Challenges and opportunities in integrating radiomics and machine learning for early cancer detection: A state-of-the-art review

Abstract

The intersection of radiomics and machine learning presents a promising avenue for revolutionizing early cancer detection. This state-of-the-art review explores the challenges and opportunities associated with integrating these two cutting-edge technologies to enhance diagnostic precision and improve patient outcomes. Radiomics, the extraction of quantitative features from medical images, has emerged as a powerful tool for characterizing tumor heterogeneity. Coupled with machine learning algorithms, this approach holds the potential to unlock intricate patterns within imaging data that may elude traditional diagnostic methods. One of the primary challenges lies in the standardization and reproducibility of radiomic features across different imaging modalities and platforms. Addressing this challenge is crucial for ensuring the reliability and generalizability of radiomics-based models in diverse clinical settings. Machine learning algorithms, particularly deep learning models, play a pivotal role in analyzing complex radiomic data. However, the scarcity of annotated datasets poses a significant obstacle, hindering the training and validation of robust models. Overcoming this limitation requires collaborative efforts to curate large, diverse datasets representative of various cancer types and stages. Moreover, the interpretability of machine learning models remains a concern, as the