BAYESIAN INFERENCE AS A TOOL FOR REDUCING DIAGNOSTIC UNCERTAINTIES IN CANCER
DOI:
https://doi.org/10.63330/aurumpub.044-015Keywords:
Bayesian Inference, Cancer, Diagnosis, Diagnostic Uncertainty, OncologyAbstract
The increasing complexity of oncological diagnosis, associated with tumor heterogeneity and limitations of traditional methods, highlights the need for approaches capable of reducing clinical uncertainties and improving decision-making. In this context, Bayesian inference emerges as a relevant tool by enabling the integration of prior probabilities with new clinical evidence. This study aimed to analyze the application of Bayesian inference in reducing diagnostic uncertainties in cancer through an integrative literature review. The search was conducted in the SciELO, PubMed, and Scopus databases, considering publications from 2021 to 2026 in Portuguese, English, and Spanish. Studies addressing the use of Bayesian methods in diagnosis, prognosis, and clinical trials in oncology were included. The results demonstrated that Bayesian inference significantly enhances clinical reasoning by allowing continuous updating of diagnostic probabilities and reducing cognitive biases. Furthermore, its application in predictive models, survival analysis, and adaptive clinical trials showed greater methodological flexibility and statistical robustness, especially in scenarios involving high uncertainty and small sample sizes. Its role in precision oncology was also highlighted, particularly in integrating clinical and molecular data to support personalized care. It is concluded that Bayesian inference represents a promising approach to improve oncological diagnosis, contributing to more accurate and evidence-based clinical decisions.
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