Artificial intelligence in the care of patients with rectal cancer undergoing neoadjuvant chemoradiation and intentional watchful waiting: a literature review

Abstract

Rectal cancer remains a major global health challenge, prompting ongoing efforts to optimize treatment strategies. In recent years, organ-preserving approaches—particularly the “watch-and-wait” strategy—have gained growing interest. Concurrently, the advent of artificial intelligence (AI) has opened new avenues in personalized oncology. This review explored the emerging role of AI in the individualized management of rectal cancer, with a focus on its potential to improve treatment outcomes and patient prognosis. Herein, we provide a comprehensive synthesis of recent studies investigating AI applications in predicting pathological complete response, metastasis, and disease-free survival following neoadjuvant therapy. These studies employ diverse data modalities, including radiomics (magnetic resonance imaging (MRI), computerized tomography (CT), and endoscopy), clinical parameters, and other omics-based features. The study evaluated the predictive models developed using machine learning and deep learning algorithms, discussing their performance metrics, strengths, and limitations. Despite the ongoing challenges—such as limited data availability, lack of model interpretability, and suboptimal predictive accuracy—AI has demonstrated potential to outperform conventional assessment methods in select areas. These findings may highlight the growing significance of AI in supporting personalized, evidence-based decision-making in rectal cancer care.