ArticleModern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc2019
Immunohistochemical evaluation of tumor budding for stratifying T1 colorectal cancer: optimal cut-off value and a novel computer-assisted semiautomatic method.
Article in Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Additional staining for lymphovascular invasion is associated with increased estimation of lymph node metastasis in patients with T1 colorectal cancer: Systematic review and meta-analysis.Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society · 2024Pooled it
- Prediction of lymph node metastasis in early colorectal cancer based on histologic images by artificial intelligence.Scientific reports · 2022Trial
- A full-automated tumor budding annotation approach in hematoxylin and eosin-stained whole slide images of colorectal cancer.NPJ precision oncology · 2025Article
- Tumor Budding as a Risk Factor for Lymph Node Metastasis and Local Recurrence in pT1 Colorectal Cancer: A Systematic Review and Meta-Analysis.Gastro hep advances · 2025Review
- Histopronostic factors in superficial colorectal adenocarcinomas treated by endoscopy: reproducibility and impact of immunohistochemistry and digital pathology.Virchows Archiv : an international journal of pathology · 2024Article
- Tumor budding of cervical squamous cell carcinoma: epithelial-mesenchymal transition-like cancer stem cells?PeerJ · 2022Article
- Novel Criteria for Intratumoral Budding with Prognostic Relevance for Colon Cancer and Its Histological Subtypes.International journal of molecular sciences · 2021Article
- Prognostic value of tumor budding in gallbladder cancer: application of the International Tumor Budding Consensus Conference scoring system.Virchows Archiv : an international journal of pathology · 2021Article
- Proposal of a scoring system for predicting pathological risk based on a semiautomated analysis of whole slide images in oral squamous cell carcinoma.Head & neck · 2021Article
- Tumour budding in solid cancers.Nature reviews. Clinical oncology · 2021Review
- Prognostic impact of tumor budding in endometrial carcinoma within distinct molecular subgroups.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2021Article
- Interobserver Variability in the Assessment of Tumor Budding in pT 3/4 Colon Cancer: Improvement by Supporting Immunohistochemistry?Diagnostics (Basel, Switzerland) · 2020Article
- Immunohistochemical analysis of tumor budding as predictor of lymph node metastasis from superficial esophageal squamous cell carcinoma.Esophagus : official journal of the Japan Esophageal Society · 2020Article
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11 authors.
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Abstract
High-grade tumor budding is an adverse prognostic factor for submucosal invasive (T1) colorectal cancer used to predict the risk for lymph node metastasis in endoscopically resected specimens. Cytokeratin immunohistochemistry is a potential option for evaluating tumor budding. The optimal cut-off value between low- and high-grade budding has not yet been determined, however, and the high inter-observer variability in selecting budding foci remains problematic. We explored the optimal cut-off value for predicting lymph node metastasis using cytokeratin immunohistochemistry, and developed a novel computer-assisted semiautomatic quantification method to reduce inter-observer variability. A retrospective single-institution study of 463 T1 colorectal cancer cases was conducted. Cases were split into derivation and validation datasets. Tumor budding foci were counted manually and semiautomatically using Image J software on cytokeratin immunohistochemistry-stained specimens. We determined the cut-off values and compared inter-observer variability among pathologists between the two methods. Univariate and multivariate analyses of the derivation dataset were performed to select the risk factors for lymph node metastasis. Predictive simulation for the validation dataset was conducted. The optimal cut-off values for the manual and semiautomatic methods were ≥10 and ≥12, respectively. For both methods, multivariate analyses revealed that venous invasion, lymphatic invasion, and high-grade tumor budding were independent risk factors for lymph node metastasis. The semiautomatic method provided significantly better inter-observer agreement. The predictive and observed lymph node metastasis frequencies were highly correlated in the validation dataset.
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