scieee AI-readable full text Open interactive document viewer

The role of digital pathology and artificial ıntelligence-assisted analysis in breast cancer diagnosis

Keser Sahin, Havva Hande

Abstract

Dear Editor, Breast cancer is the most commonly diagnosed malignant cancer among women worldwide and poses a significant public health issue in terms of early detection and effective treatment. Therefore, the rapid and accurate diagnosis of breast cancer plays a crucial role in the treatment process. In addition to traditional pathology methods, the increased use of digital pathology has emerged as a new and powerful tool in this fight. Digital pathology allows for more precise, faster, and standardized evaluations in patient diagnosis processes, and this technology enhances the efficiency of pathologists, thereby improving the quality of healthcare services. Digital pathology involves the digital scanning of specimen slides and the analysis of these images, providing speed, accuracy, and standardization to histopathological evaluations. While traditional pathological examinations can be time-consuming and sometimes subjective, digital pathology makes this process more objective. Especially in complex diseases like breast cancer, digital pathology is revolutionizing critical stages such as tumor subtyping, and the evaluation of prognostic and predictive markers (1,2). This process ensures that pathologists can reach clearer, more reliable, and accurate results. Artificial intelligence (AI)-assisted algorithms further enhance the power of digital pathology. These algorithms can perform histopathological evaluations more quickly, accurately, and consistently. For example, the digital analysis of HER2 protein evaluation shows high agreement with manual scoring and provides a more accurate correlation with clinical outcomes (3). Furthermore, telepathology helps overcome geographic barriers among pathologists, creating opportunities for equal access to healthcare services, particularly in areas with a shortage of pathologists (4). Digital pathology has several significant potential advantages that could revolutionize breast cancer diagnosis in the future. As artificial intelligence continues to develop, the costs of digital pathology are decreasing, and the technology is becoming more accessible across broader geographical areas. In particular, AI-assisted digital pathology may play a crucial role in addressing the shortage of pathologists in low- and middle-income countries (5,6). This development presents a significant opportunity to reduce global health inequalities. AI-based systems, with their complex image processing algorithms, also significantly reduce error rates in the diagnostic process. For instance, algorithms that automatically detect tumor areas and classify tumors in tissue sections allow for the accurate detection of even small metastases that the human eye might miss (7). These systems help reduce the workload of pathologists while increasing diagnostic accuracy. Additionally, the reliability of these automated analyses plays a critical role in minimizing human error in healthcare services. The contribution of digital pathology to efficiency and processing times is also noteworthy. AI-supported big data analysis enables a larger number of samples to be processed in a shorter time. This development not only reduces the workload in pathology laboratories but also contributes to the faster and more effective delivery of healthcare services (8). Particularly for fast-progressing diseases such as cancer, accelerating the diagnostic process shortens the time to begin treatment and increases patients' chances of recovery. In conclusion, the development of digital pathology, particularly with the new opportunities presented by artificial intelligence, signifies an important paradigm shift in breast cancer diagnosis. In addition to offering more precise, faster, and accurate diagnosis, this technology makes it possible to provide equal healthcare services to a broader population. By making healthcare more accessible and minimizing errors in the diagnostic process, digital pathology stands out as a significant step in the fight against cancer. In this context, emphasizing the future role of digital pathology and raising awareness on this topic will greatly contribute to the development of new solutions in the battle against cancer.

Full text

JCTEI JOURNAL OF CLINICAL TRIALS AND EXPERIMENTAL INVESTIGATIONS 153 Year: 2024 Volume: 3 Issue: 4 10.5281/ zenodo.14602751 The role of digital pathology and artificial intelligence-assisted analysis in breast cancer diagnosis LETTER Havva Hande Keser Sahin¹ 1. Hitit University Faculty of Medicine, Department of Pathology, Corum, Turkey Dear Editor, Breast cancer is the most commonly diagnosed malignant cancer among women worldwide and poses a significant public health issue in terms of early detection and effective treatment. Therefore, the rapid and accurate diagnosis of breast cancer plays a crucial role in the treatment process. In addition to traditional pathology methods, the increased use of digital pathology has emerged as a new and powerful tool in this fight. Digital pathology allows for more precise, faster, and standardized evaluations in patient diagnosis processes, and this technology enhances the efficiency of pathologists, thereby improving the quality of healthcare services. Digital pathology involves the digital scanning of specimen slides and the analysis of these images, providing speed, accuracy, and standardization to histopathological evaluations. While traditional pathological examinations can be time-consuming and sometimes subjective, digital pathology makes this process more objective. Especially in complex diseases like breast cancer, digital pathology is revolutionizing critical stages such as tumor subtyping, and the evaluation of prognostic and predictive markers (1,2). This process ensures that pathologists can reach clearer, more reliable, and accurate results. Artificial intelligence (AI)-assisted algorithms further enhance the power of digital pathology. These algorithms can perform histopathological evaluations more Cite as: Keser Sahin HH. The role of digital pathology and artificial intelligence-assisted analysis in breast cancer diagnosis. J Clin Trials Exp Investig. 2024;3(4):153-155. Correspondence Havva Hande Keser Sahin, Hitit University Faculty of Medicine, Department of Pathology, Corum, Turkey. e-mail [email protected] Received: 1 November 2024 Revised: 5 December 2024 Accepted: 7 December 2024 Published: 30 December 2024 ORCID ID of the author(s): HHKS: 0000-0003-1827-1039 2822-5090 /© 2024 Journal of Clinical Trials and Experimental Investigations. Published by Unico's Medicine. This is an openaccess article under the terms of the CC BY license. (https://creativecommons.org/licenses/by/4.0/) 154 JCTEI quickly, accurately, and consistently. For example, the digital analysis of HER2 protein evaluation shows high agreement with manual scoring and provides a more accurate correlation with clinical outcomes (3). Furthermore, telepathology helps overcome geographic barriers among pathologists, creating opportunities for equal access to healthcare services, particularly in areas with a shortage of pathologists (4). Digital pathology has several significant potential advantages that could revolutionize breast cancer diagnosis in the future. As artificial intelligence continues to develop, the costs of digital pathology are decreasing, and the technology is becoming more accessible across broader geographical areas. In particular, AI-assisted digital pathology may play a crucial role in addressing the shortage of pathologists in lowand middle-income countries (5,6). This development presents a significant opportunity to reduce global health inequalities. AI-based systems, with their complex image processing algorithms, also significantly reduce error rates in the diagnostic process. For instance, algorithms that automatically detect tumor areas and classify tumors in tissue sections allow for the accurate detection of even small metastases that the human eye might miss (7). These systems help reduce the workload of pathologists while increasing diagnostic accuracy. Additionally, the reliability of these automated analyses plays a critical role in minimizing human error in healthcare services. The contribution of digital pathology to efficiency and processing times is also noteworthy. AIsupported big data analysis enables a larger number of samples to be processed in a shorter time. This development not only reduces the workload in pathology laboratories but also contributes to the faster and more effective delivery of healthcare services (8). Particularly for fast-progressing diseases such as cancer, accelerating the diagnostic process shortens the time to begin treatment and increases patients' chances of recovery. In conclusion, the development of digital pathology, particularly with the new opportunities presented by artificial intelligence, signifies an important paradigm shift in breast cancer diagnosis. In addition to offering more precise, faster, and accurate diagnosis, this technology makes it possible to provide equal healthcare services to a broader population. By making healthcare more accessible and minimizing errors in the diagnostic process, digital pathology stands out as a significant step in the fight against cancer. In this context, emphasizing the future role of digital pathology and raising awareness on this topic will greatly contribute to the development of new solutions in the battle against cancer. Conflict of interest: The authors report no conflict of interest. Funding source: No funding was required. Ethical approval: This article does not contain any studies with human participants or animals performed by any of the authors. Informed consent: This article does not include any studies involving human participants; therefore, informed consent is not applicable. Acknowledgments: None Peer-review: Externally. Evaluated by independent reviewers working in at least two different institutions appointed by the field editor. Data availability: The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. 155 JCTEI Contributions Research concept and design: HHKS Data analysis and interpretation: HHKS Collection and/or assembly of data: HHKS Writing the article: HHKS Critical revision of the article: HHKS Final approval of the article: HHKS All authors read and approved the final version of the manuscript. References 1. Misra A, Misra PK, Kumar H, Reddy TV, Ramamurthy S, Rao KG. Role of artificial intelligence in precision pathology of breast cancer. AIP Conference Proceedings, 2023;2603(1):020021. 2. Acs B, Rantalainen M, Hartman J. Artificial intelligence as the next step towards precision pathology. J Intern Med. 2020;288(1):62-81. 3. Smine Z, Poeta S, De Caluwé A, Desmet A, Garibaldi C, Brou Boni K, et al. Automated segmentation in planningCT for breast cancer radiotherapy: A review of recent advances. Radiother Oncol. 2024;202:110615. 4. Golden JA. Deep Learning Algorithms for Detection of Lymph Node Metastases From Breast Cancer: Helping Artificial Intelligence Be Seen. JAMA. 2017;318(22):21846. 5. Niazi MKK, Parwani AV, Gurcan MN. Digital pathology and artificial intelligence. Lancet Oncol. 2019;20(5):e253-e261. 6. Tizhoosh HR, Pantanowitz L. Artificial Intelligence and Digital Pathology: Challenges and Opportunities. J Pathol Inform. 2018;9:38. 7. Dimitriou N, Arandjelović O, Caie PD. Deep Learning for Whole Slide Image Analysis: An Overview. Front Med (Lausanne). 2019;6:264. 8. Komura D, Ishikawa S. Machine Learning Methods for Histopathological Image Analysis. Comput Struct Biotechnol J. 2018;16:34-42. Publisher's Note: Unico's Medicine remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.