RESUMEN
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.
RESUMEN
Background: In January 2022 Kazakhstan experienced unprecedented nationwide protests that quickly turned to violent riots. Although the number of individuals affected physically by the disturbances were cited, the emotional toll of the events remains undetermined. The aim of this study was to evaluate the comparative rates of acute stress reactions in Kazakhstan in the aftermath of the unrests. Methods: A cross-sectional, population-based online survey was conducted one month after the start of the disturbances. The study questionnaire were completed anonymously and included the Primary Care PTSD Screen for DSM-5 (PC-PTSD-5) and the General Anxiety Disorder-7 (GAD-7), as well as socio-demographic and event exposure information. Results: Of the 7021 people who initially agreed to participate, 6510 were able to complete the full survey. For a cut-off of ≥3 on the PC-PTSD-5, 14.8 % of the study participants exhibited symptoms. With a cut-off of ≥4, this percentage reduced to 4.6 %. Participants from Almaty city and Almaty region who experienced the most extensive disturbances showed a doubled prevalence compared to the national level (30.0 % for a cut-off of ≥3, and 10.1 % for a cut-off of ≥4). At the national level, the prevalence of anxiety symptoms, defined as a score of ≥10 on the GAD-7, stood at 10.9 %. This prevalence decreased to 4.2 % when considering a cut-off of ≥15. Conclusion: Health authorities of riot-affected areas ought to be aware of the population level mental health impact of the civil conflict and consider provision of targeted interventions to mitigate the long-term consequence of these lifespan disorders, while also seeking for the peaceful resolution of the ensuing conflicts.