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Prognostic significance of collagen signatures at breast tumor boundary obtained by combining multiphoton imaging and imaging analysis

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Abstract

Purpose

Collagen features in breast tumor microenvironment is closely associated with the prognosis of patients. We aim to explore the prognostic significance of collagen features at breast tumor border by combining multiphoton imaging and imaging analysis.

Methods

We used multiphoton microscopy (MPM) to label-freely image human breast tumor samples and then constructed an automatic classification model based on deep learning to identify collagen signatures from multiphoton images. We recognized three kinds of collagen signatures at tumor boundary (CSTB I-III) in a small-scale, and furthermore obtained a CSTB score for each patient based on the combined CSTB I-III by using the ridge regression analysis. The prognostic performance of CSTB score is assessed by the area under the receiver operating characteristic curve (AUC), Cox proportional hazard regression analysis, as well as Kaplan-Meier survival analysis.

Results

As an independent prognostic factor, statistical results reveal that the prognostic performance of CSTB score is better than that of the clinical model combining three independent prognostic indicators, molecular subtype, tumor size, and lymph nodal metastasis (AUC, Training dataset: 0.773 vs. 0.749; External validation: 0.753 vs. 0.724; HR, Training dataset: 4.18 vs. 3.92; External validation: 4.98 vs. 4.16), and as an auxiliary indicator, it can greatly improve the accuracy of prognostic prediction. And furthermore, a nomogram combining the CSTB score with the clinical model is established for prognosis prediction and clinical decision making.

Conclusion

This standardized and automated imaging prognosticator may convince pathologists to adopt it as a prognostic factor, thereby customizing more effective treatment plans for patients.

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Data availability

All data generated or analyzed during this study were included in the article/Supplementary Materials. Further inquiry can be directed to the corresponding authors.

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Acknowledgements

We would like to thank all members of Chen lab for their suggestions and critical feedback.

Funding

This work was supported by the National Natural Science Foundation of China (Grant Nos. 82171991. 82172800), Natural Science Foundation of Fujian Province (Nos. 2023J01082, 2023J011125, 2020J01839), Joint Funds for the Innovation of Science and Technology of Fujian Province (2019Y9101).

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Authors and Affiliations

  1. Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, 350007, China

    Xingxin Huang, Xiahui Han, Liqin Zheng, Zhenlin Zhan, Shu Wang, Jianxin Chen & Lianhuang Li

  2. Department of Breast Surgery, Fujian Medical University Union Hospital, Fuzhou, 350001, China

    Fangmeng Fu, Wenhui Guo & Chuan Wang

  3. Department of Pathology, Fujian Medical University Union Hospital, Fuzhou, 350001, China

    Deyong Kang

  4. Department of Medical Oncology, Harbin Medical University Cancer Hospital, Harbin, 150081, China

    Qingyuan Zhang

  5. College of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, 350108, China

    Shu Wang

  6. School of Electronic and Mechanical Engineering, Fujian Polytechnic Normal University, Fuqing, 350300, China

    Shunwu Xu

  7. Department of Radiation Oncology, Harbin Medical University Cancer Hospital, Harbin, 150081, China

    Jianli Ma

  8. College of Physics and Electronic Information Engineering, Minjiang University, Fuzhou, 350108, China

    Lida Qiu

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  1. Xingxin Huang
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  2. Fangmeng Fu
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  8. Chuan Wang
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  10. Shu Wang
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  11. Shunwu Xu
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  12. Jianli Ma
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Contributions

Lianhuang Li, Lida Qiu and Jianxin Chen conceived the idea and supervised the study. Xingxin Huang, Xiahui Han, Liqin Zheng, Zhenlin Zhan, Shu Wang and Shunwu Xu performed multiphoton imaging. Fangmeng Fu, Wenhui Guo, Deyong Kang, Qingyuan Zhang, Jianli Ma and Chuan Wang were responsible for sample collection and preparation. Xingxin Huang, Lianhuang Li and Jianxin Chen conducted data analysis, and wrote or revised the manuscript. All the authors read and approved the final manuscript.

Corresponding authors

Correspondence to Jianli Ma, Lida Qiu, Jianxin Chen or Lianhuang Li.

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The authors declare no competing interests.

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This study was approved by the Institutional Review Board at each center in China (Fujian Medical University Union Hospital and Harbin Medical University Cancer Hospital).

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Xingxin Huang, Fangmeng Fu and Wenhui Guo contributed equally to this work.

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Huang, X., Fu, F., Guo, W. et al. Prognostic significance of collagen signatures at breast tumor boundary obtained by combining multiphoton imaging and imaging analysis. Cell Oncol. 47, 69–80 (2024). https://doi.org/10.1007/s13402-023-00851-4

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