Tumor budding (TB), defined as isolated single cells or small clusters of up to four tumor cells at the invasive front of colorectal carcinoma (CRC), is recognized as an important histopathologic marker associated with adverse tumor behavior. This review summarizes current knowledge on the morphologic assessment, biological significance, and clinical relevance of TB, emphasizing emerging artificial intelligence (AI) methods that aim to automate and standardize its quantification. Standardized reporting by the International Tumor Budding Consensus Conference (ITBCC) has improved reproducibility, while novel deep-learning algorithms demonstrate potential for objective and prognostically relevant TB assessment. Integration of AI-based TB evaluation with molecular and stromal biomarkers may refine patient stratification and facilitate personalized treatment strategies.
Ciobănoiu et al. (2026) studied this question.