Cancer is a major global health challenge, with increasing prevalence and mortality rates observed in recent decades. Cancer ranks as the second leading cause of mortality worldwide, accounting for approximately 10 million fatalities annually, according to the World Health Organization (WHO). Recent developments in oncology, including prevention, diagnosis, and treatment, have significantly improved the rate of survival along with the quality of life for patients (Siegel et al., 2022). Despite advancements, cancer continues to be a complex and heterogeneous disease, marked by intricate interactions among genetic, environmental, and lifestyle factors. The complexity of oncology has driven the advancement and utilization of computational methods to address essential challenges, including early detection, prognosis, planning of treatment, and monitoring. Advancements in artificial intelligence (AI) and machine learning (ML) have established computational methods as effective instruments in cancer research and treatment. These technologies can process large volumes of data produced by modern healthcare systems, including genomic profiles, imaging studies, and clinical records, facilitating the creation of predictive models and tailored treatment strategies (Esteva et al., 2019). Computational methods can enhance oncology by enabling early-stage tumor detection in medical imaging, predicting individualized patient responses to therapy, increasing accuracy, lowering costs, and improving patient outcomes (Lambin et al., 2017). Despite the potential of these technologies, their broad implementation encounters various challenges and limitations that need to be resolved to achieve optimal effectiveness.