A rapid and automated primary screening platform for breast cancer percutaneous biopsies

Abstract

In clinical practice, needle biopsy has emerged as a powerful tool in diagnosing numerous suspicious breast masses detected through medical imaging. However, a notable proportion of needle biopsies are negative and the disrupted tissue architectures lead to limited histological information, making the diagnostic tasks for pathologists repetitive and difficult. Herein, we develop a screening platform utilizing a three-dimensional histological electrophoresis (3DHE) device coupled with a data analysis algorithm to quickly and automatically (1) exclude negative samples and (2) flag suspicious regions, thereby optimizing the clinical pathology workload. The 3DHE technology is applied to separate the dye-labeled proteins within biopsy tissue sections along the z-direction, allowing for a selected protein combination that provides maximum contrast for cancer tissue identification. Meanwhile, the developed algorithm can automatically sort samples into the exclusion group and the pending group based on postelectrophoresis protein signals. We accurately diagnose all malignant needle biopsies in 34 breast cancer cases. Furthermore, by weighting algorithm filters, our platform successfully pre-excludes 81.8% of negative samples without generating any false negatives. Our platform can act as a potent supplementary tool for evaluating breast cancer needle biopsies, serving as a preliminary screening step before pathological analysis.

Keywords

breast cancer / needle biopsy / precision medicine / rapid biopsy analysis / three-dimensional histological electrophoresis

References

[1] Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74(3):229-263. https://doi.org/10.3322/caac.21834

[2] Smith RA, Andrews KS, Brooks D, et al. Cancer screening in the United States, 2019: a review of current American Cancer Society guidelines and current issues in cancer screening. CA Cancer J Clin. 2019;69(3):184-210. https://doi.org/10.3322/caac.21557

[3] Teberian I, Kaufman T, Shames J, et al. Trends in the use of percutaneous versus open surgical breast biopsy: an update. J Am Coll Radiol. 2020;17(8):1004-1010. https://doi.org/10.1016/j.jacr.2020.02.015

[4] Voskuil FJ, Vonk J, van der Vegt B, et al. Intraoperative imaging in pathology-assisted surgery. Nat Biomed Eng. 2022;6(5):503-514. https://doi.org/10.1038/s41551-021-00808-8

[5] a) Pradipta AR, Tanei T, Morimoto K, Shimazu K, Noguchi S, Tanaka K. Emerging technologies for real-time intraoperative margin assessment in future breast-conserving surgery. Adv Sci. 2020;7(9):1901519. https://doi.org/10.1002/advs.201901519; b) Tamirisa NP, Sheffield KM, Parmar AD, et al. Surgeon and facility variation in the use of minimally invasive breast biopsy in Texas. Ann Surg. 2015;262(1):171-178. https://doi.org/10.1097/sla.0000000000000883

[6] Wekking D, Porcu M, De Silva P, Saba L, Scartozzi M, Solinas C. Breast MRI: clinical indications, recommendations, and future applications in breast cancer diagnosis. Curr Oncol Rep. 2023;25(4):257-267. https://doi.org/10.1007/s11912-023-01372-x

[7] Elmore JG, Longton GM, Carney PA, et al. Diagnostic concordance among pathologists interpreting breast biopsy specimens. JAMA. 2015;313(11):1122. https://doi.org/10.1001/jama.2015.1405

[8] a) Li MY, Lu ZT, Zhang JX, et al. Near-infrared-II fluorophore with inverted dependence of fluorescence quantum yield on polarity as potent phototheranostics for fluorescence-image-guided phototherapy of tumors. Adv Mater. 2023;35(45):2209647. https://doi.org/10.1002/adma.202209647; b) Tian R, Feng X, Wei L, et al. A genetic engineering strategy for editing near-infrared-II fluorophores. Nat Commun. 2022;13(1):2853. https://doi.org/10.1038/s41467-022-30304-9

[9] a) Du YJ, Xu JJ, Zheng X, et al. NIR-II protein-escaping dyes enable high-contrast and long-term prognosis evaluation of flap transplantation. Adv Mater. 2024;36(14):2311515. https://doi.org/10.1002/adma.202311515; b) Xu J, Zhu N, Du Y, et al. Biomimetic NIR-II fluorescent proteins created from chemogenic protein-seeking dyes for multicolor deep-tissue bioimaging. Nat Commun. 2024;15(1):2845. https://doi.org/10.1038/s41467-024-47063-4

[10] a) Zhang F, Xu J, Yue Y, et al. Three-dimensional histological electrophoresis enables fast automatic distinguishment of cancer margins and lymph node metastases. Sci Adv. 2023;9(26):eadg2690. https://doi.org/10.1126/sciadv.adg2690; b) Zhang F, Xu J, Zhang C, et al. Three-dimensional histological electrophoresis for high-throughput cancer margin detection in multiple types of tumor specimens. Nano Lett. 2023;23(16):7607-7614.

[11] a) Nolan E, Lindeman GJ, Visvader JE. Deciphering breast cancer: from biology to the clinic. Cell. 2023;186(8):1708-1728. https://doi.org/10.1016/j.cell.2023.01.040; b) Harbeck N, Gnant M. Breast cancer. Lancet. 2017;389(10074):1134-1150. https://doi.org/10.1016/s0140-6736(16)31891-8

[12] a) Laemmli UK. Cleavage of structural proteins during the assembly of the head of bacteriophage T4. Nature. 1970;227(5259):680-685. https://doi.org/10.1038/227680a0; b) Ogden RC, Adams DA. Electrophoresis in agarose and acrylamide gels. Methods Enzymol. 1987;152:61.

[13] Li Y, Porta-Pardo E, Tokheim C, et al. Pan-cancer proteogenomics connects oncogenic drivers to functional states. Cell. 2023;186(18):3921-3944.e25. https://doi.org/10.1016/j.cell.2023.07.014

[14] Wang YJ, Zhang FR, Jia YL, et al. Tumor receptor-seeking dyes for rapid intraoperative definition of tumor margin and histopathological morphology. CCS Chem. 2024;6(10):2577-2593. https://doi.org/10.31635/ccschem.024.202303659

[15] Kroll AV, Fang RH, Jiang Y, et al. Nanoparticulate delivery of cancer cell membrane elicits multiantigenic antitumor immunity. Adv Mater. 2017;29(47):1703969. https://doi.org/10.1002/adma.201703969

[16] a) Guo R, Lu G, Qin B, Fei B. Ultrasound imaging technologies for breast cancer detection and management: a review. Ultrasound Med Biol. 2018;44(1):37-70. https://doi.org/10.1016/j.ultrasmedbio.2017.09.012; b) Bamber J, Cosgrove D, Dietrich CF, et al. EFSUMB guidelines and recommendations on the clinical use of ultrasound elastography. Part 1: basic principles and technology. Ultraschall Med. 2013;34(2):169-184. https://doi.org/10.1055/s-0033-1335205; c) Dahlstrom JE, Sutton S, Jain S. Histological precision of stereotactic core biopsy in diagnosis of malignant and premalignant breast lesions. Histopathology. 1996;28(6):537-541. https://doi.org/10.1046/j.1365-2559.1996.d01-463.x

[17] a) Bulten W, Pinckaers H, van Boven H, et al. Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study. Lancet Oncol. 2020;21(2):233-241. https://doi.org/10.1016/s1470-2045(19)30739-9; b) Bejnordi BE, Veta M, van Diest PJ, et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA. 2017;318:2199.

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2025 The Author(s). Journal of Intelligent Medicine published by John Wiley & Sons Australia, Ltd on behalf of Tianjin University.