Differentiating low tumor burden from oligometastatic disease in colorectal cancer: a call for individualized therapeutic approaches

Abstract

Artificial intelligence; Colorectal cancer; Immunotherapy

Inteligencia artificial; Cáncer colorrectal; Inmunoterapia

Intel·ligència artificial; Càncer colorectal; Immunoteràpia

Metastatic colorectal cancer (mCRC) remains a major clinical challenge; however, tumor burden significantly influences treatment outcomes. In this review, we explore the biological and clinical relevance of low tumor burden (LTB) in mCRC. The primary challenge in defining LTB mCRC lies in establishing a standardized definition that extends beyond the current focus on oligometastatic disease. Patients with LTB mCRC exhibit distinct clinical characteristics that may impact both prognosis and therapeutic response. Evidence suggests that LTB patients often respond better to systemic therapies and may derive potential benefits from targeted and immunotherapy approaches. However, establishing a clear definition is crucial for consistent patient stratification, and for guiding research and selecting the most appropriate therapeutic strategies, particularly in the context of emerging treatments such as immunotherapy. Recent studies using advanced imaging modalities, liquid biopsies, and lactate dehydrogenase (LDH) measurements offer novel approaches to evaluate tumor burden more accurately. These developments, coupled with emerging evidence that patients with LTB may benefit from immunotherapy, highlight the need for further research focused on LTB mCRC patients. Additionally, artificial intelligence (AI) could enhance tumor detection, automate three-dimensional (3D) volume quantification, extract radiomics-based prognostic information, and integrate multimodal data. These capabilities may enhance our ability to stratify patients and guide treatment decisions, potentially leading to better outcomes for mCRC patients. Future studies should focus on refining the definition of LTB, validating these new assessment techniques, and evaluating their impact on treatment outcomes in mCRC patients.