Skip to main content
SpringerLink
Log in

Digitalisierung in der Dermatoonkologie: künstliche Intelligenz zur Diagnostik

Digitalization in dermato-oncology: artificial intelligence-based diagnostic tools

  • Topic
  • Published:
best practice onkologie Aims and scope

Zusammenfassung

Kutane Malignitäten sind eine der häufigsten dermatologischen Diagnosen und können je nach Subtyp mit einer hohen Mortalität und Morbidität einhergehen. Die initiale Diagnostik erfolgt üblicherweise mittels makroskopischer und dermatoskopischer Beurteilung, wobei insbesondere melanozytäre Läsionen teilweise sehr herausfordernd sein können. Die künstliche Intelligenz (KI) ist eine Unterform des maschinellen Lernens und ermöglicht es, Muster aus riesigen, standardisierten Datenmengen zu lernen und diese erfolgreich auf neue Daten anzuwenden. Dabei können auch Muster erkannt werden, die sich der menschlichen Wahrnehmung entziehen. Die Daten in der Dermatoonkologie, die neben klinischen und dermatoskopischen Bildern auch 3‑D-Ganzkörperaufnahmen, histologische Schnitte und Ausgaben von neuen Bildgebungssystemen wie der Line-Field-Technik bei der optischen Kohärenztomographie umfassen, eigenen sich daher sehr gut für die Analyse mittels KI. Während es für die Analyse von klinischen/dermatoskopischen Bildern für die Klassifikation von melanozytären Läsionen bereits marktreife Systeme gibt, steckt die KI-Forschung für andere Fragestellungen noch in den Kinderschuhen. Insbesondere für die Analyse von hochstandardisierten Daten wie 3‑D-Ganzkörperaufnahmen, histopathologischen Schnitten und Daten der neuen Bildgebungsverfahren ist das Potenzial der KI-Analysen noch nicht vollständig ausgeschöpft. Der Einsatz von KI in der Dermatoonkologie muss jedoch immer kritisch und vor dessen technischem Hintergrund erfolgen. Hürden und Limitationen sind insbesondere die Notwendigkeit standardisierter Daten und die Spezifität der Algorithmen auf die Fragestellung, für welche sie trainiert wurden.

Abstract

Cutaneous malignancies are one of the most common dermatological diagnoses and—depending on subtype—are associated with high mortality and morbidity. Initial assessment includes macroscopic and dermoscopic analysis of the lesion, where melanocytic lesions can be especially challenging. Artificial intelligence (AI) is a subtype of machine learning and enables patterns to be learnt on huge, standardized datasets, with subsequent successful application of the algorithm to new data. AI algorithms can also learn patterns which elude human perception. Data in dermato-oncology include not only clinical and dermoscopic images, but also 3D full-body scans, histological slides, and the output of new imaging modalities such as line-field optical coherence tomography, all of which are well suited for AI analysis. While for AI-based assessment of melanocytic lesions on clinical/dermoscopic images there are already market-ready applications, AI research in other domains of dermato-oncology is still in its infancy. Particularly for the analysis of highly standardized data like 3D full-body scans, histological slides, and data from new imaging modalities, the full potential of AI analysis not yet exhausted. AI in dermato-oncology must be used critically and against its technical background. Limitations and obstacles include the need for standardized data and the specificity of AI algorithms for the question they have been trained to answer.

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

Abb. 1
Abb. 2

Literatur

  1. Schadendorf D et al (2015) Melanoma. Nat Rev Dis Primer 1:1–20

    Google Scholar 

  2. Therapie-Handbuch – Dermatologie und Allergologie. (Elsevier, 2022).

  3. Keung EZ, Gershenwald JE (2018) The eighth edition American Joint Committee on Cancer (AJCC) melanoma staging system: implications for melanoma treatment and care. Expert Rev Anticancer Ther 18:775–784

    Article  CAS  Google Scholar 

  4. Mintz Y, Brodie R (2019) Introduction to artificial intelligence in medicine. Minim Invasive Ther Allied Technol 28:73–81

    Article  Google Scholar 

  5. Dai Z, Liu H, Le QV, Tan M (2021) Coatnet: marrying convolution and attention for all data sizes. ArXiv210604803 Cs

    Google Scholar 

  6. Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL (2018) Artificial intelligence in radiology. Nat Rev Cancer 18:500–510

    Article  CAS  Google Scholar 

  7. Winkler JK, Haenssle HA (2022) Bilderkennung mittels künstlicher Intelligenz in der Hautkrebsdiagnostik. Dermatol 73:838–844

    Article  Google Scholar 

  8. Efimenko M, Ignatev A, Koshechkin K (2020) Review of medical image recognition technologies to detect melanomas using neural networks. BMC Bioinform 21:270

    Article  Google Scholar 

  9. Fabbrocini G et al (2011) Teledermatology: from prevention to diagnosis of nonmelanoma and melanoma skin cancer. Int J Telemed Appl. https://doi.org/10.1155/2011/125762

    Article  Google Scholar 

  10. Ali A‑RA, Deserno TM (2012) A systematic review of automated melanoma detection in dermatoscopic images and its ground truth data. Med Imaging 2012: Image Percept Obs Perform Technol Assess 8318:421–431 (SPIE, 2012). https://doi.org/10.1117/12.912389

  11. Carli P et al (2003) Pattern analysis, not simplified algorithms, is the most reliable method for teaching dermoscopy for melanoma diagnosis to residents in dermatology. Br J Dermatol 148:981–984

    Article  CAS  Google Scholar 

  12. Dolianitis C, Kelly J, Wolfe R, Simpson P (2005) Comparative performance of 4 dermoscopic algorithms by nonexperts for the diagnosis of melanocytic lesions. Arch Dermatol 141:1008–1014

    Article  Google Scholar 

  13. Brinker TJ et al (2018) Skin cancer classification using convolutional neural networks: systematic review. J Med Internet Res 20:e11936

    Article  Google Scholar 

  14. Esteva A et al (2017) Dermatologist-level classification of skin cancer with deep neural networks. Nature 542:115–118

    Article  CAS  Google Scholar 

  15. Haenssle HA et al (2018) Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Ann Oncol 29:1836–1842

    Article  CAS  Google Scholar 

  16. Haenssle HA et al (2021) Skin lesions of face and scalp—Classification by a market-approved convolutional neural network in comparison with 64 dermatologists. Eur J Cancer 144:192–199

    Article  Google Scholar 

  17. Jaworek-Korjakowska J, Kłeczek P (2016) Automatic classification of specific melanocytic lesions using artificial intelligence. Biomed Res Int. https://doi.org/10.1155/2016/8934242

    Article  Google Scholar 

  18. Liu Y et al (2020) A deep learning system for differential diagnosis of skin diseases. Nat Med 26:900–908

    Article  CAS  Google Scholar 

  19. Höhn J et al (2021) Integrating patient data into skin cancer classification using convolutional neural networks: systematic review. J Med Internet Res 23:e20708

    Article  Google Scholar 

  20. Winkler JK et al (2019) Association between surgical skin markings in dermoscopic images and diagnostic performance of a deep learning convolutional neural network for melanoma recognition. JAMA Dermatol 155:1135–1141

    Article  Google Scholar 

  21. Winkler JK et al (2021) Association between different scale bars in dermoscopic images and diagnostic performance of a market-approved deep learning convolutional neural network for melanoma recognition. Eur J Cancer 145:146–154

    Article  Google Scholar 

  22. Sitaru S, Kaczmarczyk R, Erdmann M, Biedermann T, Zink A (2022) 3D whole body scans in dermatology—a new era in clinical practice and research? Dermatologie 73:575–579

    Article  Google Scholar 

  23. Rutjes C, Torrano J, Soyer HP (2022) A 3D total-body photography research network: the Australian experiment. Hautarzt. https://doi.org/10.1007/s00105-021-04938-7

    Article  Google Scholar 

  24. Freeman K et al (2020) Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies. BMJ 368:m127

    Article  Google Scholar 

  25. Matin RN, Dinnes J (2021) AI-based smartphone apps for risk assessment of skin cancer need more evaluation and better regulation. Br J Cancer 124:1749–1750

    Article  Google Scholar 

  26. Schmidle P, Braun SA (2022) Digitalisierung in der Dermatopathologie. Dermatologie 73:845–852

    Article  Google Scholar 

  27. Le’Clerc Arrastia J et al (2021) Deeply supervised unet for semantic segmentation to assist dermatopathological assessment of basal cell carcinoma. J Imaging 7:71

    Article  Google Scholar 

  28. Figueroa-Silva O, Pastur Romay LA, Viruez Roca RD, Rojas MDS-AY, Suárez-Peñaranda JM (2022) Machine learning techniques in predicting BRAF mutation status in cutaneous melanoma from clinical and histopathologic features. Appl Immunohistochem Mol Morphol 30:674–680

    Article  CAS  Google Scholar 

  29. Mindpeak News | BreastIHC CE-IVD certified. https://mindpeak.ai//post/breastihc-ce-ivd-certified. Zugegriffen: 04.01.2023

  30. Lee K, Lee SH (2020) Artificial intelligence-driven oncology clinical decision support system for multidisciplinary teams. Sensors 20:E4693

    Article  Google Scholar 

  31. Gilmore SJ (2018) Automated decision support in melanocytic lesion management. PLoS ONE 13:e203459

    Article  Google Scholar 

  32. Liu W, Zhu Y, Lin C, Liu L, Li G (2022) An online prognostic application for melanoma based on machine learning and statistics. J Plast Reconstr Aesthetic Surg 75:3853–3858

    Article  Google Scholar 

  33. Johannet P et al (2021) Using machine learning algorithms to predict immunotherapy response in patients with advanced melanoma. Clin Cancer Res 27:131–140

    Article  CAS  Google Scholar 

  34. Indini A, Di Guardo L, Cimminiello C, De Braud F, Del Vecchio M (2019) Artificial intelligence estimates the importance of baseline factors in predicting response to anti-PD1 in metastatic melanoma. Am J Clin Oncol 42:643–648

    Article  CAS  Google Scholar 

  35. Kong J et al (2022) Network-based machine learning approach to predict immunotherapy response in cancer patients. Nat Commun 13:3703

    Article  CAS  Google Scholar 

  36. Liu M, Xu Y (2022) Gene identification and potential drug therapy for drug-resistant melanoma with bioinformatics and deep learning technology. Dis Markers. https://doi.org/10.1155/2022/2461055

    Article  Google Scholar 

  37. Lewinson RT et al (2021) Machine learning for prediction of cutaneous adverse events in patients receiving anti-PD‑1 immunotherapy. J Am Acad Dermatol 84:183–185

    Article  Google Scholar 

  38. Lambin P et al (2017) Decision support systems for personalized and participative radiation oncology. Adv Drug Deliv Rev 109:131–153

    Article  CAS  Google Scholar 

  39. Pellacani G, Pepe P, Casari A, Longo C (2014) Reflectance confocal microscopy as a second-level examination in skin oncology improves diagnostic accuracy and saves unnecessary excisions: a longitudinal prospective study. Br J Dermatol 171:1044–1051

    Article  CAS  Google Scholar 

  40. Dinnes J et al (2018) Reflectance confocal microscopy for diagnosing cutaneous melanoma in adults. Cochrane Database Syst Rev. https://doi.org/10.1002/14651858.CD013190

    Article  Google Scholar 

  41. Sies K, Winkler JK, Zieger M, Kaatz M, Haenssle HA (2020) Neue optische Untersuchungsverfahren für die Diagnostik von Hautkrankheiten. Hautarzt 71:101–108

    Article  CAS  Google Scholar 

  42. Schuh S, Ruini C, Sattler E, Welzel J (2021) Konfokale Line-Field-OCT. Hautarzt 72:1039–1047

    Article  Google Scholar 

  43. Perez-Anker J et al (2022) Morphological evaluation of melanocytic lesions with three-dimensional line-field confocal optical coherence tomography: correlation with histopathology and reflectance confocal microscopy. A pilot study. Clin Exp Dermatol. https://doi.org/10.1111/ced.15383

    Article  Google Scholar 

  44. Chou H‑Y, Huang S‑L, Tjiu J‑W, Chen HH (2021) Dermal epidermal junction detection for full-field optical coherence tomography data of human skin by deep learning. Comput Med Imaging Graph 87:101833

    Article  Google Scholar 

  45. Fischman S et al (2022) Non-invasive scoring of cellular atypia in keratinocyte cancers in 3D LC-OCT images using Deep Learning. Sci Rep 12:481

    Article  CAS  Google Scholar 

  46. Warnat-Herresthal S et al (2021) Swarm Learning for decentralized and confidential clinical machine learning. Nature 594:265–270

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Klinik und Poliklinik für Dermatologie und Allergologie am Biederstein, Klinikum rechts der Isar, Technische Universität München, Biedersteiner Str. 29, 80802, München, Deutschland

    Sebastian Sitaru &  Alexander Zink

  2. Division of Dermatology and Venereology, Department of Medicine Solna, Karolinska Institutet, 17176, Stockholm, Schweden

    Alexander Zink

Authors
  1. Sebastian Sitaru
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alexander Zink
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sebastian Sitaru.

Ethics declarations

Interessenkonflikt

S. Sitaru und A. Zink geben an, dass kein Interessenkonflikt besteht.

Für diesen Beitrag wurden von den Autor/-innen keine Studien an Menschen oder Tieren durchgeführt. Für die aufgeführten Studien gelten die jeweils dort angegebenen ethischen Richtlinien.

Additional information

Redaktion

Hans-Joachim Schulze, Münster

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sitaru, S., Zink, A. Digitalisierung in der Dermatoonkologie: künstliche Intelligenz zur Diagnostik. best practice onkologie 18, 20–26 (2023). https://doi.org/10.1007/s11654-022-00461-w

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11654-022-00461-w

Schlüsselwörter

Keywords

Search

Navigation