Tumor Budding in Colorectal Carcinoma—From Incidental Observation to Prognostic Marker: Lessons Learned from Colorectal Cancer Assessment

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.

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Publication Details

Journal
Medicina
Published
2026-04-12
DOI
https://doi.org/10.3390/medicina62040734
Primary Topic
AI in cancer detection
Type
article
Field-Weighted Citation Impact
0.00
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article

Tumor Budding in Colorectal Carcinoma—From Incidental Observation to Prognostic Marker: Lessons Learned from Colorectal Cancer Assessment

Valentin Titus Grigorean, Anwar Erchid, Iancu Emil Pleşea, Răzvan Mihail Pleşea et al.
Medicina
AI in cancer detection
article

Tumor Budding in Colorectal Carcinoma—From Incidental Observation to Prognostic Marker: Lessons Learned from Colorectal Cancer Assessment

Valentin Titus Grigorean, Anwar Erchid, Iancu Emil Pleşea, Răzvan Mihail Pleşea, Aminia Diana Ciobănoiu
article en

Abstract

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.

MedicinaVol. 62(4)
Carol Davila University of Medicine and Pharmacy (RO), Clinical Emergency Hospital Bucharest (RO), University of Medicine and Pharmacy of Craiova (RO), University of Craiova (RO)
Openalex Percentile: Top 26%
AI in cancer detection
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