RareNet: a deep learning model for rare cancer diagnosis
Abstract Although significant advances have been made in the early detection of many cancers, challenges remain in the early diagnosis of rare cancers, including Wilms tumor, Clear Cell Sarcoma of the Kidney, Neuroblastoma, Osteosarcoma, and Acute Myeloid Leukemia, perhaps due to their relative obsc...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-08829-y |
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| author | Danyang Shao Sohan Addagudi Joseph Cowles Arnav Jain Lauren D’Souza Steven Gore Rajeev K. Azad |
| author_facet | Danyang Shao Sohan Addagudi Joseph Cowles Arnav Jain Lauren D’Souza Steven Gore Rajeev K. Azad |
| author_sort | Danyang Shao |
| collection | DOAJ |
| description | Abstract Although significant advances have been made in the early detection of many cancers, challenges remain in the early diagnosis of rare cancers, including Wilms tumor, Clear Cell Sarcoma of the Kidney, Neuroblastoma, Osteosarcoma, and Acute Myeloid Leukemia, perhaps due to their relative obscurity and scarce data compared to common cancers. Application of artificial intelligence or deep learning has shown promising results in disease diagnosis including in their ability to diagnose cancers and detect their tissue of origin. However, their ability to detect rare cancers is yet to be comprehensively assessed. This motivated us to develop, RareNet, which leverages transfer learning of an established deep learning model, namely, CancerNet, to classify rare cancers. The transfer learning framework of RareNet utilized DNA methylation data of various biopsied rare cancers to learn epigenetic signatures of rare cancers. RareNet achieved an overall accuracy (F1 score) of ~ 96%, outperforming other machine learning models including Random Forest, K Nearest Neighbors, Decision Tree Classifier, and Support Vector Classifier. |
| format | Article |
| id | doaj-art-13ff4b10f20c4d18a6d921cceae4a47c |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-13ff4b10f20c4d18a6d921cceae4a47c2025-08-20T04:01:36ZengNature PortfolioScientific Reports2045-23222025-07-011511810.1038/s41598-025-08829-yRareNet: a deep learning model for rare cancer diagnosisDanyang Shao0Sohan Addagudi1Joseph Cowles2Arnav Jain3Lauren D’Souza4Steven Gore5Rajeev K. Azad6Department of Biological Sciences and BioDiscovery Institute, University of North TexasDepartment of Computer Sciences, University of MinnesotaDepartment of Computer Sciences, Texas Tech UniversityDepartment of Electrical Engineering and Computer Science, University of KansasDepartment of Electrical Engineering and Computer Science, University of KansasDepartment of Biological Sciences and BioDiscovery Institute, University of North TexasDepartment of Biological Sciences and BioDiscovery Institute, University of North TexasAbstract Although significant advances have been made in the early detection of many cancers, challenges remain in the early diagnosis of rare cancers, including Wilms tumor, Clear Cell Sarcoma of the Kidney, Neuroblastoma, Osteosarcoma, and Acute Myeloid Leukemia, perhaps due to their relative obscurity and scarce data compared to common cancers. Application of artificial intelligence or deep learning has shown promising results in disease diagnosis including in their ability to diagnose cancers and detect their tissue of origin. However, their ability to detect rare cancers is yet to be comprehensively assessed. This motivated us to develop, RareNet, which leverages transfer learning of an established deep learning model, namely, CancerNet, to classify rare cancers. The transfer learning framework of RareNet utilized DNA methylation data of various biopsied rare cancers to learn epigenetic signatures of rare cancers. RareNet achieved an overall accuracy (F1 score) of ~ 96%, outperforming other machine learning models including Random Forest, K Nearest Neighbors, Decision Tree Classifier, and Support Vector Classifier.https://doi.org/10.1038/s41598-025-08829-y |
| spellingShingle | Danyang Shao Sohan Addagudi Joseph Cowles Arnav Jain Lauren D’Souza Steven Gore Rajeev K. Azad RareNet: a deep learning model for rare cancer diagnosis Scientific Reports |
| title | RareNet: a deep learning model for rare cancer diagnosis |
| title_full | RareNet: a deep learning model for rare cancer diagnosis |
| title_fullStr | RareNet: a deep learning model for rare cancer diagnosis |
| title_full_unstemmed | RareNet: a deep learning model for rare cancer diagnosis |
| title_short | RareNet: a deep learning model for rare cancer diagnosis |
| title_sort | rarenet a deep learning model for rare cancer diagnosis |
| url | https://doi.org/10.1038/s41598-025-08829-y |
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