Discrimination of superficial lymph nodes using ultrasonography and tissue metabolomics coupled with machine learning
IntroductionDiagnosing the types of malignant lymphoma could help determine the most suitable treatment, anticipate the probability of recurrence and guide long-term monitoring and follow-up care.MethodsWe evaluated the differences in benign, lymphoma and metastasis superficial lymph nodes using ult...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2025.1510018/full |
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| author | Lu Li Xinyue Wang Hongyan Deng Wenjuan Lu Yasu Zhou Xinhua Ye Yong Li Jie Wang |
| author_facet | Lu Li Xinyue Wang Hongyan Deng Wenjuan Lu Yasu Zhou Xinhua Ye Yong Li Jie Wang |
| author_sort | Lu Li |
| collection | DOAJ |
| description | IntroductionDiagnosing the types of malignant lymphoma could help determine the most suitable treatment, anticipate the probability of recurrence and guide long-term monitoring and follow-up care.MethodsWe evaluated the differences in benign, lymphoma and metastasis superficial lymph nodes using ultrasonography and tissue metabolomics.ResultsOur findings indicated that three ultrasonographic features, blood supply pattern, cortical echo, and cortex elasticity, hold potential in differentiating malignant lymph nodes from benign ones, and the shape and corticomedullary boundary emerged as significant indicators for distinguishing between metastatic and lymphoma groups. Metabolomics revealed the difference in metabolic profiles among lymph nodes. We observed significant increases in many amino acids, organic acids, lipids, and nucleosides in both lymphoma and metastasis groups, compared to the benign group. Specifically, the lymphoma group exhibited higher levels of nucleotides (inosine monophosphate and adenosine diphosphate) as well as glutamic acid, and the metastasis group was characterized by higher levels of carbohydrates, acylcarnitines, glycerophospholipids, and uric acid. Linear discriminant analysis coupled with these metabolites could be used for differentiating lymph nodes, achieving recognition rates ranging from 87.4% to 89.3%, outperforming ultrasonography (63.1% to 75.4%).DiscussionOur findings could contribute to a better understanding of malignant lymph node development and provide novel targets for therapeutic interventions. |
| format | Article |
| id | doaj-art-97370e3dc8bd43de9e1ede861410985e |
| institution | Kabale University |
| issn | 2234-943X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj-art-97370e3dc8bd43de9e1ede861410985e2025-01-28T05:10:28ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2025-01-011510.3389/fonc.2025.15100181510018Discrimination of superficial lymph nodes using ultrasonography and tissue metabolomics coupled with machine learningLu Li0Xinyue Wang1Hongyan Deng2Wenjuan Lu3Yasu Zhou4Xinhua Ye5Yong Li6Jie Wang7Department of Ultrasound, The First Affiliated Hospital, Nanjing Medical University, Nanjing, ChinaDepartment of Ultrasound, The First Affiliated Hospital, Nanjing Medical University, Nanjing, ChinaDepartment of Ultrasound, The First Affiliated Hospital, Nanjing Medical University, Nanjing, ChinaDepartment of Ultrasound, The First Affiliated Hospital, Nanjing Medical University, Nanjing, ChinaDepartment of Ultrasound, The First Affiliated Hospital, Nanjing Medical University, Nanjing, ChinaDepartment of Ultrasound, The First Affiliated Hospital, Nanjing Medical University, Nanjing, ChinaInstitute of Food Safety and Nutrition, Jiangsu Academy of Agricultural Sciences, Nanjing, ChinaDepartment of Radiology, The First Affiliated Hospital, Nanjing Medical University, Nanjing, ChinaIntroductionDiagnosing the types of malignant lymphoma could help determine the most suitable treatment, anticipate the probability of recurrence and guide long-term monitoring and follow-up care.MethodsWe evaluated the differences in benign, lymphoma and metastasis superficial lymph nodes using ultrasonography and tissue metabolomics.ResultsOur findings indicated that three ultrasonographic features, blood supply pattern, cortical echo, and cortex elasticity, hold potential in differentiating malignant lymph nodes from benign ones, and the shape and corticomedullary boundary emerged as significant indicators for distinguishing between metastatic and lymphoma groups. Metabolomics revealed the difference in metabolic profiles among lymph nodes. We observed significant increases in many amino acids, organic acids, lipids, and nucleosides in both lymphoma and metastasis groups, compared to the benign group. Specifically, the lymphoma group exhibited higher levels of nucleotides (inosine monophosphate and adenosine diphosphate) as well as glutamic acid, and the metastasis group was characterized by higher levels of carbohydrates, acylcarnitines, glycerophospholipids, and uric acid. Linear discriminant analysis coupled with these metabolites could be used for differentiating lymph nodes, achieving recognition rates ranging from 87.4% to 89.3%, outperforming ultrasonography (63.1% to 75.4%).DiscussionOur findings could contribute to a better understanding of malignant lymph node development and provide novel targets for therapeutic interventions.https://www.frontiersin.org/articles/10.3389/fonc.2025.1510018/fulllymph nodeslymphomametastasisultrasonographymetabolomics |
| spellingShingle | Lu Li Xinyue Wang Hongyan Deng Wenjuan Lu Yasu Zhou Xinhua Ye Yong Li Jie Wang Discrimination of superficial lymph nodes using ultrasonography and tissue metabolomics coupled with machine learning Frontiers in Oncology lymph nodes lymphoma metastasis ultrasonography metabolomics |
| title | Discrimination of superficial lymph nodes using ultrasonography and tissue metabolomics coupled with machine learning |
| title_full | Discrimination of superficial lymph nodes using ultrasonography and tissue metabolomics coupled with machine learning |
| title_fullStr | Discrimination of superficial lymph nodes using ultrasonography and tissue metabolomics coupled with machine learning |
| title_full_unstemmed | Discrimination of superficial lymph nodes using ultrasonography and tissue metabolomics coupled with machine learning |
| title_short | Discrimination of superficial lymph nodes using ultrasonography and tissue metabolomics coupled with machine learning |
| title_sort | discrimination of superficial lymph nodes using ultrasonography and tissue metabolomics coupled with machine learning |
| topic | lymph nodes lymphoma metastasis ultrasonography metabolomics |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2025.1510018/full |
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