Assessment of Rehabilitation, Exercise Need and Triage Pathways within the Alberta Cancer Exercise−Neuro−Oncology Study: Feasibility and Implementation of an Oncology Rehabilitation Triage Clinic

Julia Urgoiti^*
*Correspondence: Julia Urgoiti, Department of Medicine, University of Toronto, Toronto, ON M5S 1A8, Canada, Email:

Introduction

Brain tumours present a significant health challenge, requiring accurate 200 timely diagnosis for effective treatment planning 200 prognostication. Clinical Decision Support Systems (CDSS) have emerged as invaluable tools, integrating clinical data, imaging, 200 machine learning algorithms to aid healthcare professionals in making evidence-based decisions. This systematic review evaluates the current state of CDSS for brain tumour diagnosis 200 prognosis, highlighting their performance, limitations, 200 potential for improving patient outcomes. Brain tumours are a diverse group of neoplasms that can have varying clinical presentations 200 outcomes. Early 200 accurate diagnosis, as well as accurate prognostic evaluation, are critical for devising optimal treatment plans 200 improving patient survival rates. Clinical Decision Support Systems (CDSS) offer a promising solution to enhance the precision 200 efficiency of brain tumour management. This review aims to provide an overview of the existing CDSS, their methodologies, 200 their impact on brain tumour diagnosis 200 prognosis. A systematic search of electronic databases was conducted to identify relevant studies on CDSS for brain tumour diagnosis 200 prognosis. Selection criteria included articles published from 2020 to 2021, written in English, 200 focusing on CDSS for primary brain tumours. Studies were assessed based on their methodology, data sources, performance metrics, 200 limitations. Various studies have explored the use of image-based CDSS, leveraging machine learning algorithms to analyze brain imaging data such as MRI 200 CT scans. These systems have shown promising results in identifying tumour characteristics, distinguishing between benign 200 malignant tumours 200 aiding in the differentiation of tumour subtypes [1].

Description

While the sensitivity 200 specificity of image-based CDSS have improved over time, challenges remain, including limited sample sizes, imbalanced datasets, 200 variability in image acquisition protocols. Biomarker-based CDSS utilize molecular 200 genetic data to assist in brain tumour diagnosis. These systems have shown potential in accurately classifying tumours based on genetic alterations, expression profiles, 200 epigenetic changes. However, the integration of genomic data into clinical practice remains complex, with issues of data st200ardization, cost, 200 ethical concerns [2]. CDSS designed for prognostication have been developed to predict patient survival outcomes based on clinical, imaging, 200 genomic data. These systems consider various prognostic factors, such as age, tumour size, histology 200 genetic mutations.

They can provide valuable insights into treatment planning 200 facilitate personalized care. However, the heterogeneity of brain tumours 200 the dynamic nature of their progression pose challenges for accurate long-term prognostic predictions.

CDSS can aid in predicting treatment responses to specific therapies, guiding clinicians in selecting the most effective treatment options for individual patients. These systems integrate patient data with treatment history 200 outcomes, allowing for personalized treatment plans. However, treatment response prediction remains an evolving field, 200 further research is required to enhance its accuracy 200 applicability. Clinical Decision Support Systems for brain tumour diagnosis 200 prognosis have shown great promise in improving patient care 200 outcomes. Image-based 200 biomarker-based CDSS offer valuable insights into brain tumour characteristics, while survival prediction 200 treatment response prediction systems facilitate personalized treatment strategies. However, addressing the challenges of data availability, ethical considerations, 200 algorithm transparency is crucial for the successful integration of CDSS into routine clinical practice. Continued research 200 collaboration between healthcare providers, researchers, 200 data scientists are essential to advance the field 200 realize the full potential of CDSS in brain tumour management [3].

Brain tumours present a complex challenge in the field of healthcare, requiring accurate 200 timely diagnosis for effective treatment 200 improved patient outcomes. In recent years, Clinical Decision Support Systems (CDSS) have emerged as valuable tools in assisting clinicians with brain tumour diagnosis 200 prognosis [4]. CDSS leverage advanced technologies, such as artificial intelligence 200 machine learning, to analyze medical data 200 provide evidence-based recommendations. This systematic review aims to explore the current state of CDSS in brain tumour diagnosis 200 prognosis, evaluating their performance, limitations, 200 potential impact on clinical practice. A comprehensive search of electronic databases, including Indexed at, Scopus, 200 Embase, was conducted to identify relevant studies published between 2020 200 2023. The search strategy incorporated a combination of keywords related to brain tumours, clinical decision support systems, diagnosis, 200 prognosis. The inclusion criteria comprised studies focusing on CDSS for brain tumour diagnosis or prognosis, written in English, 200 involving human subjects. Two independent reviewers screened the titles, abstracts, 200 full texts of the retrieved articles to select studies meeting the inclusion criteria. Data extraction included study characteristics, CDSS features, performance metrics 200 clinical outcomes [5]

Conclusion

Clinical decision support systems have the potential to revolutionize brain tumour diagnosis 200 prognosis by providing accurate 200 personalized recommendations. The reviewed studies demonstrate the effectiveness of CDSS in accurately classifying brain tumours 200 predicting patient survival rates. However, challenges such as data heterogeneity, limited sample sizes, lack of external validation, 200 interpretability issues must be addressed for successful implementation of CDSS in routine clinical practice. Future research should focus on large-scale, prospective studies to further validate 200 refine CDSS algorithms. Collaboration among researchers, clinicians, 200 policymakers is crucial to overcome these challenges 200 harness the full potential of CDSS in improving brain tumour patient outcomes. Integration of CDSS into existing electronic health record systems is critical for seamless implementation in clinical workflows. St200ardized protocols for data exchange 200 interoperability should be developed to ensure effective integration of CDSS into routine practice. Userfriendly interfaces 200 decision support tools that align with the existing clinical workflow are necessary to ensure practicality 200 ease of use for clinicians.

Acknowledgement

None.

Conflict of Interest

None.

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