DIGITAL PATHOLOGY IN MELANOMA: ACHIEVEMENTS, BARRIERS AND PROSPECTS

Authors

DOI:

https://doi.org/10.11603/mie.1996-1960.2022.4.13411

Keywords:

digital pathology, artificial intelligence, neural networks, malignant neoplasm, melanoma

Abstract

Background. This review is focused on the assessment of the current state of development and implementation of digital pathology in pathologists practice for better diagnostics, counseling, and personalization of melanoma treatment.

Materials and methods. The data concerning the digital pathology tools used for melanoma diagnostics and prognostic/ predictive biomarkers assessment were extracted and analysed.

Results. The convergence of digital pathology and artificial intelligence has led to a paradigm shift in pathologists' practice. Thanks to digital pathology, pathologists got the opportunity to improve the accuracy, efficiency and consistency of melanoma diagnosis. Access to digital tools with assessing whole slide images facilitated improvement of the remote primary diagnostics, provision of teleconsultations, increased efficiency and balance of workload, improves collaboration between general pathologists and dermatopathologists, flourished virtual education and innovative studies. Detection of sub-visual morphometric features and integration of multi-omics data are prerequisites for improving prognostic and predictive information for personalizing the treatment of melanoma patients, which discovers new prospects for precision medicine.

Conclusions. Despite the progress in digital pathology, the implementation of artificial intelligence in diagnostic algorithms of pathologists and personalized treatment requires to solve a number of challenges related to the development and clinical validation of digital tools.

References

Acs, B., Rimm, D. L. (2018). Not just digital pathology, intelligent digital pathology. JAMA Oncol., 4(3), 403-404.

Alheejawi, S., Xu, H., Berendt, R., Jha, N., Mandal, M. (2019). Novel lymph node segmentation and proliferation index measurement for skin melanoma biopsy images. Comput Med Imaging Graph., 73, 19-29.

Anwa, S. M., Majid, M., Qayyum, A., Awais, M., Alnowami, M., Khan, M. K. (2018). Medical image analysis using convolutional neural networks: a review. J Med Syst., 42, 1-13.

Article 89 GDPR: Safeguards and derogations relating to processing for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes. General Data Protection Regulation (GDPR). URL: https://gdpr-info.eu/art-89-gdpr/.

Cereceda, K., Bravo, N., Jorquera, R., Gonzalez-Stegmaier, R., Villarroel-Esprndola, F. (2022). Simultaneous and Spatially-Resolved Analysis of T-Lymphocytes, Macrophages and PD-L1 Immune Checkpoint in Rare Cancers. Cancers (Basel)., 14(11), 2815.

Chou, M., Illa-Bochaca, I., Minxi, B., Darvishian, F., Johannet, P., Moran, U., et al. (2021). Optimization of an automated tumor-infiltrating lymphocyte algorithm for improved prognostication in primary melanoma. Mod Pathol., 34(3), 562-571.

De Logu, F., Ugolini, F., Maio, V., Simi, S., Cossu, A., Massi, D. (2020). Italian Association for Cancer Research (AIRC) Study Group. Nassini, R., Laurino, M. Recognition of Cutaneous Melanoma on Digitized Histopathological Slides via Artificial Intelligence Algorithm. Front Oncol., 10, 1559.

De Smet, F., Antoranz Martinez, A., Bosisio, F. M. (2020). Next-Generation Pathology by Multiplexed Immunohistochemistry. Trends Biochem, 46 (1), 8082.

Dimitriou, N., Arandjelovi®, O., Caie, P. D. (2019). Deep learning for whole slide image analysis: an overview. Front Med., 6, 264.

Dudin, O., Mintser, O., Sulaieva, O. (2021). Artificial intelligence and next generation pathology: towards personalized medicine. Proc Shevchenko Sci Soc Med Sci. doi:10.25040/ntsh2021.02.07.

Elder, D. E., Piepkorn, M. W., Barnhill, R. L., Longton, G. M. et al. (2018). Pathologist characteristics associated with accuracy and reproducibility of melanocytic skin lesion interpretation. J Am Acad Dermatol., 79(1), 52-59.

Elmore, J. G., Barnhill, R. L., Elder, D. E., Longton, G. M. et al. (2017). Pathologists' diagnosis of invasive melanoma and melanocytic proliferations: Observer accuracy and reproducibility study. BMJ, 357, 2813.

Esteva, A., Kuprel, B., Novoa, R. A., Ko, J. et al. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542, 115118.

Esteva, A., Topol, E. (2019). Can skin cancer diagnosis be transformed by AI? Lancet, 394(10211), 1795.

Evans, A. J., Bauer, T. W., Bui, M. M., Cornish, T. C. et al. (2018). Food and Drug Administration Approval of Whole Slide Imaging for Primary Diagnosis: A Key Milestone Is Reached and New Questions Are Raised. Arch Pathol Lab Med., 142(11), 1383-1387.

Farmer, E. R., Gonin, R., Hanna, M. P. (1996). Discordance in the histopathologic diagnosis of melanoma and melanocytic nevi between expert pathologists. Hum Pathol., 27(6), 528-531.

Gao, J., Jiang, Q., Zhou, B., Chen, D. (2019). Convolutional neural networks for computer-aided detection or diagnosis in medical image analysis: an overview. Math Biosci Eng., 16, 6536-6561.

GeoMx Digital Spatial Profiling - NanoString Technologies. URL: https://www.nanostring.com/products/geomx-digital-spatial-profiler/geomx-dsp.

Goltsev, Y., Samusik, N., Kennedy-Darling, J., Bhate, S., et al. (2018). Deep Profiling of Mouse Splenic Architecture with CODEX Multiplexed Imaging. Cell., 174(4), 968-981.

Govek, K. W., Troisi, E. C., Miao, Z., Woodhouse, S., Camara, P. G. (2020). Single-Cell Transcriptomic Analysis of mIHC Images via Antigen Mapping. doi: 10.1126/sciadv.abc5464.

Halse, H., Colebatch, A. J., Petrone, P., Henderson, M. A. et al. (2018). Multiplex immunohistochemistry accurately defines the immune context of metastatic melanoma. Sci Rep., 8(1), 11158.

Han, T., Liu, C., Yang, W., Jiang, D. (2019). Learning transferable features in deep convolutional neural networks for diagnosing unseen machine conditions. ISA Trans., 93, 341-353.

Hekler, A., Utikal, J. S., Enk, A. H., Berking, C. et al. (2019). Pathologist-level classification of histopathological melanoma images with deep neural networks. Eur J Cancer., 115, 79-83.

Hekler, A., Utikal, J. S., Enk, A. H., Solass, W. et al. (2019). Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images. Eur J Cancer, 118, 91-96.

HistoGeneX. Histo Highlights (2016). URL: https://www.histogenex.com/images/PDFs/Histo-Highlights-July-2016-HistoGeneX-Newsletter.pdf.

Ianni, J. D., Soans, R. E., Sankarapandian, S., Chamarthi, R. V., Ayyagari, D., Olsen, T. G. et al. (2020). A Whole Slide Image Classification System Validated on Uncurated Multi-Site Data Emulating the Prospective Pathology Workload. Sci Rep., 10(1), 3217.

Kent, M. N., Olsen, T. G., Feeser, T. A., Tesno, K. C., Moad, J. C, Conroy, M. P. et al. (2017). Diagnostic Accuracy of Virtual Pathology vs Traditional Microscopy in a Large Dermatopathology Study. JAMA Dermatol., 153(12), 1285-1291.

Khosravi, P., Kazemi, E., Imielinski, M., Elemento. O., Hajirasouliha, I. (2018). Deep convolutional neuralnetworks enable discrimination of heterogeneous digital pathology images. EBioMedicine., 27, 317-328.

Komura, D., Ishikawa, S. (2019). Machine learning approaches for pathologic diagnosis. Virchows Arch., 475, 131-138.

Kucharski, D., Kleczek, P., Jaworek-Korjakowska, J., Dyduch, G., Gorgon, M. (2020). Semi-Supervised Nests of Melanocytes Segmentation Method Using Convolutional Autoencoders. Sensors (Basel)., 20(6), 1546.

Kulkarni, S., Seneviratne, N., Baig, M. S., Khan,

A. H. A. (2020). Artificial intelligence in medicine: where are we now? Acad Radiol., 27, 62-70.

Lee, J. H., Daugharthy, E. R., Scheiman, J., Kalhor, R., Yang, J. L., Ferrante, T. C. et al. (2014). Highly multiplexed subcellular RNA sequencing in situ. Science., 343(6177), 1360-1363.

Madabhushi, A., Lee, G. (2016). Image analysis and machine learning in digital pathology: challenges and opportunities. Med Image Anal., 33, 170-175.

Meroueh, C., Chen, Z. E. (2022). Artificial intelligence in anatomical pathology: building a strong foundation for precision medicine. Hum Pathol. doi: 10.1016/j.humpath.2022.07.008.

Merritt, C. R., Ong, G. T., Church, S. E., Barker, K., Danaher, P., Geiss, G. et al. (2020). Multiplex digital spatial profiling of proteins and RNA in fixed tissue. Nat Biotechnol, 38(5), 586-599.

Onega, T., Barnhill, R. L., Piepkorn, M. W., Longton, G. M., Elder, D. E., Weinstock, M. A. et al. (2018). Accuracy of Digital Pathologic Analysis vs Traditional Microscopy in the Interpretation of Melanocytic Lesions. JAMA Dermatol., 154(10), 1159-1166.

Onega, T., Reisch, L. M., Frederick, P. D., Geller, B. M., Nelson, H. D., Lott, J. P. et al. (2016). Use of Digital Whole Slide Imaging in Dermatopathology. J Digit Imaging., 29(2), 243-253.

Pantanowitz, L., Sharma, A., Carter, A. B., Kurc, T., Sussman, A., Saltz, J. (2018). Twenty Years of Digital Pathology: An Overview of the Road Travelled, What is on the Horizon, and the Emergence of Vendor-Neutral Archives. J. Pathol Inform., 9, 40.

Robinson, E., Kulkarni, P. M., Pradhan, J. S., Gartrell, R. D., Yang, C. et al. (2019). Prediction of distant melanoma recurrence from primary tumor digital H&E images using deep learning. J. Clin. Oncol., 37(15I_suppl), 9577-9577.

Saginala, K., Barsouk, A., Aluru, J. S., Rawla, P., Barsouk, A. (2021). Epidemiology of Melanoma. Med. Sci. (Basel)., 9(4), 63.

Salto-Tellez, M., Maxwell, P., Hamilton, P. (2019). Artificial intelligence-the third revolution in pathology. Histopathology., 74(3), 372-376.

Serag, A., Ion-Margineanu, A., Qureshi, H., McMillan, R., Saint Martin, M. J. et al. (2019). Translational AI and Deep Learning in Diagnostic Pathology. Front Med (Lausanne)., 6, 185.

Sharma, G., Carter, A. (2017). Artificial intelligence and the pathologist: Future frenemies? Arch. Pathol. Lab. Med., 141, 622-623.

Shen, D., Wu, G., Suk, H. I., (2017). Deep learning in medical image analysis. Annu Rev Biomed Eng.,19, 221-248.

Shoo, B. A., Sagebiel, R. W., Kashani-Sabet, M. (2010). Discordance in the histopathologic diagnosis of melanoma at a melanoma referral center. J. Am. Acad. Dermatol., 62, 751-756.

Siegel, R. L., Miller, K. D., Fuchs, H. E., Jemal, A. (2021). Cancer Statistics, 2021. CA Cancer J. Clin., 71(1), 7-33.

Stoeckius, M., Hafemeister, C., Stephenson, W., Houck-Loomis, B., Chattopadhyay, P. K., Swerdlow, H., et al. (2017). Simultaneous epitope and transcriptome measurement in single cells. Nat Methods.,14(9), 865-868.

Ugolini, F., Pasqualini, E., Simi, S., Baroni, G., Massi, D. (2022). Bright-Field Multiplex Immunohistochemistry Assay for Tumor Microenvironment Evaluation in Melanoma Tissues. Massi. Cancers (Basel)., 14(15), 3682.

Van Herck, Y., Antoranz, A., Andhari, M. D., Milli, G., Bechter, O., De Smet, F. et al. (2021). Multiplexed Immunohistochemistry and DigitalPathology as the Foundation for Next-Generation Pathology in Melanoma: Methodological Comparison and Future Clinical Applications. Front Oncol., 11, 636681.

Visiopharm. High-quality alignment of serial sections (2020). URL: https://visiopharm.com/ visiopharm-digital-image-analysis-software-features/ tissuealign/.

Wang, L., Ding, L., Liu, Z., Sun, L., Chen, L., Jia, R. et al. (2020). Automated identification of malignancy in whole-slide pathological images: Identification of eyelid malignant melanoma in gigapixel pathological slides using deep learning. Br. J. Ophthalmol., 104, 318-323.

Wong, S. T. (2018). Is pathology prepared for the adoption of artificial intelligence? Cancer Cytopathol, 126, 373-375.

Xu, H., Berendt, R., Jha, N., Mandal, M. (2017). Automatic measurement of melanoma depth of invasion in skin histopathological images. Micron., 97, 56-67.

Xu, H., Lu, C., Berendt, R., Jha, N., Manda,l M. (2018). Automated analysis and classification of melanocytic tumor on skin whole slide images. Comput. Med. Imaging. Graph., 66, 124-134.

Published

2023-05-26

How to Cite

Dudin, O. Y. (2023). DIGITAL PATHOLOGY IN MELANOMA: ACHIEVEMENTS, BARRIERS AND PROSPECTS. Medical Informatics and Engineering, (4), 9–20. https://doi.org/10.11603/mie.1996-1960.2022.4.13411

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Articles