AI-assisted SERS imaging method for label-free and rapid discrimination of clinical lymphoma
Abstract Background Lymphoma is a malignant tumor of the immune system and its incidence is increasing year after year, causing a major threat to people's health. Conventional diagnosis of lymphoma basically depends on histological images consuming long-time and tedious manipulations (e.g., 7–1...
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BMC
2025-04-01
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| Series: | Journal of Nanobiotechnology |
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| Online Access: | https://doi.org/10.1186/s12951-025-03339-5 |
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| author | Haiting Cao Xiaofeng Wu Huayi Shi Binbin Chu Yao He Houyu Wang Fenglin Dong |
| author_facet | Haiting Cao Xiaofeng Wu Huayi Shi Binbin Chu Yao He Houyu Wang Fenglin Dong |
| author_sort | Haiting Cao |
| collection | DOAJ |
| description | Abstract Background Lymphoma is a malignant tumor of the immune system and its incidence is increasing year after year, causing a major threat to people's health. Conventional diagnosis of lymphoma basically depends on histological images consuming long-time and tedious manipulations (e.g., 7–15 days) and large-field view (e.g., > 1000 × 1000 μm2). Artificial intelligence has recently revolutionized cancer diagnosis by training pathological image databases via deep learning. Current approaches, however, remain dependent on analyzing wide-field pathological images to detect distinct nuclear, cytologic, and histomorphologic traits for diagnostic categorization, limiting their applicability to minimally invasive lesion. Results Herein, we develop a molecular imaging strategy for minimally invasive lymphoma diagnosis. By spreading lymphoma tissue sections tightly on a surface-enhanced Raman scattering (SERS) chip, label-free images of DNA double strand breaks (DSBs) in 30 × 30 μm2 tissue sections could be achieved in ~ 15 min. To establish a proof of concept, the Raman image datasets collected from clinical samples of normal lymphatic tissues and non-Hodgkin's lymphoma (NHL) tissues were well organized and trained in a deep convolutional neural network model, finally achieving a recognition rate of ~ 91.7 ± 2.1%. Conclusions The molecular imaging strategy for minimally invasive lymphoma diagnosis that can achieve a recognition rate of ~ 91.7 ± 2.1%. We anticipate that these results will catalyze the development of a series of histological SERS-AI technologies for diagnosing various diseases, including other types of cancer. In this work, we present a reliable tool to facilitate clinicians in the diagnosis of lymphoma. Graphical Abstract |
| format | Article |
| id | doaj-art-8655a7f631aa4b51995802ff515052bb |
| institution | DOAJ |
| issn | 1477-3155 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | BMC |
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| series | Journal of Nanobiotechnology |
| spelling | doaj-art-8655a7f631aa4b51995802ff515052bb2025-08-20T03:18:42ZengBMCJournal of Nanobiotechnology1477-31552025-04-0123111110.1186/s12951-025-03339-5AI-assisted SERS imaging method for label-free and rapid discrimination of clinical lymphomaHaiting Cao0Xiaofeng Wu1Huayi Shi2Binbin Chu3Yao He4Houyu Wang5Fenglin Dong6Suzhou Key Laboratory of Nanotechnology and Biomedicine, Institute of Functional Nano & Soft Materials (FUNSOM), and Collaborative Innovation Center of Suzhou Nano Science and Technology (NANO-CIC), Soochow UniversityDepartment of Ultrasound, The First Affiliated Hospital of Soochow UniversitySuzhou Key Laboratory of Nanotechnology and Biomedicine, Institute of Functional Nano & Soft Materials (FUNSOM), and Collaborative Innovation Center of Suzhou Nano Science and Technology (NANO-CIC), Soochow UniversitySuzhou Key Laboratory of Nanotechnology and Biomedicine, Institute of Functional Nano & Soft Materials (FUNSOM), and Collaborative Innovation Center of Suzhou Nano Science and Technology (NANO-CIC), Soochow UniversitySuzhou Key Laboratory of Nanotechnology and Biomedicine, Institute of Functional Nano & Soft Materials (FUNSOM), and Collaborative Innovation Center of Suzhou Nano Science and Technology (NANO-CIC), Soochow UniversitySuzhou Key Laboratory of Nanotechnology and Biomedicine, Institute of Functional Nano & Soft Materials (FUNSOM), and Collaborative Innovation Center of Suzhou Nano Science and Technology (NANO-CIC), Soochow UniversityDepartment of Ultrasound, The First Affiliated Hospital of Soochow UniversityAbstract Background Lymphoma is a malignant tumor of the immune system and its incidence is increasing year after year, causing a major threat to people's health. Conventional diagnosis of lymphoma basically depends on histological images consuming long-time and tedious manipulations (e.g., 7–15 days) and large-field view (e.g., > 1000 × 1000 μm2). Artificial intelligence has recently revolutionized cancer diagnosis by training pathological image databases via deep learning. Current approaches, however, remain dependent on analyzing wide-field pathological images to detect distinct nuclear, cytologic, and histomorphologic traits for diagnostic categorization, limiting their applicability to minimally invasive lesion. Results Herein, we develop a molecular imaging strategy for minimally invasive lymphoma diagnosis. By spreading lymphoma tissue sections tightly on a surface-enhanced Raman scattering (SERS) chip, label-free images of DNA double strand breaks (DSBs) in 30 × 30 μm2 tissue sections could be achieved in ~ 15 min. To establish a proof of concept, the Raman image datasets collected from clinical samples of normal lymphatic tissues and non-Hodgkin's lymphoma (NHL) tissues were well organized and trained in a deep convolutional neural network model, finally achieving a recognition rate of ~ 91.7 ± 2.1%. Conclusions The molecular imaging strategy for minimally invasive lymphoma diagnosis that can achieve a recognition rate of ~ 91.7 ± 2.1%. We anticipate that these results will catalyze the development of a series of histological SERS-AI technologies for diagnosing various diseases, including other types of cancer. In this work, we present a reliable tool to facilitate clinicians in the diagnosis of lymphoma. Graphical Abstracthttps://doi.org/10.1186/s12951-025-03339-5SERSConvolutional neural networkLymphomaMinimally invasive diagnosis |
| spellingShingle | Haiting Cao Xiaofeng Wu Huayi Shi Binbin Chu Yao He Houyu Wang Fenglin Dong AI-assisted SERS imaging method for label-free and rapid discrimination of clinical lymphoma Journal of Nanobiotechnology SERS Convolutional neural network Lymphoma Minimally invasive diagnosis |
| title | AI-assisted SERS imaging method for label-free and rapid discrimination of clinical lymphoma |
| title_full | AI-assisted SERS imaging method for label-free and rapid discrimination of clinical lymphoma |
| title_fullStr | AI-assisted SERS imaging method for label-free and rapid discrimination of clinical lymphoma |
| title_full_unstemmed | AI-assisted SERS imaging method for label-free and rapid discrimination of clinical lymphoma |
| title_short | AI-assisted SERS imaging method for label-free and rapid discrimination of clinical lymphoma |
| title_sort | ai assisted sers imaging method for label free and rapid discrimination of clinical lymphoma |
| topic | SERS Convolutional neural network Lymphoma Minimally invasive diagnosis |
| url | https://doi.org/10.1186/s12951-025-03339-5 |
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