Prediction of Chemotherapy Response in Locally Advanced Breast Cancer Patients at Pre-Treatment Using CT Textural Features and Machine Learning: Comparison of Feature Selection Methods
Rationale: Neoadjuvant chemotherapy (NAC) is a key element of treatment for locally advanced breast cancer (LABC). Predicting the response of NAC for patients with LABC before initiating treatment would be valuable to customize therapies and ensure the delivery of effective care. Objective: Our obje...
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2025-03-01
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| Series: | Tomography |
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| author | Amir Moslemi Laurentius Oscar Osapoetra Archya Dasgupta Schontal Halstead David Alberico Maureen Trudeau Sonal Gandhi Andrea Eisen Frances Wright Nicole Look-Hong Belinda Curpen Michael Kolios Gregory J. Czarnota |
| author_facet | Amir Moslemi Laurentius Oscar Osapoetra Archya Dasgupta Schontal Halstead David Alberico Maureen Trudeau Sonal Gandhi Andrea Eisen Frances Wright Nicole Look-Hong Belinda Curpen Michael Kolios Gregory J. Czarnota |
| author_sort | Amir Moslemi |
| collection | DOAJ |
| description | Rationale: Neoadjuvant chemotherapy (NAC) is a key element of treatment for locally advanced breast cancer (LABC). Predicting the response of NAC for patients with LABC before initiating treatment would be valuable to customize therapies and ensure the delivery of effective care. Objective: Our objective was to develop predictive measures of tumor response to NAC prior to starting for LABC using machine learning and textural computed tomography (CT) features in different level of frequencies. Materials and Methods: A total of 851 textural biomarkers were determined from CT images and their wavelet coefficients for 117 patients with LABC to evaluate the response to NAC. A machine learning pipeline was designed to classify response to NAC treatment for patients with LABC. For training predictive models, three models including all features (wavelet and original image features), only wavelet and only original-image features were considered. We determined features from CT images in different level of frequencies using wavelet transform. Additionally, we conducted a comparison of feature selection methods including mRMR, Relief, Rref QR decomposition, nonnegative matrix factorization and perturbation theory feature selection techniques. Results: Of the 117 patients with LABC evaluated, 82 (70%) had clinical–pathological response to chemotherapy and 35 (30%) had no response to chemotherapy. The best performance for hold-out data splitting was obtained using the KNN classifier using the Top-5 features, which were obtained by mRMR, for all features (accuracy = 77%, specificity = 80%, sensitivity = 56%, and balanced-accuracy = 68%). Likewise, the best performance for leave-one-out data splitting could be obtained by the KNN classifier using the Top-5 features, which was obtained by mRMR, for all features (accuracy = 75%, specificity = 76%, sensitivity = 62%, and balanced-accuracy = 72%). Conclusions: The combination of original textural features and wavelet features results in a greater predictive accuracy of NAC response for LABC patients. This predictive model can be utilized to predict treatment outcomes prior to starting, and clinicians can use it as a recommender system to modify treatment. |
| format | Article |
| id | doaj-art-e4239c0c0f4c4daa8c4cc5235b400b22 |
| institution | Kabale University |
| issn | 2379-1381 2379-139X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Tomography |
| spelling | doaj-art-e4239c0c0f4c4daa8c4cc5235b400b222025-08-20T03:43:58ZengMDPI AGTomography2379-13812379-139X2025-03-011133310.3390/tomography11030033Prediction of Chemotherapy Response in Locally Advanced Breast Cancer Patients at Pre-Treatment Using CT Textural Features and Machine Learning: Comparison of Feature Selection MethodsAmir Moslemi0Laurentius Oscar Osapoetra1Archya Dasgupta2Schontal Halstead3David Alberico4Maureen Trudeau5Sonal Gandhi6Andrea Eisen7Frances Wright8Nicole Look-Hong9Belinda Curpen10Michael Kolios11Gregory J. Czarnota12Physical Sciences, Sunnybrook Research Institute, Toronto, ON M4N 3M5, CanadaPhysical Sciences, Sunnybrook Research Institute, Toronto, ON M4N 3M5, CanadaPhysical Sciences, Sunnybrook Research Institute, Toronto, ON M4N 3M5, CanadaPhysical Sciences, Sunnybrook Research Institute, Toronto, ON M4N 3M5, CanadaPhysical Sciences, Sunnybrook Research Institute, Toronto, ON M4N 3M5, CanadaDepartment of Medical Oncology, Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, ON M4N 3M5, CanadaDepartment of Medical Oncology, Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, ON M4N 3M5, CanadaDepartment of Medical Oncology, Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, ON M4N 3M5, CanadaDepartment of Surgical Oncology, Department of Surgery, Sunnybrook Health Sciences Centre, Toronto, ON M4N 3M5, CanadaDepartment of Surgical Oncology, Department of Surgery, Sunnybrook Health Sciences Centre, Toronto, ON M4N 3M5, CanadaDepartment of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, ON M4N 3M5, CanadaDepartment of Physics, Toronto Metropolitan University, Toronto, ON M5B 2K3, CanadaPhysical Sciences, Sunnybrook Research Institute, Toronto, ON M4N 3M5, CanadaRationale: Neoadjuvant chemotherapy (NAC) is a key element of treatment for locally advanced breast cancer (LABC). Predicting the response of NAC for patients with LABC before initiating treatment would be valuable to customize therapies and ensure the delivery of effective care. Objective: Our objective was to develop predictive measures of tumor response to NAC prior to starting for LABC using machine learning and textural computed tomography (CT) features in different level of frequencies. Materials and Methods: A total of 851 textural biomarkers were determined from CT images and their wavelet coefficients for 117 patients with LABC to evaluate the response to NAC. A machine learning pipeline was designed to classify response to NAC treatment for patients with LABC. For training predictive models, three models including all features (wavelet and original image features), only wavelet and only original-image features were considered. We determined features from CT images in different level of frequencies using wavelet transform. Additionally, we conducted a comparison of feature selection methods including mRMR, Relief, Rref QR decomposition, nonnegative matrix factorization and perturbation theory feature selection techniques. Results: Of the 117 patients with LABC evaluated, 82 (70%) had clinical–pathological response to chemotherapy and 35 (30%) had no response to chemotherapy. The best performance for hold-out data splitting was obtained using the KNN classifier using the Top-5 features, which were obtained by mRMR, for all features (accuracy = 77%, specificity = 80%, sensitivity = 56%, and balanced-accuracy = 68%). Likewise, the best performance for leave-one-out data splitting could be obtained by the KNN classifier using the Top-5 features, which was obtained by mRMR, for all features (accuracy = 75%, specificity = 76%, sensitivity = 62%, and balanced-accuracy = 72%). Conclusions: The combination of original textural features and wavelet features results in a greater predictive accuracy of NAC response for LABC patients. This predictive model can be utilized to predict treatment outcomes prior to starting, and clinicians can use it as a recommender system to modify treatment.https://www.mdpi.com/2379-139X/11/3/33NACLABCCT imagingtextural featuresmachine learning |
| spellingShingle | Amir Moslemi Laurentius Oscar Osapoetra Archya Dasgupta Schontal Halstead David Alberico Maureen Trudeau Sonal Gandhi Andrea Eisen Frances Wright Nicole Look-Hong Belinda Curpen Michael Kolios Gregory J. Czarnota Prediction of Chemotherapy Response in Locally Advanced Breast Cancer Patients at Pre-Treatment Using CT Textural Features and Machine Learning: Comparison of Feature Selection Methods Tomography NAC LABC CT imaging textural features machine learning |
| title | Prediction of Chemotherapy Response in Locally Advanced Breast Cancer Patients at Pre-Treatment Using CT Textural Features and Machine Learning: Comparison of Feature Selection Methods |
| title_full | Prediction of Chemotherapy Response in Locally Advanced Breast Cancer Patients at Pre-Treatment Using CT Textural Features and Machine Learning: Comparison of Feature Selection Methods |
| title_fullStr | Prediction of Chemotherapy Response in Locally Advanced Breast Cancer Patients at Pre-Treatment Using CT Textural Features and Machine Learning: Comparison of Feature Selection Methods |
| title_full_unstemmed | Prediction of Chemotherapy Response in Locally Advanced Breast Cancer Patients at Pre-Treatment Using CT Textural Features and Machine Learning: Comparison of Feature Selection Methods |
| title_short | Prediction of Chemotherapy Response in Locally Advanced Breast Cancer Patients at Pre-Treatment Using CT Textural Features and Machine Learning: Comparison of Feature Selection Methods |
| title_sort | prediction of chemotherapy response in locally advanced breast cancer patients at pre treatment using ct textural features and machine learning comparison of feature selection methods |
| topic | NAC LABC CT imaging textural features machine learning |
| url | https://www.mdpi.com/2379-139X/11/3/33 |
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