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The Performance and Clinical Applicability of HER2 Digital Image Analysis in Breast Cancer: A Systematic Review.
Dunenova, Gauhar; Kalmataeva, Zhanna; Kaidarova, Dilyara; Dauletbaev, Nurlan; Semenova, Yuliya; Mansurova, Madina; Grjibovski, Andrej; Kassymbekova, Fatima; Sarsembayev, Aidos; Semenov, Daniil; Glushkova, Natalya.
Afiliación
  • Dunenova G; Department of Epidemiology, Biostatistics and Evidence-Based Medicine, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan.
  • Kalmataeva Z; Rector Office, Asfendiyarov Kazakh National Medical University, Almaty 050000, Kazakhstan.
  • Kaidarova D; Kazakh Research Institute of Oncology and Radiology, Almaty 050022, Kazakhstan.
  • Dauletbaev N; Department of Internal, Respiratory and Critical Care Medicine, Philipps University of Marburg, 35037 Marburg, Germany.
  • Semenova Y; Department of Pediatrics, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC H4A 3J1, Canada.
  • Mansurova M; Faculty of Medicine and Health Care, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan.
  • Grjibovski A; School of Medicine, Nazarbayev University, Astana 010000, Kazakhstan.
  • Kassymbekova F; Department of Artificial Intelligence and Big Data, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan.
  • Sarsembayev A; Central Scientific Research Laboratory, Northern State Medical University, Arkhangelsk 163000, Russia.
  • Semenov D; Department of Epidemiology and Modern Vaccination Technologies, I.M. Sechenov First Moscow State Medical University, Moscow 105064, Russia.
  • Glushkova N; Department of Biology, Ecology and Biotechnology, Northern (Arctic) Federal University, Arkhangelsk 163000, Russia.
Cancers (Basel) ; 16(15)2024 Aug 03.
Article en En | MEDLINE | ID: mdl-39123488
ABSTRACT
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Cancers (Basel) Año: 2024 Tipo del documento: Article País de afiliación: Kazajstán Pais de publicación: Suiza
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Cancers (Basel) Año: 2024 Tipo del documento: Article País de afiliación: Kazajstán Pais de publicación: Suiza