Machine learning-based classification model to differentiate subtypes of invasive breast cancer using MRI
IntroductionBreast cancer is considered one of the most lethal diseases among women worldwide. Invasive Ductal Carcinoma (IDC) and Invasive Lobular Carcinoma (ILC) are the two most prominent subtypes of breast cancer. They differ in epidemiology, molecular alterations, and clinicopathological featur...
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2025.1588787/full |
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| author | Nadesalingam Paripooranan Warnakulasuriya Buddhini Nirasha H. R. P. Perera Sahan M. Vijithananda P. Badra Hewavithana Lahanda Purage Givanthika Sherminie Mohan L. Jayatilake |
| author_facet | Nadesalingam Paripooranan Warnakulasuriya Buddhini Nirasha H. R. P. Perera Sahan M. Vijithananda P. Badra Hewavithana Lahanda Purage Givanthika Sherminie Mohan L. Jayatilake |
| author_sort | Nadesalingam Paripooranan |
| collection | DOAJ |
| description | IntroductionBreast cancer is considered one of the most lethal diseases among women worldwide. Invasive Ductal Carcinoma (IDC) and Invasive Lobular Carcinoma (ILC) are the two most prominent subtypes of breast cancer. They differ in epidemiology, molecular alterations, and clinicopathological features. Patient treatment and management also differ due to these variations.AimThe study aimed to develop a predictive model to differentiate IDC and ILC using machine learning techniques based on the morphological features of the contralateral breast. Methods- 143 magnetic resonance imaging (MRI) images were sourced from the “DUKE Breast-Cancer” collection on the Cancer Imaging Archive website. Regions of interest were drawn on each slice to compute the morphological features of the contralateral breast using the 3D Slicer application. Supervised learning methods were applied to the morphological features to build a predictive model incorporating a Random Forest Classifier to differentiate IDC and ILC. Hyperparameters were tuned to optimize the model.ResultsThe model was able to differentiate IDC and ILC with an accuracy of 79% and an Area Under the Curve of 0.851 on the Receiver Operating Characteristic Curve. Among the morphological features, the total volume of the contralateral breast, surface area of the contralateral breast, breast density, and the ratio of the total volume of the contralateral breast to its surface area had higher F-scores, indicating that the dimensions of the contralateral breast could be an important factor in differentiating IDC and ILC.ConclusionThis study successfully developed and optimized a predictive model based on breast morphological features to differentiate IDC and ILC using machine learning methods. |
| format | Article |
| id | doaj-art-e40cfd71ef2d4c628cc12788f57cd40d |
| institution | OA Journals |
| issn | 2234-943X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj-art-e40cfd71ef2d4c628cc12788f57cd40d2025-08-20T02:32:19ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2025-06-011510.3389/fonc.2025.15887871588787Machine learning-based classification model to differentiate subtypes of invasive breast cancer using MRINadesalingam Paripooranan0Warnakulasuriya Buddhini Nirasha1H. R. P. Perera2Sahan M. Vijithananda3P. Badra Hewavithana4Lahanda Purage Givanthika Sherminie5Mohan L. Jayatilake6Department of Radiology, Faculty of Medicine, University of Peradeniya, Peradeniya, Sri LankaDepartment of Radiography/Radiotherapy, Faculty of Allied Health Sciences, University of Peradeniya, Peradeniya, Sri LankaDepartment of Radiography/Radiotherapy, Faculty of Allied Health Sciences, University of Peradeniya, Peradeniya, Sri LankaDepartment of Radiology, Faculty of Medicine, University of Peradeniya, Peradeniya, Sri LankaDepartment of Radiology, Faculty of Medicine, University of Peradeniya, Peradeniya, Sri LankaDepartment of Radiography/Radiotherapy, Faculty of Allied Health Sciences, University of Peradeniya, Peradeniya, Sri LankaDepartment of Radiography/Radiotherapy, Faculty of Allied Health Sciences, University of Peradeniya, Peradeniya, Sri LankaIntroductionBreast cancer is considered one of the most lethal diseases among women worldwide. Invasive Ductal Carcinoma (IDC) and Invasive Lobular Carcinoma (ILC) are the two most prominent subtypes of breast cancer. They differ in epidemiology, molecular alterations, and clinicopathological features. Patient treatment and management also differ due to these variations.AimThe study aimed to develop a predictive model to differentiate IDC and ILC using machine learning techniques based on the morphological features of the contralateral breast. Methods- 143 magnetic resonance imaging (MRI) images were sourced from the “DUKE Breast-Cancer” collection on the Cancer Imaging Archive website. Regions of interest were drawn on each slice to compute the morphological features of the contralateral breast using the 3D Slicer application. Supervised learning methods were applied to the morphological features to build a predictive model incorporating a Random Forest Classifier to differentiate IDC and ILC. Hyperparameters were tuned to optimize the model.ResultsThe model was able to differentiate IDC and ILC with an accuracy of 79% and an Area Under the Curve of 0.851 on the Receiver Operating Characteristic Curve. Among the morphological features, the total volume of the contralateral breast, surface area of the contralateral breast, breast density, and the ratio of the total volume of the contralateral breast to its surface area had higher F-scores, indicating that the dimensions of the contralateral breast could be an important factor in differentiating IDC and ILC.ConclusionThis study successfully developed and optimized a predictive model based on breast morphological features to differentiate IDC and ILC using machine learning methods.https://www.frontiersin.org/articles/10.3389/fonc.2025.1588787/fullmachine learningbreast MRIinvasive breast cancerinvasive ductal carcinomainvasive lobular carcinoma |
| spellingShingle | Nadesalingam Paripooranan Warnakulasuriya Buddhini Nirasha H. R. P. Perera Sahan M. Vijithananda P. Badra Hewavithana Lahanda Purage Givanthika Sherminie Mohan L. Jayatilake Machine learning-based classification model to differentiate subtypes of invasive breast cancer using MRI Frontiers in Oncology machine learning breast MRI invasive breast cancer invasive ductal carcinoma invasive lobular carcinoma |
| title | Machine learning-based classification model to differentiate subtypes of invasive breast cancer using MRI |
| title_full | Machine learning-based classification model to differentiate subtypes of invasive breast cancer using MRI |
| title_fullStr | Machine learning-based classification model to differentiate subtypes of invasive breast cancer using MRI |
| title_full_unstemmed | Machine learning-based classification model to differentiate subtypes of invasive breast cancer using MRI |
| title_short | Machine learning-based classification model to differentiate subtypes of invasive breast cancer using MRI |
| title_sort | machine learning based classification model to differentiate subtypes of invasive breast cancer using mri |
| topic | machine learning breast MRI invasive breast cancer invasive ductal carcinoma invasive lobular carcinoma |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2025.1588787/full |
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