Efficient and accurate non-invasive tumor monitoring and diagnosis by interpretable deep learning
Summary: Detecting tumor-specific DNA methylation in circulating tumor DNA (ctDNA) offers a non-invasive method for tumor detection. The primary challenge lies in identifying the extremely low abundance of ctDNA in cell-free blood plasma (cfDNA). In this study, we present Oncoder, an interpretable d...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-08-01
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| Series: | iScience |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004225014191 |
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| author | Youpeng Yang Jiaying Liu Yutong He Yingjie Yang Tao Jiang Jia Tang Xin Li |
| author_facet | Youpeng Yang Jiaying Liu Yutong He Yingjie Yang Tao Jiang Jia Tang Xin Li |
| author_sort | Youpeng Yang |
| collection | DOAJ |
| description | Summary: Detecting tumor-specific DNA methylation in circulating tumor DNA (ctDNA) offers a non-invasive method for tumor detection. The primary challenge lies in identifying the extremely low abundance of ctDNA in cell-free blood plasma (cfDNA). In this study, we present Oncoder, an interpretable deep learning-based tool for economical and accurate non-invasive tumor monitoring and diagnosis. Unlike other methods, Oncoder learns scientifically sound reference methylation atlases from patient blood to provide additional diagnostic insights, fostering trust among clinicians and patients, and continuously improves its accuracy through iterative learning. In simulations, Oncoder reduced prediction errors of tumor signals in blood by at least 30% compared to existing methods and showed the highest prediction correlation, indicating more accurate tumor progression monitoring. We also evaluated Oncoder’s performance in various real-world applications. Oncoder sensitively detected changes in ctDNA levels during tumor development and treatment and exhibited superior diagnostic potential even in the earliest stages of cancer. |
| format | Article |
| id | doaj-art-51bf5865e39d4448b73e686b7687bc84 |
| institution | DOAJ |
| issn | 2589-0042 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj-art-51bf5865e39d4448b73e686b7687bc842025-08-20T03:15:42ZengElsevieriScience2589-00422025-08-0128811315810.1016/j.isci.2025.113158Efficient and accurate non-invasive tumor monitoring and diagnosis by interpretable deep learningYoupeng Yang0Jiaying Liu1Yutong He2Yingjie Yang3Tao Jiang4Jia Tang5Xin Li6School of Medicine, Shenzhen Campus of Sun Yat-sen University, Shenzhen, ChinaSchool of Medicine, Shenzhen Campus of Sun Yat-sen University, Shenzhen, ChinaSchool of Medicine, Shenzhen Campus of Sun Yat-sen University, Shenzhen, ChinaSchool of Medicine, Shenzhen Campus of Sun Yat-sen University, Shenzhen, ChinaSchool of Medicine, Shenzhen Campus of Sun Yat-sen University, Shenzhen, ChinaNHC Key Laboratory of Male Reproduction and Genetics, Guangdong Provincial Reproductive Science Institute (Guangdong Provincial Fertility Hospital), Guangzhou, China; Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China; Corresponding authorSchool of Medicine, Shenzhen Campus of Sun Yat-sen University, Shenzhen, China; Guangdong Provincial Key Laboratory of Digestive Cancer Research, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, China; Corresponding authorSummary: Detecting tumor-specific DNA methylation in circulating tumor DNA (ctDNA) offers a non-invasive method for tumor detection. The primary challenge lies in identifying the extremely low abundance of ctDNA in cell-free blood plasma (cfDNA). In this study, we present Oncoder, an interpretable deep learning-based tool for economical and accurate non-invasive tumor monitoring and diagnosis. Unlike other methods, Oncoder learns scientifically sound reference methylation atlases from patient blood to provide additional diagnostic insights, fostering trust among clinicians and patients, and continuously improves its accuracy through iterative learning. In simulations, Oncoder reduced prediction errors of tumor signals in blood by at least 30% compared to existing methods and showed the highest prediction correlation, indicating more accurate tumor progression monitoring. We also evaluated Oncoder’s performance in various real-world applications. Oncoder sensitively detected changes in ctDNA levels during tumor development and treatment and exhibited superior diagnostic potential even in the earliest stages of cancer.http://www.sciencedirect.com/science/article/pii/S2589004225014191CancerArtificial intelligence |
| spellingShingle | Youpeng Yang Jiaying Liu Yutong He Yingjie Yang Tao Jiang Jia Tang Xin Li Efficient and accurate non-invasive tumor monitoring and diagnosis by interpretable deep learning iScience Cancer Artificial intelligence |
| title | Efficient and accurate non-invasive tumor monitoring and diagnosis by interpretable deep learning |
| title_full | Efficient and accurate non-invasive tumor monitoring and diagnosis by interpretable deep learning |
| title_fullStr | Efficient and accurate non-invasive tumor monitoring and diagnosis by interpretable deep learning |
| title_full_unstemmed | Efficient and accurate non-invasive tumor monitoring and diagnosis by interpretable deep learning |
| title_short | Efficient and accurate non-invasive tumor monitoring and diagnosis by interpretable deep learning |
| title_sort | efficient and accurate non invasive tumor monitoring and diagnosis by interpretable deep learning |
| topic | Cancer Artificial intelligence |
| url | http://www.sciencedirect.com/science/article/pii/S2589004225014191 |
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