronto Metropolitan University, Toronto, ON M5B 2K3, CanadaBiological Sciences Platform, Sunnybrook Research Institute, 2075 Bayview Ave., Room TB 097, Toronto, ON M4N 3M5, CanadaBackground: Patients with advanced breast cancer (BC) may be treated with stereotactic ablative radiotherapy (SABR) for tumor control. Variable treatment responses are a clinical challenge and there is a need to predict tumor radiosensitivity a priori. There is evidence showing that tumor infiltrating lymphocytes (TILs) are markers for chemotherapy response; however, this association has not yet been validated in breast radiation therapy. This pilot study investigates the computational analysis of TILs to predict SABR response in patients with inoperable BC. Methods: Patients with inoperable breast cancer (<i>n</i> = 22) were included for analysis and classified into partial response (<i>n</i> = 12) and stable disease (<i>n</i> = 10) groups. Pre-treatment tumor biopsies (<i>n</i> = 104) were prepared, digitally imaged, and underwent computational analysis. Whole slide images (WSIs) were pre-processed, and then a pre-trained convolutional neural network model (CNN) was employed to identify the regions of interest. The TILs were annotated, and spatial graph features were extracted. The clinical and spatial features were collected and analyzed using machine learning (ML) classifiers, including K-nearest neighbor (KNN), support vector machines (SVMs), and Gaussian Naïve Bayes (GNB), to predict the SABR response. The models were evaluated using receiver operator characteristics (ROCs) and area under the curve (AUC) analysis. Results: The KNN, SVM, and GNB models were implemented using clinical and graph features. Among the generated prediction models, the graph features showed higher predictive performances compared to the models containing clinical features alone. The highest-performing model, using computationally derived graph features, showed an AUC of 0.92, while the highest clinical model showed an AUC of 0.62 within unseen test sets. Conclusions: Spatial TIL models demonstrate strong potential for predicting SABR response in inoperable breast cancer. TILs indicate a higher independent predictive performance than clinical-level features alone.https://www.mdpi.com/2673-592X/5/2/11stereotactic ablative radiotherapyadvanced inoperable breast cancertumor infiltrating lymphocytes |
| spellingShingle | Mateusz Bielecki Khadijeh Saednia Fang-I Lu Shely Kagan Danny Vesprini Katarzyna J. Jerzak Roberto Salgado Raffi Karshafian William T. Tran Assessment of Tumor Infiltrating Lymphocytes in Predicting Stereotactic Ablative Radiotherapy (SABR) Response in Unresectable Breast Cancer Radiation stereotactic ablative radiotherapy advanced inoperable breast cancer tumor infiltrating lymphocytes |
| title | Assessment of Tumor Infiltrating Lymphocytes in Predicting Stereotactic Ablative Radiotherapy (SABR) Response in Unresectable Breast Cancer |
| title_full | Assessment of Tumor Infiltrating Lymphocytes in Predicting Stereotactic Ablative Radiotherapy (SABR) Response in Unresectable Breast Cancer |
| title_fullStr | Assessment of Tumor Infiltrating Lymphocytes in Predicting Stereotactic Ablative Radiotherapy (SABR) Response in Unresectable Breast Cancer |
| title_full_unstemmed | Assessment of Tumor Infiltrating Lymphocytes in Predicting Stereotactic Ablative Radiotherapy (SABR) Response in Unresectable Breast Cancer |
| title_short | Assessment of Tumor Infiltrating Lymphocytes in Predicting Stereotactic Ablative Radiotherapy (SABR) Response in Unresectable Breast Cancer |
| title_sort | assessment of tumor infiltrating lymphocytes in predicting stereotactic ablative radiotherapy sabr response in unresectable breast cancer |
| topic | stereotactic ablative radiotherapy advanced inoperable breast cancer tumor infiltrating lymphocytes |
| url | https://www.mdpi.com/2673-592X/5/2/11 |
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