A novel approach correlating pathologic complete response with digital pathology and radiomics in triple-negative breast cancer
Breast Cancer - Trang 1-7 - 2024
Tóm tắt
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.
Tài liệu tham khảo
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.
Bohr A, Memarzadeh K. The rise of artificial intelligence in healthcare applications. In: Artificial intelligence in healthcare. Amsterdam: Elsevier; 2020. p. 25–60.
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.
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.
Jahn SW, Plass M, Moinfar F. Digital pathology: advantages, limitations and emerging perspectives. J Clin Med. 2020;9(11):3697.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.