Digital Pathology as an Innovative Tool for Improving Cancer Diagnosis and Treatment

Authors

  • T. ZADVORNYI R.E. Kavetsky Institute of Experimental Pathology, Oncology, and Radiobiology, the NAS of Ukraine, Kyiv, Ukraine

DOI:

https://doi.org/10.15407/exp-oncology.2024.04.289

Abstract

For more than a century, the “gold” standard for diagnosing malignant neoplasms has been pathohistology. However, the continuous advancement of modern technologies is leading to a radical transformation of this field and the emergence of digital pathology. The main advantages of digital pathology include the convenience of the data storage and transfer, as well as the potential for automating diagnostic processes through the application of artificial intelligence technologies. Integrating digital pathology into clinical practice is expected to accelerate the analysis of histological samples, reduce the costs associated with such procedures, and enable the accumulation of large datasets for future scientific research. At the same time, the development of digital pathology faces certain challenges such as the need for technical upgrades in laboratories, ensuring data cybersecurity, and training qualified personnel.

References

Mattiuzzi C, Lippi G. Current cancer epidemiology. J Epidemiol Glob Health. 2019; 9:217-222. https://doi.org/10.2991/ jegh.k.191008.001

Sung H, Ferlay J, Siegel L, et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortali- ty worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians. 2021;71(3):209-249. https://doi. org/10.3322/caac.21660

Soerjomataram I, Bray F. Planning for tomorrow: global cancer incidence and the role of prevention 2020–2070.

Nat Rev Clin Oncol. 2021;18:663-672. https://doi.org/10.1038/s41571-021-00514-z

Hussain S, Mubeen I, Ullah N, et al. Modern diagnostic imaging technique applications and risk factors in the medi- cal field: a review. BioMed Res Int. 2022;2022(1):5164970. https://doi.org/10.1155/2022/5164970

Munir K, Elahi H, Ayub A, etal. Cancerdiagnosisusingdeeplearning: Abibliographicreview. Cancers. 2019;11(9):1235. https://doi.org/10.3390/cancers11091235

Baxi V, Edwards R, Montalto M, Saha S. Digital pathology and artificial intelligence in translational medicine and clinical practice. Mod Pathol. 2022;35(1):23-32. https://doi.org/10.1038/s41379-021-00919-2

Tseng LJ, Matsuyama A, MacDonald-Dickinson V. Histology: The gold standard for diagnosis? Can Vet J. 2023;64(4):389-391. PMID: 37008634

Lalibert SM, Poirier VJ, Pinard CJ, et al. A retrospective comparison of first and second opinion histopathology with patient outcomes in veterinary oncology cases (2011–2019). Vet Comp Oncol. 2022;20:198-206. https://doi. org/10.1111/vco.12762

Regan RC, Rassnick KM, Malone EK, McDonough SP. A prospective evaluation of the impact of second-opinion histopathology on diagnostic testing, cost and treatment in dogs and cats with cancer. Vet Comp Oncol. 2015;13:106- 116. https://doi.org/10.1111/vco.12023

He W, Liu T, Han Y, et al. A review: The detection of cancer cells in histopathology based on machine vision. Comput Biol Med. 2022;146:105636. https://doi.org/10.1016/j.compbiomed.2022.105636

Teot LA, Sposto R, Khayat A, et al. The problems and promise of central pathology review: development of a stan- dardized procedure for the Children's Oncology Group. Pediatr Dev Pathol. 2007;10(3):199-207. https://doi. org/10.2350/06-06-0121.1

Elmore JG, Longton GM, Carney PA, et al. Diagnostic concordance among pathologists interpreting breast biopsy specimens. JAMA. 2015;313(11):1122-1132. https://doi.org/10.1001/jama.2015.1405

Allen TC. Second opinions: pathologists' preventive medicine. Arch Pathol Lab Med. 2013;137(3):310-311. https:// doi.org/10.5858/arpa.2012-0512-ED

Jahn SW, Plass M, Moinfar F. Digital pathology: advantages, limitations and emerging perspectives. J Clin Med. 2020;9(11):3697. https://doi.org/10.3390/jcm9113697

Gross DJ, Robboy SJ, Cohen MB, et al. Strong job market for pathologists: results from the 2021 College of American Pa- thologists practice leader survey. Arch Pathol Lab Med. 2023;147(4):434-441. https://doi.org/10.5858/arpa.2022-0023-CP

Gratzinger D, Johnson KA, Brissette MD, et al. The recent pathology residency graduate job search experience: a syn- thesis of 5 years of College of American Pathologists job market surveys. Arch Pathol Lab Med. 2018;142(4):490-495. https://doi.org/10.5858/arpa.2017-0207-CP

Madabhushi A. Digital pathology image analysis: opportunities and challenges. Imaging Med. 2009;1(1):7-10. https:// doi.org/10.2217/IIM.09.9

Al‐Janabi S, Huisman A, Van Diest PJ. Digital pathology: current status and future perspectives. Histopathology. 2012;61(1):1-9. https://doi.org/10.1111/j.1365-2559.2011.03814.x

Schüffler PJ, Geneslaw L, Yarlagadda DVK, et al. Integrated digital pathology at scale: a solution for clinical diagnos- tics and cancer research at a large academic medical center. J Am Med Inform Assoc. 2021;28(9):1874-1884. https:// doi.org/10.1093/jamia/ocab085

Griffin J, Treanor D. Digital pathology in clinical use: where are we now and what is holding us back? Histopathology. 2017;70(1):134-145. https://doi.org/10.1111/his.12993

Dietz RL, Hartman DJ, Pantanowitz L. Systematic review of the use of telepathology during intraoperative consulta- tion. Am J Clin Pathol. 2020;153(2):198-209. https://doi.org/10.1093/ajcp/aqz155

Eccher A, Dei Tos AP, Scarpa A, et al. Cost analysis of archives in the pathology laboratories: from safety to manage- ment. J Clin Pathol. 2013;76(10):659-663. https://doi.org/10.1136/jcp-2023-209035

Smith MA, Barnes EL, Chiosea SI. Pathology archive: evaluation of integrity, regulatory compliance, and construc- tion of searchable database from print reports. Am J Clin Pathol. 2011;135(5):753-759. https://doi.org/10.1309/ AJCP3CVA2NAVUUVU

Bankhead P. Developing image analysis methods for digital pathology. J Pathol. 2022;257(4):391-402. https://doi. org/10.1002/path.5921

Chen JM, Li Y, Xu J, et al. Computer-aided prognosis on breast cancer with hematoxylin and eosin histopathology images: A review. Tumor Biol. 2017;39(3):1010428317694550. https://doi.org/10.1177/1010428317694550

Huang PW, Ouyang H, Hsu BY, Chang YR, Lin YC, Chen YA, Hsieh YH, Fu CC, Li CF, Lin CH, Lin YY, et al. Deep- learning based breast cancer detection for cross-staining histopathology images. Heliyon. 2023;9(2):e13171. https:// doi.org/10.1016/j.heliyon.2023.e13171

Heindl A, Nawaz S, Yuan Y. Mapping spatial heterogeneity in the tumor microenvironment: a new era for digital pathology. Lab Invest. 2015;95(4):377-384. https://doi.org/10.1038/labinvest.2014.155

Chen X, Song E. The theory of tumor ecosystem. Cancer Commun (Lond). 2022;42(7):587-608. https://doi. org/10.1002/cac2.12316

de Visser KE, Joyce JA. The evolving tumor microenvironment: From cancer initiation to metastatic outgrowth.

Cancer Cell. 2023;41(3):374-403. https://doi.org/10.1016/j.ccell.2023.02.016

Acs B, Ahmed FS, Gupta S, et al. An open source automated tumor infiltrating lymphocyte algorithm for prognosis in melanoma. Nat Commun. 2019;10(1):5440. https://doi.org/10.1038/s41467-019-13043-2

Verdicchio M, Brancato V, Cavaliere C, et al. A pathomic approach for tumor-infiltrating lymphocytes classification on breast cancer digital pathology images. Heliyon. 2023;9(3): e14371. https://doi.org/10.1016/j.heliyon.2023.e14371

Xu L, Walker B, Liang PI, et al. Colorectal cancer detection based on deep learning. J Path Inform. 2020;11(1):28. https://doi.org/10.4103/jpi.jpi_68_19

Saito A, Toyoda H, Kobayashi M, et al. Prediction of early recurrence of hepatocellular carcinoma after resection using digital pathology images assessed by machine learning. Modern Pathol. 2021;34(2):417-425. https://doi. org/10.1038/s41379-020-00671-z

Wang S, Rong R, Yang DM, et al. Computational staining of pathology images to study the tumor microenvironment in lung cancer. Cancer Res 2020;80(10):2056-2066. https://doi.org/10.1158/0008-5472.CAN-19-1629

Lukianova N, Zadvornyi T, Mushii O, et al. Evaluation of diagnostic algorithm based on collagen organization pa- rameters for breast tumors. Exp Oncol. 2022;44(4):281-286. https://doi.org/10.32471/exp-oncology.2312-8852.vol- 44-no-4.19137

Zadvornyi T, Lukianova N, Mushii O, et al. Benign and malignant prostate neoplasms show different spatial organi- zation of collagen. Croat Med J. 2023;64(6):413-420. https://doi.org/10.3325/cmj.2023.64.413.

Lukianova N, Mushii O, Zadvornyi T, Chekhun V. Development of an algorithm for biomedical image analysis of the spatial organization of collagen in breast cancer tissue of patients with different clinical status. FEBS Open Bio. 2024;14(4):675-686. https://doi.org/10.1002/2211-5463.13773

Li H, Bera K, Toro P, et al. Collagen fiber orientation disorder from H&E images is prognostic for early stage breast cancer: clinical trial validation. NPJ Breast Cancer. 2021;7:104. https://doi.org/10.1038/s41523-021-00310-z

Gole L, Yeong J, Lim JCT, et al. Quantitative stain-free imaging and digital profiling of collagen structure reveal diverse sur- vival of triple negative breast cancer patients. Breast Cancer Res. 2020;22:42. https://doi.org/10.1186/s13058-020-01282-x

Pallua JD, Brunner A, Zelger B, et al. The future of pathology is digital. Pathol Res Pract. 2020;216(9):153040. https:// doi.org/10.1016/j.prp.2020.153040

Mungenast F, Fernando A, Nica R, et al. Next-generation digital histopathology of the tumor microenvironment.

Genes. 2021;12(4):538. https://doi.org/10.3390/genes12040538

Tolkach Y, Wolgast LM, Damanakis A, et al. Artificial intelligence for tumour tissue detection and histological re- gression grading in oesophageal adenocarcinomas: a retrospective algorithm development and validation study. Lancet Digit Health. 2023;5(5):e265-e275. https://doi.org/10.1016/S2589-7500(23)00027-4

Sakamoto T, Furukawa T, Lami K, et al. A narrative review of digital pathology and artificial intelligence: focusing on lung cancer. Transl Lung Cancer Res. 2020;9(5):2255. https://doi.org/10.21037/tlcr-20-591

Downloads

Published

20.02.2025

How to Cite

ZADVORNYI, T. (2025). Digital Pathology as an Innovative Tool for Improving Cancer Diagnosis and Treatment. Experimental Oncology, 46(4), 289–294. https://doi.org/10.15407/exp-oncology.2024.04.289

Most read articles by the same author(s)

1 2 > >>