Skip to main content

Advertisement

SpringerLink
Log in

A novel approach correlating pathologic complete response with digital pathology and radiomics in triple-negative breast cancer

  • Rapid Communication
  • Published:
Breast Cancer Aims and scope Submit manuscript

Abstract

This rapid communication highlights the correlations between digital pathology—whole slide imaging (WSI) and radiomics—magnetic resonance imaging (MRI) features in triple-negative breast cancer (TNBC) patients. The research collected 12 patients who had both core needle biopsy and MRI performed to evaluate pathologic complete response (pCR). The results showed that higher collagenous values in pathology data were correlated with more homogeneity, whereas higher tumor expression values in pathology data correlated with less homogeneity in the appearance of tumors on MRI by size zone non-uniformity normalized (SZNN). Higher myxoid values in pathology data are correlated with less similarity of gray-level non-uniformity (GLN) in tumor regions on MRIs, while higher immune values in WSIs correlated with the more joint distribution of smaller-size zones by small area low gray-level emphasis (SALGE) in the tumor regions on MRIs. Pathologic complete response (pCR) was associated with collagen, tumor, and myxoid expression in WSI and GLN and SZNN in radiomic features. The correlations of WSI and radiomic features may further our understanding of the TNBC tumoral microenvironment (TME) and could be used in the future to better tailor the use of neoadjuvant chemotherapy (NAC). This communication will focus on the post-NAC MRI features correlated with pCR and their association with WSI features from core needle biopsies.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Data availability

All data will be provided by the corresponding author upon reasonable request.

References

  1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209–49.

    Article  PubMed  Google Scholar 

  2. Bohr A, Memarzadeh K. The rise of artificial intelligence in healthcare applications. In: Artificial intelligence in healthcare. Amsterdam: Elsevier; 2020. p. 25–60.

    Chapter  Google Scholar 

  3. Yousif M, van Diest PJ, Laurinavicius A, Rimm D, van der Laak J, Madabhushi A, Schnitt S, Pantanowitz L. Artificial intelligence applied to breast pathology. Virchows Arch. 2022;480(1):191–209.

    Article  PubMed  Google Scholar 

  4. Bera K, Braman N, Gupta A, Velcheti V, Madabhushi A. Predicting cancer outcomes with radiomics and artificial intelligence in radiology. Nat Rev Clin Oncol. 2022;19(2):132–46.

    Article  CAS  PubMed  Google Scholar 

  5. Jahn SW, Plass M, Moinfar F. Digital pathology: advantages, limitations and emerging perspectives. J Clin Med. 2020;9(11):3697.

    Article  PubMed  PubMed Central  Google Scholar 

  6. DA Aysola K, Welch C, Xu J, Qin Y, Reddy V, Matthews R, Owens C, Okoli J, Beech D, Piyathilake C, Reddy S, Rao V. Triple negative breast cancer—an overview. Hereditary Genet. 2013;2013:001.

    PubMed  PubMed Central  Google Scholar 

  7. Adir O, Poley M, Chen G, Froim S, Krinsky N, Shklover J, Shainsky-Roitman J, Lammers T, Schroeder A. Integrating artificial intelligence and nanotechnology for precision cancer medicine. Adv Mater. 2020;32(13): e1901989.

    Article  PubMed  Google Scholar 

  8. Hacking SM, Karam J, Singh K, GamsizUzun ED, Brickman A, Yakirevich E, Taliano R, Wang Y. Whole slide image features predict pathologic complete response and poor clinical outcomes in triple-negative breast cancer. Pathol Res Practice. 2023;246:154476.

    Article  CAS  Google Scholar 

  9. Bankhead P, Loughrey MB, Fernández JA, Dombrowski Y, McArt DG, Dunne PD, McQuaid S, Gray RT, Murray LJ, Coleman HG, et al. QuPath: open source software for digital pathology image analysis. Sci Rep. 2017;7(1):16878.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Wu D, Hacking SM, Chavarria H, Abdelwahed M, Nasim M. Computational portraits of the tumoral microenvironment in human breast cancer. Virchows Arch. 2022;481(3):367–85.

    Article  CAS  PubMed  Google Scholar 

  11. Yushkevich PA, Piven J, Hazlett HC, Smith RG, Ho S, Gee JC, Gerig G. User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage. 2006;31(3):1116–28.

    Article  PubMed  Google Scholar 

  12. Jiao Z, Li H, Xiao Y, Aggarwal C, Galperin-Aizenberg M, Pryma D, Simone CB 2nd, Feigenberg SJ, Kao GD, Fan Y. Integration of risk survival measures estimated from pre- and posttreatment computed tomography scans improves stratification of patients with early-stage non-small cell lung cancer treated with stereotactic body radiation therapy. Int J Radiat Oncol Biol Phys. 2021;109(5):1647–56.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Sun K, Jiao Z, Zhu H, Chai W, Yan X, Fu C, Cheng JZ, Yan F, Shen D. Radiomics-based machine learning analysis and characterization of breast lesions with multiparametric diffusion-weighted MR. J Transl Med. 2021;19(1):443.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Jiao Z, Li H, Xiao Y, Dorsey J, Simone CB, Feigenberg S, Kao G, Fan Y. Integration of deep learning radiomics and counts of circulating tumor cells improves prediction of outcomes of early stage NSCLC patients treated with stereotactic body radiation therapy. Int J Radiat Oncol Biol Phys. 2022;112(4):1045–54.

    Article  PubMed  Google Scholar 

  15. de Kruijf EM, van Nes JG, van de Velde CJ, Putter H, Smit VT, Liefers GJ, Kuppen PJ, Tollenaar RA, Mesker WE. Tumor-stroma ratio in the primary tumor is a prognostic factor in early breast cancer patients, especially in triple-negative carcinoma patients. Breast Cancer Res Treat. 2011;125(3):687–96.

    Article  PubMed  Google Scholar 

  16. Chang H, Kang Y, Ahn JM, Lee E, Lee JW, Kang HS. Texture analysis of magnetic resonance image to differentiate benign from malignant myxoid soft tissue tumors: a retrospective comparative study. PLoS ONE. 2022;17(5): e0267569.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Su G-H, Xiao Y, Jiang L, Zheng R-C, Wang H, Chen Y, Gu Y-J, You C, Shao Z-M. Radiomics features for assessing tumor-infiltrating lymphocytes correlate with molecular traits of triple-negative breast cancer. J Transl Med. 2022;20(1):471.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Xu N, Zhou J, He X, Ye S, Miao H, Liu H, Chen Z, Zhao Y, Pan Z, Wang M. Radiomics model for evaluating the level of tumor-infiltrating lymphocytes in breast cancer based on dynamic contrast-enhanced MRI. Clin Breast Cancer. 2021;21(5):440–9.e441.

    Article  PubMed  Google Scholar 

  19. Buisseret L, Garaud S, de Wind A, Van den Eynden G, Boisson A, Solinas C, Gu-Trantien C, Naveaux C, Lodewyckx JN, Duvillier H, et al. Tumor-infiltrating lymphocyte composition, organization and PD-1/PD-L1 expression are linked in breast cancer. Oncoimmunology. 2017;6(1): e1257452.

    Article  PubMed  Google Scholar 

  20. Lo Gullo R, Wen H, Reiner JS, Hoda R, Sevilimedu V, Martinez DF, Thakur SB, Jochelson MS, Gibbs P, Pinker K. Assessing PD-L1 expression status using radiomic features from contrast-enhanced breast MRI in breast cancer patients: initial results. Cancers (Basel). 2021;13(24):6273.

    Article  CAS  PubMed  Google Scholar 

  21. Abousamra S, Gupta R, Hou L, Batiste R, Zhao T, Shankar A, Rao A, Chen C, Samaras D, Kurc T, et al. Deep learning-based mapping of tumor infiltrating lymphocytes in whole slide images of 23 types of cancer. Front Oncol. 2022. https://doi.org/10.3389/fonc.2021.806603.

    Article  PubMed  PubMed Central  Google Scholar 

  22. McAnena P, Moloney BM, Browne R, O’Halloran N, Walsh L, Walsh S, Sheppard D, Sweeney KJ, Kerin MJ, Lowery AJ. A radiomic model to classify response to neoadjuvant chemotherapy in breast cancer. BMC Med Imaging. 2022;22(1):225.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Caballo M, Sanderink WBG, Han L, Gao Y, Athanasiou A, Mann RM. Four-dimensional machine learning radiomics for the pretreatment assessment of breast cancer pathologic complete response to neoadjuvant chemotherapy in dynamic contrast-enhanced MRI. J Magn Reson Imaging. 2023;57(1):97–110.

    Article  PubMed  Google Scholar 

  24. Cui H, Sun Y, Zhao D, Zhang X, Kong H, Hu N, Wang P, Zuo X, Fan W, Yao Y, et al. Radiogenomic analysis of prediction HER2 status in breast cancer by linking ultrasound radiomic feature module with biological functions. J Transl Med. 2023;21(1):44.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We would like to thank our departments for their support and dedication.

Author information

Authors and Affiliations

  1. Department of Pathology, NYU Grossman School of Medicine, New York, NY, USA

    Sean M. Hacking

  2. Department of Pathology and Laboratory Medicine, Warren Alpert Medical School of Brown University, Providence, RI, USA

    Sean M. Hacking & Yihong Wang

  3. Warren Alpert Medical School of Brown University, Providence, RI, USA

    Gabrielle Windsor

  4. Department of Radiology, Warren Alpert Medical School of Brown University, Providence, RI, USA

    Robert Cooper, Zhicheng Jiao & Ana Lourenco

Authors
  1. Sean M. Hacking
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gabrielle Windsor
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Robert Cooper
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhicheng Jiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ana Lourenco
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yihong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sean M. Hacking.

Ethics declarations

Conflict of interest

Author SH has equity ownership in Odyssey HealthCare Solutions Inc. The remaining authors have no remaining possible conflicts of interest to disclose.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hacking, S.M., Windsor, G., Cooper, R. et al. A novel approach correlating pathologic complete response with digital pathology and radiomics in triple-negative breast cancer. Breast Cancer 31, 529–535 (2024). https://doi.org/10.1007/s12282-024-01544-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12282-024-01544-y

Keywords

Search

Navigation