A Multimodal Deep Learning Model for the Classification of Breast Cancer Subtypes
<b>Background</b>: Breast cancer is a heterogeneous disease with distinct molecular subtypes, each requiring tailored therapeutic strategies. Accurate classification of these subtypes is crucial for optimizing treatment and improving patient outcomes. While immunohistochemistry remains t...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/15/8/995 |
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| author | Chaima Ben Rabah Aamenah Sattar Ahmed Ibrahim Ahmed Serag |
| author_facet | Chaima Ben Rabah Aamenah Sattar Ahmed Ibrahim Ahmed Serag |
| author_sort | Chaima Ben Rabah |
| collection | DOAJ |
| description | <b>Background</b>: Breast cancer is a heterogeneous disease with distinct molecular subtypes, each requiring tailored therapeutic strategies. Accurate classification of these subtypes is crucial for optimizing treatment and improving patient outcomes. While immunohistochemistry remains the gold standard for subtyping, it is invasive and may not fully capture tumor heterogeneity. Artificial Intelligence (AI), particularly Deep Learning (DL), offers a promising non-invasive alternative by analyzing medical imaging data. <b>Methods</b>: In this study, we propose a multimodal DL model that integrates mammography images with clinical metadata to classify breast lesions into five categories: benign, luminal A, luminal B, HER2-enriched, and triple-negative. Using the publicly available Chinese Mammography Database (CMMD), our model was trained and evaluated on a dataset of 4056 images from 1775 patients. <b>Results</b>: The proposed multimodal approach significantly outperformed a unimodal model based solely on mammography images, achieving an AUC of 88.87% for multiclass classification of these five categories, compared to 61.3% AUC for the unimodal model. <b>Conclusions</b>: These findings highlight the potential of multimodal AI-driven approaches for non-invasive breast cancer subtype classification, paving the way for improved diagnostic precision and personalized treatment strategies. |
| format | Article |
| id | doaj-art-ec5ce93a9ee64a31be42c58177aeb8e0 |
| institution | DOAJ |
| issn | 2075-4418 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj-art-ec5ce93a9ee64a31be42c58177aeb8e02025-08-20T03:13:51ZengMDPI AGDiagnostics2075-44182025-04-0115899510.3390/diagnostics15080995A Multimodal Deep Learning Model for the Classification of Breast Cancer SubtypesChaima Ben Rabah0Aamenah Sattar1Ahmed Ibrahim2Ahmed Serag3AI Innovation Lab, Weill Cornell Medicine, Doha 24144, QatarDepartment of Medicine, New Vision University, 0159 Tbilisi, GeorgiaAI Innovation Lab, Weill Cornell Medicine, Doha 24144, QatarAI Innovation Lab, Weill Cornell Medicine, Doha 24144, Qatar<b>Background</b>: Breast cancer is a heterogeneous disease with distinct molecular subtypes, each requiring tailored therapeutic strategies. Accurate classification of these subtypes is crucial for optimizing treatment and improving patient outcomes. While immunohistochemistry remains the gold standard for subtyping, it is invasive and may not fully capture tumor heterogeneity. Artificial Intelligence (AI), particularly Deep Learning (DL), offers a promising non-invasive alternative by analyzing medical imaging data. <b>Methods</b>: In this study, we propose a multimodal DL model that integrates mammography images with clinical metadata to classify breast lesions into five categories: benign, luminal A, luminal B, HER2-enriched, and triple-negative. Using the publicly available Chinese Mammography Database (CMMD), our model was trained and evaluated on a dataset of 4056 images from 1775 patients. <b>Results</b>: The proposed multimodal approach significantly outperformed a unimodal model based solely on mammography images, achieving an AUC of 88.87% for multiclass classification of these five categories, compared to 61.3% AUC for the unimodal model. <b>Conclusions</b>: These findings highlight the potential of multimodal AI-driven approaches for non-invasive breast cancer subtype classification, paving the way for improved diagnostic precision and personalized treatment strategies.https://www.mdpi.com/2075-4418/15/8/995breast cancermolecular subtype classificationdeep learningartificial intelligencemultimodalpersonalized medicine |
| spellingShingle | Chaima Ben Rabah Aamenah Sattar Ahmed Ibrahim Ahmed Serag A Multimodal Deep Learning Model for the Classification of Breast Cancer Subtypes Diagnostics breast cancer molecular subtype classification deep learning artificial intelligence multimodal personalized medicine |
| title | A Multimodal Deep Learning Model for the Classification of Breast Cancer Subtypes |
| title_full | A Multimodal Deep Learning Model for the Classification of Breast Cancer Subtypes |
| title_fullStr | A Multimodal Deep Learning Model for the Classification of Breast Cancer Subtypes |
| title_full_unstemmed | A Multimodal Deep Learning Model for the Classification of Breast Cancer Subtypes |
| title_short | A Multimodal Deep Learning Model for the Classification of Breast Cancer Subtypes |
| title_sort | multimodal deep learning model for the classification of breast cancer subtypes |
| topic | breast cancer molecular subtype classification deep learning artificial intelligence multimodal personalized medicine |
| url | https://www.mdpi.com/2075-4418/15/8/995 |
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