The DOI matches its Crossref record; 56 of the 56 references it cites were found in a registry or an index.
Rapid, label-free histopathological diagnosis of liver cancer based on Raman spectroscopy and deep learning
Open accessAbstract
Biopsy is the recommended standard for pathological diagnosis of liver carcinoma. However, this method usually requires sectioning and staining, and well-trained pathologists to interpret tissue images. Here, we utilize Raman spectroscopy to study human hepatic tissue samples, developing and validating a workflow for in vitro and intraoperative pathological diagnosis of liver cancer. We distinguish carcinoma tissues from adjacent non-tumour tissues in a rapid, non-disruptive, and label-free manner by using Raman spectroscopy combined with deep learning, which is validated by tissue metabolomics. This technique allows for detailed pathological identification of the cancer tissues, including subtype, differentiation grade, and tumour stage. 2D/3D Raman images of unprocessed human tissue slices with submicrometric resolution are also acquired based on visualization of molecular composition, which could assist in tumour boundary recognition and clinicopathologic diagnosis. Lastly, the potential for a portable handheld Raman system is illustrated during surgery for real-time intraoperative human liver cancer diagnosis.
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A survey on deep learning in medical image analysis
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Raman Spectroscopy of Biological Tissues
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Global Burden of Disease Liver Cancer Collaboration, Akinyemiju, T., Abera, S., Ahmed, M., Alam, N., Alemayohu, M. A., Allen, C., Al-Raddadi, R., Alvis-Guzman, N., Amoako, Y., et al. (2017) · JAMA Oncology · doi:10.1001/jamaoncol.2017.3055 · Cited by 1,987
Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning
Ho, C.-S., Jean, N., Hogan, C. A., Blackmon, L., Jeffrey, S. S., Holodniy, M., Banaei, N., Saleh, A. A. E., Ermon, S., & Dionne, J. (2019) · Nature Communications · doi:10.1038/s41467-019-12898-9 · Cited by 871
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Deep learning and artificial intelligence methods for Raman and surface-enhanced Raman scattering
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Cited by
Zhang, Y., Chang, K., Ogunlade, B., Herndon, L., Tadesse, L. F., Kirane, A. R., & Dionne, J. A. (2024) · ACS Nano · doi:10.1021/acsnano.4c04282 · Cited by 83
Yang, Z., Tian, T., Kong, J., & Chen, H. (2025) · Analytical Chemistry · doi:10.1021/acs.analchem.4c06677 · Cited by 46
Metadata
- Authors
- Huang, L., Sun, H., Sun, L., Shi, K., Chen, Y., Ren, X., Ge, Y., Jiang, D., Liu, X., Knoll, W., Zhang, Q., & Wang, Y.
- Venue
- Nature Communications
- Published
- 2023-01-04
- Volume
- 14
- Issue
- 1
- Pages
- 48-48
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W4313546446
- Cited by
- 266
- References
- 56
- FWCI
- 29.60
- Type
- journal article
- Language
- en
- Open access
- gold
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- Huang, L., Sun, H., Sun, L., Shi, K., Chen, Y., Ren, X., Ge, Y., Jiang, D., Liu, X., Knoll, W., Zhang, Q., & Wang, Y. (2023). Rapid, label-free histopathological diagnosis of liver cancer based on Raman spectroscopy and deep learning. Nature Communications. https://doi.org/10.1038/s41467-022-35696-2
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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