Advances in Cancer Through Machine Learning Models

The integration of machine learning (ML) algorithms in oncology creates a new path for prognosis, early diagnosis, prevention, and treatment customization. However, large-scale clinical implementation is difficult due to the lack of standardized assessments and the variation in reported performance. A systematic review of the most recent research on ML applications in oncology (1 January 2020–31 December 2025) was conducted. The databases employed are Web of Science, Scopus, and PubMed. Filters applied for open-access articles that were simultaneously indexed and had numerical data in the abstract. From an initial of 13,292 articles, successive selection according to the PRISMA diagram resulted in a final set of 1364 studies. These were analyzed from four perspectives: the types of cancer investigated, the characteristics of the datasets (reproducibility and generalizability), the ML models used, and the performance achieved (accuracy, precision, recall, F1-score, and AUC). There is high interest in breast cancer (350 articles), colorectal cancer (337 articles), and lung cancer (151 articles), with frequent use of the databases The Cancer Genome Atlas (133 studies), Gene Expression Omnibus (94 studies), and Surveillance, Epidemiology, and End Results (72 studies). The Random Forest model proved to be predominant due to its tolerance for incomplete data. Reported performance varies considerably between cancer types and even within the same type. This analysis demonstrates the potential of ML methods for deciphering genomic alterations and supports the development of integrated personalized medicine approaches in oncology.