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Label-free navigation system for grading prostate tumour malignancy in situ via tissue pH and prostate-specific antigen activity

Nature Biomedical Engineering (2025)Cite this article

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Abstract

Radical prostatectomy is a standard curative approach for high-risk prostate cancer, yet accurately defining tumour margins during surgery remains a major challenge. Intraoperative assessment of prostate tumour malignancy—particularly those with high aggressiveness catalogued in Gleason grade group (GG) ≥ 3—is crucial to prevent positive surgical margins and minimize postoperative complications. Here we develop a surface-enhanced Raman scattering (SERS)-based navigation system for intraoperative localization of high-grade malignant regions by simultaneously accessing tissue acidity and prostate-specific antigen (PSA) enzymatic activity. This system integrates a sampling pen for automated biomarker extraction from tissue surfaces, a nano-imprinted SERS array producing a ratiometric Raman signal in response to acidity and PSA activity, and a two-dimensional deep-learning model for rapid Raman spectral interpretation. We show that the system can intraoperatively identify GG ≥ 3 malignancies in fresh prostate tissues from 144 Chinese patients with an area under the receiver operating characteristic curve of 0.89. This SERS-based navigation system holds strong potential to enhance surgical precision, minimize tumour residue and ultimately improve patient outcomes.

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Fig. 1: An on-site SERS navigation system for grading the malignancy of PCa.
Fig. 2: Sampling module enabling automatic sample extraction from tissue surfaces.
Fig. 3: Detection module measuring sample acidity and PSA activity simultaneously.
Fig. 4: A 2D-transformation-based deep-learning model for Raman spectral analysis.
Fig. 5: A consolidated standard for the examination of patients with PCa using the SERS navigation system.
Fig. 6: On-site navigation system for classifying the GG of prostate biopsies.
Fig. 7: The navigation system locates malignant regions in patient prostate sections.

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Data availability

The main data supporting the results of this study are available within the paper and its Supplementary Information. Source data are provided with this paper and available from figshare at https://doi.org/10.6084/m9.figshare.30001840 (ref. 58). Any other datasets generated and analysed during the study are available for research purposes from the corresponding authors upon request.

Code availability

The code supporting this study is available from https://github.com/zz9912/CNN_Raman_pH_detection.git.

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Acknowledgements

This work was supported by the National Key Research and Development Program of China (2023YFA1801200 to Cong Li), National Science Fund for Distinguished Young Scholars (82025019 to Cong Li), National Natural Science Foundation of China (82227806 and 92159304 to Cong Li and 62273099 to H.W.), Shanghai Health Commission Emerging Cross Disciplinary Research Project (2022JC003 to Cong Li), Science and Technology Commission of Shanghai Municipality (23TS1401100 and 23J21901700 to Cong Li), AI for Science Initiative of Fudan University (FudanX24AI049 to Changle Li and FudanX24AI038 to J.Y.), Postdoctoral Fellowship Program of the China Postdoctoral Science Foundation (grant GZC20251908 to Z.J.) and 2025 Medical Engineering Integration Project of Fudan University (to H.W.).

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Author notes

  1. These authors contributed equally: Ziyi Jin, Sihui Chen, Xiaoyan Dong, Zheng Zhao.

Authors and Affiliations

  1. Department of Urology, Zhongshan Hospital, Fudan University; School of Pharmacy, Key Laboratory of Smart Drug Delivery, Ministry of Education, Fudan University, Shanghai, China

    Ziyi Jin, Xiaoyan Dong, Hang Yin, Suhongrui Zhou, Hang Wang & Cong Li

  2. MOE Innovative Center for New Drug Development of Immune Inflammatory Diseases, State Key Laboratory of Brain Function and Disorders, Minhang Hospital, Xuhui Hospital, Shanghai Fifth People’s Hospital, Fudan University, Shanghai, China

    Ziyi Jin, Chang He, Jiaqi Huang, Jun Zhang, Jie Cheng, Hang Wang & Cong Li

  3. State Key Laboratory of Biomedical Imaging Science and System, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

    Sihui Chen, Huaiyang Zeng, Pengcheng Zhang, Changle Li, Hairong Zheng & Hui Yang

  4. College of Biomedical Engineering, Fudan University, Shanghai, China

    Zheng Zhao & Jinhua Yu

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Contributions

Z.J., H. Zheng, J.Y., H. Yang, H.W. and Cong Li conceived of and designed the research. Z.J., S.C., X.D., Z.Z., H. Zeng, P.Z., H. Yin and Changle Li performed the experiments. C.H., J.H. and J.C. provided clinical patient specimens. Z.J. and Z.Z. wrote the manuscript. Cong Li supervised the overall project. All authors discussed the results and assisted with editing the manuscript.

Corresponding authors

Correspondence to Hairong Zheng, Jinhua Yu, Hui Yang, Hang Wang or Cong Li.

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Nature Biomedical Engineering thanks Pola Goldberg Oppenheimer and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information (download PDF )

Supplementary Figs. 1–16, Tables 1–7, methods and results.

Reporting Summary (download PDF )

Supplementary Video 1 (download MP4 )

Video demonstrating the sampling module.

Supplementary Video 2 (download MP4 )

Video demonstrating the sampling pen tip.

Supplementary Video 3 (download MP4 )

Video demonstrating the detection module.

Supplementary Data 1 (download XLSX )

Patient information.

Supplementary Data 2 (download XLSX )

Source data for Supplementary Figs. 3, 6–8 and 11–16.

Source data

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Jin, Z., Chen, S., Dong, X. et al. Label-free navigation system for grading prostate tumour malignancy in situ via tissue pH and prostate-specific antigen activity. Nat. Biomed. Eng (2025). https://doi.org/10.1038/s41551-025-01561-y

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