Prognostic and Predictive Value of Tumor Budding in Colorectal Cancer

Mitrovic, Bojana; Handley, Kelly; Assarzadegan, Naziheh; Chang, Hector Li; Dawson, Heather; Grin, Andrea; Hutchins, Gordon; Magill, Laura; Quirke, Philip; Riddell, Robert H.; Gray, Richard; Kirsch, Richard

doi:10.1016/j.clcc.2021.05.003

Cited by 14 publications

(11 citation statements)

References 41 publications

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“…Previously, biomarkers including TGP and tumor budding (TB) have been proposed to describe histomorphological changes of the tumor invasive margin [ 10 , 22 ]. TGP was first proposed by Jass et al.…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence-assisted analysis for tumor-immune interaction within the invasive margin of colorectal cancer

et al. 2023

Annals of Medicine

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Background In colorectal cancer (CRC), both tumor invasion and immunological analysis at the tumor invasive margin (IM) are significantly associated with patient prognosis, but have traditionally been reported independently. We propose a new scoring system, the TGP-I score, to assess the association and interactions between tumor growth pattern (TGP) and tumor infiltrating lymphocytes at the IM and to predict its prognostic validity for CRC patient stratification. Materials and Methods The types of TGP were assessed in hematoxylin and eosin-stained whole-slide images. The CD3 + T-cells density at the IM was automatically quantified on immunohistochemical-stained slides using a deep learning method. A discovery ( N = 347) and a validation ( N = 132) cohorts were used to evaluate the prognostic value of the TGP-I score for overall survival. Results The TGP-I score 3 (trichotomy) was an independent prognostic factor, with higher TGP-I score 3 associated with worse prognosis in the discovery (unadjusted hazard ratio [HR] for high vs. low 3.62, 95% confidence interval [CI] 2.22–5.90; p < 0.001) and validation cohort (unadjusted HR for high vs. low 5.79, 95% CI 1.84–18.20; p = 0.003). The relative contribution of each parameter to predicting survival was analyzed. The TGP-I score 3 had similar importance compared to tumor-node-metastasis staging (31.2% vs. 32.9%) and was stronger than other clinical parameters. Conclusions This automated workflow and the proposed TGP-I score could further provide accurate prognostic stratification and have potential value for supporting the clinical decision-making of stage I–III CRC patients. Key messages A new scoring system, the TGP-I score, was proposed to assess the association and interactions of TGP and TILs at the tumor invasive margin. TGP-I score could be an independent predictor of prognosis for CRC patients, with higher scores being associated with worse survival. TGP-I score had similar importance compared to tumor-node-metastasis staging and was stronger than other clinical parameters.

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Section: Discussionmentioning

confidence: 99%

Artificial intelligence-assisted analysis for tumor-immune interaction within the invasive margin of colorectal cancer

et al. 2023

Annals of Medicine

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“…Es wurde jedoch gezeigt, dass das Rezidivrisiko nach Chemotherapie bei Patienten mit „high grade“ Tumor Budding reduziert ist. Aktuell besteht keine grundsätzliche Indikation zur adjuvanten Chemotherapie bei Stadium-II-Karzinomen aufgrund des geringen Nutzens gegenüber den Nebenwirkungen (QUASAR-Studie), und diese wird nur bei einzelnen Subgruppen, wie etwa bei hoher Gradierung, pT4 oder extramuraler Veneninvasion in Erwägung gezogen [ 19 ]. Aufgrund des negativen prognostischen Werts von hohem Tumor Budding könnte es eine weitere Stütze für die klinische Entscheidung für eine adjuvante Therapie sein, jedoch ist die aktuelle Evidenz hierfür noch nicht ausreichend.…”

Section: Anwendungsbereich Stadium-ii-crcunclassified

Tumor Budding beim kolorektalen Karzinom – Informationen zur klinischen Anwendung und Anleitung zur praktischen Bestimmung

2021

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Zusammenfassung Hintergrund Bei einzelnen Patienten mit kolorektalen Karzinomen (CRC) zeigt sich ein schlechter klinischer Verlauf innerhalb desselben UICC-Stadiums (Union for International Cancer Control). Die Identifizierung von zusätzlichen Risikofaktoren ist daher notwendig, um eine optimale Therapieplanung zu erreichen. Fragestellung In welchen Situationen kann Tumor Budding die klinische Therapieentscheidung beeinflussen und wie sollte die standardisierte Auswertung erfolgen? Material und Methode Aktuelle Publikationen zum Thema Tumor Budding werden mit Fokus auf die praktische Anwendung und potenzielle Problemfälle in der Bestimmung des Tumor Buddings erläutert. Ergebnisse Tumor Budding ist ein signifikanter Risikofaktor für einen schlechteren Verlauf des CRC und kann bei pT1-Karzinomen sowie Stadium-II-Karzinomen die Behandlung beeinflussen. Die Auswertung wurde durch die International Tumor Budding Consensus Conference (ITBCC) 2016 standardisiert und ist in der Praxis anwendbar. Schwierigkeiten in der Anwendung können durch die Kenntnis von potenziellen Problemfällen vermieden werden.

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“…In CRC, tumor buds have emerged as a valuable prognostic factor [2,3]. They are associated with unfavorable pathological features that include mismatch repair stability, nodal metastasis, and poor tumor differentiation [4][5][6]. Tumor budding is a histological finding representing the dissociation/budding of malignant cells from tumor glands at the tumor-invasive front [2,3].…”

Section: Introductionmentioning

confidence: 99%

Enhancing colorectal cancer tumor bud detection using deep learning from routine H&E-stained slides

Sajjad,

Chen,

Rezapour

et al. 2024

Medical Imaging 2024: Digital and Computational Pathology

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Tumor budding refers to a cluster of one to four tumor cells located at the tumor-invasive front. While tumor budding is a prognostic factor for colorectal cancer, counting and grading tumor budding are time consuming and not highly reproducible. There could be high inter-and intra-reader disagreement on H&E evaluation. This leads to the noisy training (imperfect ground truth) of deep learning algorithms, resulting in high variability and losing their ability to generalize on unseen datasets. Pan-cytokeratin staining is one of the potential solutions to enhance the agreement, but it is not routinely used to identify tumor buds and can lead to false positives. Therefore, we aim to develop a weakly-supervised deep learning method for tumor bud detection from routine H&E-stained images that does not require strict tissue-level annotations. We also propose Bayesian Multiple Instance Learning (BMIL) that combines multiple annotated regions during the training process to further enhance the generalizability and stability in tumor bud detection. Our dataset consists of 29 colorectal cancer H&E-stained images that contain 115 tumor buds per slide on average. In six-fold cross-validation, our method demonstrated an average precision and recall of 0.94, and 0.86 respectively. These results provide preliminary evidence of the feasibility of our approach in improving the generalizability in tumor budding detection using H&E images while avoiding the need for non-routine immunohistochemical staining methods.

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Prognostic and Predictive Value of Tumor Budding in Colorectal Cancer

Cited by 14 publications

References 41 publications

Artificial intelligence-assisted analysis for tumor-immune interaction within the invasive margin of colorectal cancer

Artificial intelligence-assisted analysis for tumor-immune interaction within the invasive margin of colorectal cancer

Tumor Budding beim kolorektalen Karzinom – Informationen zur klinischen Anwendung und Anleitung zur praktischen Bestimmung

Enhancing colorectal cancer tumor bud detection using deep learning from routine H&E-stained slides

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