The Performance and Clinical Applicability of HER2 Digital Image Analysis in Breast Cancer: A Systematic Review

Dunenova G. Kalmataeva Z. Kaidarova D. Dauletbaev N. Semenova Y. Mansurova M. Grjibovski A. Kassymbekova F. Sarsembayev A. Semenov D. Glushkova N.
August 2024 Multidisciplinary Digital Publishing Institute (MDPI)

Cancers
2024 #16 Issue 15

This systematic review aims to address the research gap in the performance of computational algorithms for the digital image analysis of HER2 images in clinical settings. While numerous studies have explored various aspects of these algorithms, there is a lack of comprehensive evaluation regarding their effectiveness in real-world clinical applications. We conducted a search of the Web of Science and PubMed databases for studies published from 31 December 2013 to 30 June 2024, focusing on performance effectiveness and components such as dataset size, diversity and source, ground truth, annotation, and validation methods. The study was registered with PROSPERO (CRD42024525404). Key questions guiding this review include the following: How effective are current computational algorithms at detecting HER2 status in digital images? What are the common validation methods and dataset characteristics used in these studies? Is there standardization of algorithm evaluations of clinical applications that can improve the clinical utility and reliability of computational tools for HER2 detection in digital image analysis? We identified 6833 publications, with 25 meeting the inclusion criteria. The accuracy rate with clinical datasets varied from 84.19% to 97.9%. The highest accuracy was achieved on the publicly available Warwick dataset at 98.8% in synthesized datasets. Only 12% of studies used separate datasets for external validation; 64% of studies used a combination of accuracy, precision, recall, and F1 as a set of performance measures. Despite the high accuracy rates reported in these studies, there is a notable absence of direct evidence supporting their clinical application. To facilitate the integration of these technologies into clinical practice, there is an urgent need to address real-world challenges and overreliance on internal validation. Standardizing study designs on real clinical datasets can enhance the reliability and clinical applicability of computational algorithms in improving the detection of HER2 cancer.

applicability in real clinical practice , breast cancer , digital image analysis , HER2 , performance evaluation

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Department of Epidemiology, Biostatistics and Evidence-Based Medicine, Al-Farabi Kazakh National University, Almaty, 050040, Kazakhstan
Rector Office, Asfendiyarov Kazakh National Medical University, Almaty, 050000, Kazakhstan
Kazakh Research Institute of Oncology and Radiology, Almaty, 050022, Kazakhstan
Department of Internal, Respiratory and Critical Care Medicine, Philipps University of Marburg, Marburg, 35037, Germany
Department of Pediatrics, Faculty of Medicine and Health Sciences, McGill University, Montreal, H4A 3J1, QC, Canada
Faculty of Medicine and Health Care, Al-Farabi Kazakh National University, Almaty, 050040, Kazakhstan
School of Medicine, Nazarbayev University, Astana, 010000, Kazakhstan
Department of Artificial Intelligence and Big Data, Al-Farabi Kazakh National University, Almaty, 050040, Kazakhstan
Central Scientific Research Laboratory, Northern State Medical University, Arkhangelsk, 163000, Russian Federation
Department of Epidemiology and Modern Vaccination Technologies, I.M. Sechenov First Moscow State Medical University, Moscow, 105064, Russian Federation
Department of Biology, Ecology and Biotechnology, Northern (Arctic) Federal University, Arkhangelsk, 163000, Russian Federation
Department of Health Policy and Management, Al-Farabi Kazakh National University, Almaty, 050040, Kazakhstan
Department of Public Health and Social Sciences, Kazakhstan Medical University “KSPH”, Almaty, 050060, Kazakhstan
School of Digital Technologies, Almaty Management University, Almaty, 050060, Kazakhstan
Health Research Institute, Al-Farabi Kazakh National University, Almaty, 050040, Kazakhstan
Computer Science and Engineering Program, Astana IT University, Astana, 020000, Kazakhstan

Department of Epidemiology
Rector Office
Kazakh Research Institute of Oncology and Radiology
Department of Internal
Department of Pediatrics
Faculty of Medicine and Health Care
School of Medicine
Department of Artificial Intelligence and Big Data
Central Scientific Research Laboratory
Department of Epidemiology and Modern Vaccination Technologies
Department of Biology
Department of Health Policy and Management
Department of Public Health and Social Sciences
School of Digital Technologies
Health Research Institute
Computer Science and Engineering Program

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