Subscribe to RSS
Please copy the URL and add it into your RSS Feed Reader.
https://www.thieme-connect.de/rss/thieme/en/10.1055-s-00000125.xml
CC BY-NC-ND 4.0 · Senologie - Zeitschrift für Mammadiagnostik und -therapie 2025; 22(01): 31-42
DOI: 10.1055/a-2314-0472
DOI: 10.1055/a-2314-0472
Review
Pathology: Diagnostics, Reporting and Artificial Intelligence
Article in several languages: English | deutsch
Abstract
Breast pathology poses a particular diagnostic challenge due to the broad spectrum of functional, reactive and neoplastic changes in the breast. Objectifiable and reproducible criteria are the key to a valid diagnosis. In addition to the diagnostic classification of lesions, it is the task of pathologists to identify and document all tumor characteristics that are relevant for clinical management. Modern personalized medicine is based on up-to-date, valid pathomorphological and molecular diagnostics. Reports of findings should be written comprehensibly, completely and quickly. Structured pathology reports are ideal for this purpose. Before artificial intelligence can fulfil the hopes placed in it regarding the acceleration and objectification of reporting, technical and financial limitations must be resolved in addition to the explainability of AI-generated decisions.
Keywords
Breast pathology - biomarkers - precision medicine - pathology reporting - artificial intelligencePublication History
Article published online:
10 March 2025
© 2025. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial-License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).
Georg Thieme Verlag KG
Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany
-
References
- 1 IQNPath, ECPC, EFPIA. Unlocking the potential of precision medicine in Europe. 2021 Accessed January 08, 2025 at: https://www.efpia.eu/media/589727/unlocking-the-potential-of-precision-medicine-in-europe.pdf
- 2 Campanella G, Hanna MG, Geneslaw L. et al. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nature medicine 2019; 25 (08) 1301-1309
- 3 Litjens G, Kooi T, Bejnordi BE. et al. A survey on deep learning in medical image analysis. Medical Image Analysis 2017; 42: 60-88
- 4 Bera K, Schalper KA, Rimm DL. et al. Artificial intelligence in digital pathology – new tools for diagnosis and precision oncology. Nature Reviews Clinical Oncology 2019; 16 (11) 703-715
- 5 Elston CW, Ellis IO. Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with long-term follow-up. Histopathology 1991; 19 (05) 403-410
- 6 Bloom HJG, Richardson WW. Histological Grading and Prognosis in Breast Cancer. British Journal of Cancer 1957; 11 (03) 359-377
- 7 5th ed, Vol. 2. Lyon (France): International Agency for Research on Cancer; 2019
- 8 Deutsche Krebsgesellschaft; Deutsche Krebshilfe; AWMF. S3-Leitlinie Früherkennung, Diagnose, Therapie und Nachsorge des Mammakarzinoms, Version 4.4 2021 [updated Mai 2021;14.01.2022]. Available from. Accessed January 08, 2025 at: http://www.leitlinienprogramm-onkologie.de/leitlinien/mammakarzinom/
- 9 Park-Simon TW, Müller V, Albert US. et al. Arbeitsgemeinschaft Gynakologische Onkologie Recommendations for the Diagnosis and Treatment of Patients with Early Breast Cancer: Update 2024. Breast Care (Basel) 2024; 19 (03) 165-182
- 10 Thill M, Janni W, Albert US. et al. Arbeitsgemeinschaft Gynakologische Onkologie Recommendations for the Diagnosis and Treatment of Patients with Locally Advanced and Metastatic Breast Cancer: Update 2024. Breast Care (Basel) 2024; 19 (03) 183-191
- 11 Qualitätssicherungs-Initiative Pathologie (QuIP). Mammakarzinomportal 2024 [updated August 2024]. Available from. Accessed January 08, 2025 at: https://www.mammakarzinomportal.eu/de/
- 12 Hewer E, Rump A, Langer R. Standardisierte strukturierte Befundberichte gastrointestinaler Tumoren. Der Pathologe 2022; 43 (01) 57-62
- 13 College of American Pathologists (CAP). Cancer Protocol Templates [updated June 2024;]. Available from. Accessed January 08, 2025 at: https://www.cap.org/protocols-and-guidelines/cancer-reporting-tools/cancer-protocol-templates#protocols
- 14 International Collaboration on Cancer Reporting (ICCR). Published Datasets 2024. Available from. Accessed January 08, 2025 at: https://www.iccr-cancer.org/datasets/published-datasets/
- 15 Ellis I, Webster F, Allison KH. et al. Dataset for reporting of the invasive carcinoma of the breast: recommendations from the International Collaboration on Cancer Reporting (ICCR). Histopathology 2024; 85 (03) 418-436
- 16 López-Pineda A, Rodríguez-Moran MF, Álvarez-Aguilar C. et al. Data mining of digitized health records in a resource-constrained setting reveals that timely immunophenotyping is associated with improved breast cancer outcomes. BMC Cancer 2018; 18 (01)
- 17 Sepulveda AR, Hamilton SR, Allegra CJ. et al. Molecular Biomarkers for the Evaluation of Colorectal Cancer: Guideline From the American Society for Clinical Pathology, College of American Pathologists, Association for Molecular Pathology, and the American Society of Clinical Oncology. Journal of Clinical Oncology 2017; 35 (13) 1453-1486
- 18 Versorgung im Rahmen des Programms zur Früherkennung von Brustkrebs durch Mammographie-Screening, (2024). Anlage 9.2 BMV-Ä. Version 1.7.2024. Available from. Accessed October 06, 2024 at: https://www.kbv.de/media/sp/09.2_Mammographie.pdf
- 19 Deutsche Krebsgesllschaft (DKG). Zertifizierung der Deutschen Krebsgesellschaft: Dokumente im Überblick 2024. Available from. Accessed January 08, 2025 at: https://www.krebsgesellschaft.de/zertdokumente.html
- 20 European Commission, Joint Research Centre. European Commission Initiative on Breast Cancer – European Quality Assurance Scheme for Breast Cancer Services. Luxembourg: Publications Office of the European Union; 2025. https://data.europa.eu/doi/10.2760/7067385 , JRC140425
- 21 Soliman A, Li Z, Parwani AV. Artificial intelligence’s impact on breast cancer pathology: a literature review. Diagnostic Pathology 2024; 19 (01)
- 22 Sandbank J, Bataillon G, Nudelman A. et al. Validation and real-world clinical application of an artificial intelligence algorithm for breast cancer detection in biopsies. npj Breast Cancer 2022; 8 (01)
- 23 Abdelsamea MM, Zidan U, Senousy Z. et al. A survey on artificial intelligence in histopathology image analysis. WIREs Data Mining and Knowledge Discovery 2022; 12 (06)
- 24 Litjens G, Bandi P, Babak E. et al. 1399 H&E-stained sentinel lymph node sections of breast cancer patients: the CAMELYON dataset. GigaScience 2018; 7 (06)
- 25 National Cancer Institute. National Cancer Institute CDG Data Portal 2024 Available from. Accessed January 08, 2025 at: https://portal.gdc.cancer.gov/analysis_page?app=Downloads
- 26 Weigert M, Schmidt U. Nuclei Instance Segmentation and Classification in Histopathology Images with Stardist. The IEEE International Symposium on Biomedical Imaging Challenges (ISBIC). 2022
- 27 Weigert M, Schmidt U, Haase R. et al. Star-convex Polyhedra for 3D Object Detection and Segmentation in Microscopy. The IEEE Winter Conference on Applications of Computer Vision (WACV) 2020;
- 28 Schmidt U, Weigert M, Broaddus C. et al. Cell Detection with Star-Convex Polygons. Medical Image Computing and Computer Assisted Intervention (MICCAI), 21st International Conference 2018 September 16–20, 2018; Granada, Spain. 2018
- 29 Conde-Sousa E, Vale J, Feng M. et al. HEROHE Challenge: Predicting HER2 Status in Breast Cancer from Hematoxylin–Eosin Whole-Slide Imaging. Journal of Imaging 2022; 8 (08) 213
- 30 Li Y, Xiong X, Liu X. et al. An interpretable deep learning model for detecting BRCA pathogenic variants of breast cancer from hematoxylin and eosin-stained pathological images. PeerJ 2024; 12: e18098
- 31 Kreipe HH, Höfler H, Lebeau A. et al. Ergebnisse der Referenzpathologie im Mammographie-Screening. Der Pathologe 2008; 29 (Suppl. 2) 178-180
- 32 Amendoeira I, Bianchi S, Boecker W. et al. Consistency achieved by 23 European pathologists from 12 countries in diagnosing breast disease and reporting prognostic features of carcinomas. Virchows Archiv 1999; 434 (01) 3-10
- 33 Ellis IO, Coleman D, Wells C. et al. Impact of a national external quality assessment scheme for breast pathology in the UK. Journal of Clinical Pathology 2006; 59 (02) 138-145
- 34 Rakha EA, Ahmed MA, Aleskandarany MA. et al. Diagnostic concordance of breast pathologists: lessons from the National Health Service Breast Screening Programme Pathology External Quality Assurance Scheme. Histopathology 2017; 70 (04) 632-642
- 35 Kreipe HH. Zweitmeinungs- und Referenzpathologie beim Mammakarzinom. Die Pathologie 2022; 43 (Suppl. 1) 74-80
- 36 Walker M, Mayr E-M, Koppermann M-L. et al. Molekularpathologische Untersuchungen im Wandel der Zeit. Die Pathologie 2024; 45 (03) 173-179