Assessment of individual tumor buds using keratin immunohistochemistry: moderate interobserver agreement suggests a role for machine learning

High KRT17 expression in tumour budding indicates immunologically 'hot' tumour budding and predicts good survival in patients with colorectal cancer

Objectives

Emerging evidence has demonstrated that tumour budding (TB) is negatively associated with T-lymphocyte infiltration in CRC. Despite extensive research, the molecular characteristics of immunologically 'hot' TB remain poorly understood.

Methods

We quantified the number of TB by haematoxylin-eosin (H&E) sections and the densities of CD3+ and CD8+ T-lymphocytes by immunohistochemistry in a CRC cohort of 351 cases who underwent curative resection. We analysed the differential expression and T-lymphocyte infiltration score of 37 human epithelial keratins in CRC using RNA sequencing from the TCGA dataset. In 278 TB-positive cases, KRT17 expression was evaluated in tumour centre (TC) and TB with a staining score. Patient demographic, clinicopathological features and survival rates were analysed.

Results

In a CRC cohort of 351 cases, low-grade TB was associated with high CD3+ and CD8+ T-cell densities in the invasive margin (IM) but not in the TC. Of 37 human epithelial keratins, only KRT17 expression in TB had an apparent association with TB-grade and T-lymphocyte infiltration. In 278 TB-positive cases, high KRT17 expression in TB (KRT17TB) was negatively associated with low-grade TB and positively associated with high CD3+ and CD8+ T-cell densities in IM. High KRT17TB predicted early tumour grade, absence of lymph node metastasis and absence of tumour deposits. Additionally, patients with high KRT17TB had good overall survival and disease-free survival. Notably, low KRT17TB can specifically identify those patients with a poor prognosis among colorectal cancer patients with low TB and high T-lymphocyte infiltration.

Conclusions

KRT17 can be employed as a new indicator for distinguishing different immunological TBs.

Deep Learning and Colon Cancer Interpretation: Rise of the Machine

The rapidly evolving development of artificial intelligence (AI) has spurred the development of numerous algorithms that augment information obtained from routine pathologic review of hematoxylin and eosin-stained slides. AI tools that predict prognosis and underlying molecular alterations have been the focus of much of the research to date. The results of these studies highlight the tremendous potential of AI to enhance our pathology reports by providing rapid predictions of key features that influence therapy and outcomes.

Pathologists' first opinions on barriers and facilitators of computational pathology adoption in oncological pathology: an international study

Computational pathology (CPath) algorithms detect, segment or classify cancer in whole slide images, approaching or even exceeding the accuracy of pathologists. Challenges have to be overcome before these algorithms can be used in practice. We therefore aim to explore international perspectives on the future role of CPath in oncological pathology by focusing on opinions and first experiences regarding barriers and facilitators. We conducted an international explorative eSurvey and semi-structured interviews with pathologists utilizing an implementation framework to classify potential influencing factors. The eSurvey results showed remarkable variation in opinions regarding attitude, understandability and validation of CPath. Interview results showed that barriers focused on the quality of available evidence, while most facilitators concerned strengths of CPath. A lack of consensus was present for multiple factors, such as the determination of sufficient validation using CPath, the preferred function of CPath within the digital workflow and the timing of CPath introduction in pathology education. The diversity in opinions illustrates variety in influencing factors in CPath adoption. A next step would be to quantitatively determine important factors for adoption and initiate validation studies. Both should include clear case descriptions and be conducted among a more homogenous panel of pathologists based on sub specialization.

Fully Automated Tumor Bud Assessment in Hematoxylin and Eosin-Stained Whole Slide Images of Colorectal Cancer

Tumor budding (TB), the presence of single cells or small clusters of up to 4 tumor cells at the invasive front of colorectal cancer (CRC), is a proven risk factor for adverse outcomes. International definitions are necessary to reduce interobserver variability. According to the current international guidelines, hotspots at the invasive front should be counted in hematoxylin and eosin (H&E)-stained slides. This is time-consuming and prone to interobserver variability; therefore, there is a need for computer-aided diagnosis solutions. In this study, we report an artificial intelligence-based method for detecting TB in H&E-stained whole slide images. We propose a fully automated pipeline to identify the tumor border, detect tumor buds, characterize them based on the number of tumor cells, and produce a TB density map to identify the TB hotspot. The method outputs the TB count in the hotspot as a computational biomarker. We show that the proposed automated TB detection workflow performs on par with a panel of 5 pathologists at detecting tumor buds and that the hotspot-based TB count is an independent prognosticator in both the univariate and the multivariate analysis, validated on a cohort of n = 981 patients with CRC. Computer-aided detection of tumor buds based on deep learning can perform on par with expert pathologists for the detection and quantification of tumor buds in H&E-stained CRC histopathology slides, strongly facilitating the introduction of budding as an independent prognosticator in clinical routine and clinical trials.

QuPath Algorithm Accurately Identifies MLH1-Deficient Inflammatory Bowel Disease-Associated Colorectal Cancers in a Tissue Microarray

Current methods for analysing immunohistochemistry are labour-intensive and often confounded by inter-observer variability. Analysis is time consuming when identifying small clinically important cohorts within larger samples. This study trained QuPath, an open-source image analysis program, to accurately identify MLH1-deficient inflammatory bowel disease-associated colorectal cancers (IBD-CRC) from a tissue microarray containing normal colon and IBD-CRC. The tissue microarray (n = 162 cores) was immunostained for MLH1, digitalised, and imported into QuPath. A small sample (n = 14) was used to train QuPath to detect positive versus no MLH1 and tissue histology (normal epithelium, tumour, immune infiltrates, stroma). This algorithm was applied to the tissue microarray and correctly identified tissue histology and MLH1 expression in the majority of valid cases (73/99, 73.74%), incorrectly identified MLH1 status in one case (1.01%), and flagged 25/99 (25.25%) cases for manual review. Qualitative review found five reasons for flagged cores: small quantity of tissue, diverse/atypical morphology, excessive inflammatory/immune infiltrations, normal mucosa, or weak/patchy immunostaining. Of classified cores (n = 74), QuPath was 100% (95% CI 80.49, 100) sensitive and 98.25% (95% CI 90.61, 99.96) specific for identifying MLH1-deficient IBD-CRC; κ = 0.963 (95% CI 0.890, 1.036) (p < 0.001). This process could be efficiently automated in diagnostic laboratories to examine all colonic tissue and tumours for MLH1 expression.

High-yield areas to grade tumor budding in colorectal cancer: A practical approach for pathologists

BACKGROUND

Tumor budding (TB) has significant prognostic implication in stage II colorectal cancer (CRC) and is graded based on the International Tumor Budding Consensus Conference (ITBCC) protocol. In the current study, we evaluate tumor budding and its relationship to multiple histologic features in 104 tumors.

METHODS

One-hundred four resected CRC cases were retrieved. Tumor bud count and TB grade were compared to the final tumor bud count/TB grade of the tumor per ITBCC protocol. The following high-yield co-features were assessed in each slide: highest T stage, presence of benign mucosa, presence of a precursor lesion, and highest tumor volume.

RESULTS

Twenty-nine (28 %) cases had discrepancies between slide TB grade and final TB grade. The least discrepancies were seen in slides with benign mucosa (7 %) and precursor lesions (7 %). Among stage II patients without high-risk features, no discrepancies were observed in slides with benign mucosa. Slides with deepest invasion (r_s = 1.000, p = 0.01) and benign mucosa (r_s = 0.957, p < 0.001) had the strongest correlation with final tumor bud count in the same stage II subgroup. Similar relationships were observed when comparing final TB grade. Deepest invasion, tumor volume, as well as lymphovascular invasion, when present, also showed strong correlations with final TB grade in the entire cohort (r_s = 0.828-0.845, p < 0.001).

CONCLUSION

Our study is the first study to evaluate the relationship between TB grade and co-existing histologic features. We highlight the benefit of focusing on slides with high-yield co-features, with the strongest correlation seen in slides with adjacent benign mucosa and precursor lesions.

Semi-Supervised Learning to Automate Tumor Bud Detection in Cytokeratin-Stained Whole-Slide Images of Colorectal Cancer

Tumor budding is a histopathological biomarker associated with metastases and adverse survival outcomes in colorectal carcinoma (CRC) patients. It is characterized by the presence of single tumor cells or small clusters of cells within the tumor or at the tumor-invasion front. In order to obtain a tumor budding score for a patient, the region with the highest tumor bud density must first be visually identified by a pathologist, after which buds will be counted in the chosen hotspot field. The automation of this process will expectedly increase efficiency and reproducibility. Here, we present a deep learning convolutional neural network model that automates the above procedure. For model training, we used a semi-supervised learning method, to maximize the detection performance despite the limited amount of labeled training data. The model was tested on an independent dataset in which human- and machine-selected hotspots were mapped in relation to each other and manual and machine detected tumor bud numbers in the manually selected fields were compared. We report the results of the proposed method in comparison with visual assessment by pathologists. We show that the automated tumor bud count achieves a prognostic value comparable with visual estimation, while based on an objective and reproducible quantification. We also explore novel metrics to quantify buds such as density and dispersion and report their prognostic value. We have made the model available for research use on the grand-challenge platform.

Quantitative Pathologic Analysis of Digitized Images of Colorectal Carcinoma Improves Prediction of Recurrence-Free Survival

BACKGROUND & AIMS

To examine whether quantitative pathologic analysis of digitized hematoxylin and eosin slides of colorectal carcinoma (CRC) correlates with clinicopathologic features, molecular alterations, and prognosis.

METHODS

A quantitative segmentation algorithm (QuantCRC) was applied to 6468 digitized hematoxylin and eosin slides of CRCs. Fifteen parameters were recorded from each image and tested for associations with clinicopathologic features and molecular alterations. A prognostic model was developed to predict recurrence-free survival using data from the internal cohort (n = 1928) and validated on an internal test (n = 483) and external cohort (n = 938).

RESULTS

There were significant differences in QuantCRC according to stage, histologic subtype, grade, venous/lymphatic/perineural invasion, tumor budding, CD8 immunohistochemistry, mismatch repair status, KRAS mutation, BRAF mutation, and CpG methylation. A prognostic model incorporating stage, mismatch repair, and QuantCRC resulted in a Harrell's concordance (c)-index of 0.714 (95% confidence interval [CI], 0.702-0.724) in the internal test and 0.744 (95% CI, 0.741-0.754) in the external cohort. Removing QuantCRC from the model reduced the c-index to 0.679 (95% CI, 0.673-0.694) in the external cohort. Prognostic risk groups were identified, which provided a hazard ratio of 2.24 (95% CI, 1.33-3.87, P = .004) for low vs high-risk stage III CRCs and 2.36 (95% CI, 1.07-5.20, P = .03) for low vs high-risk stage II CRCs, in the external cohort after adjusting for established risk factors. The predicted median 36-month recurrence rate for high-risk stage III CRCs was 32.7% vs 13.4% for low-risk stage III and 15.8% for high-risk stage II vs 5.4% for low-risk stage II CRCs.

CONCLUSIONS

QuantCRC provides a powerful adjunct to routine pathologic reporting of CRC. A prognostic model using QuantCRC improves prediction of recurrence-free survival.

Development of a semi-automated method for tumour budding assessment in colorectal cancer and comparison with manual methods

AIMS

Tumour budding (TB) is an established prognostic feature in multiple cancers but is not routinely assessed in pathology practice. Efforts to standardise and automate assessment have shifted from haematoxylin and eosin (H&E)-stained images towards cytokeratin immunohistochemistry. The aim of this study was to compare manual H&E and cytokeratin assessment methods with a semi-automated approach built within QuPath open-source software.

METHODS AND RESULTS

TB was assessed in cores from the advancing tumour edge in a cohort of stage II/III colon cancers (n = 186). The total numbers of buds detected with each method were as follows: manual H&E, n = 503; manual cytokeratin, n = 2290; and semi-automated, n = 5138. More than four times the number of buds were identified manually with cytokeratin assessment than with H&E assessment. One thousand seven hundred and thirty-four individual buds were identified with both manual and semi-automated assessments applied to cytokeratin images, representing 75.7% of the buds identified manually (n = 2290) and 33.7% of the buds detected with the semi-automated method (n = 5138). Higher semi-automated TB scores were due to any discrete area of cytokeratin immunopositivity within an accepted area range being identified as a bud, regardless of shape or crispness of definition, and to the inclusion of tumour cell clusters within glandular lumina ('luminal pseudobuds'). Although absolute numbers differed, semi-automated and manual bud counts were strongly correlated across cores (ρ = 0.81, P < 0.0001). All methods of TB assessment demonstrated poorer survival associated with higher TB scores.

CONCLUSIONS

We present a new QuPath-based approach to TB assessment, which compares favourably with established methods and offers a freely available, rapid and transparent tool that is also applicable to whole slide images.

Novel Criteria for Intratumoral Budding with Prognostic Relevance for Colon Cancer and Its Histological Subtypes

Peritumoral budding and intratumoral budding (ITB) are important prognostic factors for colorectal cancer patients. Scientists worldwide have investigated the role of budding in tumor progression and its prognosis, but guidelines for reliably identifying tumor buds based on morphology are lacking. In this study, next-generation tissue microarray (ngTMA^®) construction was used for tumor bud evaluation, and highly detailed rule-out annotation was used for tumor definition in pancytokeratin-stained tissue sections. Initially, tissues of 245 colon cancer patients were evaluated with high interobserver reliability, and a concordance of 96% was achieved. It was shown that high ITB scores were associated with poor distant metastasis-free survival (p = 0.006 with a cut-off of ≥10 buds). This cut-off was defined as the best maximum value from one of two/three ngTMA^® cores (0.6 mm diameter). ITB in 30 cases of mucinous, medullary, and signet ring cell carcinoma was analyzed for the subsequent determination of differences in tumor bud analyses between those subtypes. In conclusion, blinded randomized punched cores in the tumor center can be useful for ITB detection. It can be assumed that this method is suitable for its adoption in clinical routines.

Tumor budding to investigate local invasion, metastasis and prognosis in temporal bone squamous cell carcinoma

BACKGROUND

Few studies have tried to go beyond the conventional clinic-pathological prognostic factors investigating the molecular markers involved in the biology of temporal bone squamous cell carcinoma (TBSSC). Tumor budding represents a very aggressive subpopulation of cancer cells and demonstrates the heterogeneity of cancer cells residing in different locations within tumors. The main aim of this exploratory study was to investigate the role of tumor budding in primary TBSCC prognosis. We also analyzed the association between TBSCC tumor budding and: (i) loco-regional aggressiveness evaluated according to the revised Pittsburgh staging system, (ii) tumor infiltrating lymphocytes, lymphovascular invasion (LVSI), perineural invasion, pattern of invasion, and type of stroma.

METHODS

Thirty-two TBSCCs treated surgically were considered. The three-tier grading system recommended by the International Tumor Budding Consensus Conference was used first on TBSCC.

RESULTS

Advanced (T3-4) TBSCC was related with high risk intra-tumoral budding (ITB) at two-tier risk grading (p = 0.0361). N + status was associated with intermediate/high budding (Bd2-3) at three-tier risk grading for peri-tumoral budding (PTB) (p = 0.0382). Disease-free survival (DFS) was related with T-stage (p = 0.0406), N-status (p < 0.0001), PTB two-tier risk grading (p = 0.0463), LVSI (p < 0.0001). Overall survival (OS) was associated with N-status (p = 0.0167), PTB absolute count (p = 0.0341), PTB three-tier risk grading (p = 0.0359), PTB two-tier risk grading (p = 0.0132), and LVSI (p = 0.0004). At the multivariate analysis, DFS was related with N-status (p = 0.0147) and LVSI (p < 0.0001), while OS resulted associated only with LVSI (p = 0.0144).

CONCLUSIONS

Our preliminary findings suggest that tumor budding in TBSCC, regardless of its localization (the main tumor body [ITB] or invasive front [PTB]) may be a reliable predictor of neck lymph node metastasis and poor prognosis. Tumor budding and LVI could be predictive markers for precise treatment in TBSCC. Further investigations on larger prospective series should be designed to confirm this evidence both in post-operative specimens and in preoperative biopsies.

Stroma AReactive Invasion Front Areas (SARIFA)-A New Easily to Determine Biomarker in Colon Cancer-Results of a Retrospective Study

Simple Summary

Many studies have used histomorphological features to more precisely predict the prognosis of patients with colon cancer, focusing on tumor budding, poorly differentiated clusters, and the tumor–stroma ratio. Here, we introduce SARIFA: Stroma AReactive Invasion Front Area(s). We defined SARIFA as the direct contact between a tumor gland/tumor cell cluster (≥5 cells) and inconspicuous surrounding adipose tissue in the invasion front. SARIFA shows an excellent interobserver reliability and high prognostic value and is thus a promising histomorphological prognostic indicator for adipose-infiltrative adenocarcinomas of the colon.

Abstract

Many studies have used histomorphological features to more precisely predict the prognosis of patients with colon cancer, focusing on tumor budding, poorly differentiated clusters, and the tumor–stroma ratio. Here, we introduce SARIFA: Stroma AReactive Invasion Front Area(s). We defined SARIFA as the direct contact between a tumor gland/tumor cell cluster (≥5 cells) and inconspicuous surrounding adipose tissue in the invasion front. In this retrospective, single-center study, we classified 449 adipose-infiltrative adenocarcinomas (not otherwise specified) from two groups based on SARIFA and found 25% of all tumors to be SARIFA-positive. Kappa values between the two pathologists were good/very good: 0.77 and 0.87. Patients with SARIFA-positive tumors had a significantly shorter colon-cancer-specific survival (p = 0.008, group A), absence of metastasis, and overall survival (p < 0.001, p = 0.003, group B). SARIFA was significantly associated with adverse features such as pT4 stage, lymph node metastasis, tumor budding, and higher tumor grade. Moreover, SARIFA was confirmed as an independent prognostic indicator for colon-cancer-specific survival (p = 0.011, group A). SARIFA assessment was very quick (<1 min). Because of low interobserver variability and good prognostic significance, SARIFA seems to be a promising histomorphological prognostic indicator in adipose-infiltrative adenocarcinomas of the colon. Further studies should validate our results and also determine whether SARIFA is a universal prognostic indicator in solid cancers.

Development and initial validation of a deep learning algorithm to quantify histological features in colorectal carcinoma including tumour budding/poorly differentiated clusters

AIMS

To develop and validate a deep learning algorithm to quantify a broad spectrum of histological features in colorectal carcinoma.

METHODS AND RESULTS

A deep learning algorithm was trained on haematoxylin and eosin-stained slides from tissue microarrays of colorectal carcinomas (N = 230) to segment colorectal carcinoma digitised images into 13 regions and one object. The segmentation algorithm demonstrated moderate to almost perfect agreement with interpretations by gastrointestinal pathologists, and was applied to an independent test cohort of digitised whole slides of colorectal carcinoma (N = 136). The algorithm correctly classified mucinous and high-grade tumours, and identified significant differences between mismatch repair-proficient and mismatch repair-deficient (MMRD) tumours with regard to mucin, inflammatory stroma, and tumour-infiltrating lymphocytes (TILs). A cutoff of >44.4 TILs per mm² carcinoma gave a sensitivity of 88% and a specificity of 73% in classifying MMRD carcinomas. Algorithm measures of tumour budding (TB) and poorly differentiated clusters (PDCs) outperformed TB grade derived from routine sign-out, and compared favourably with manual counts of TB/PDCs with regard to lymphatic, venous and perineural invasion. Comparable associations were seen between algorithm measures of TB/PDCs and manual counts of TB/PDCs for lymph node metastasis (all P < 0.001); however, stronger correlations were seen between the proportion of positive lymph nodes and algorithm measures of TB/PDCs. Stronger associations were also seen between distant metastasis and algorithm measures of TB/PDCs (P = 0.004) than between distant metastasis and TB (P = 0.04) and TB/PDC counts (P = 0.06).

CONCLUSIONS

Our results highlight the potential of deep learning to identify and quantify a broad spectrum of histological features in colorectal carcinoma.

Improving tumor budding reporting in colorectal cancer: a Delphi consensus study

Tumor budding is a long-established independent adverse prognostic marker in colorectal cancer, yet methods for its assessment have varied widely. In an effort to standardize its reporting, a group of experts met in Bern, Switzerland, in 2016 to reach consensus on a single, international, evidence-based method for tumor budding assessment and reporting (International Tumor Budding Consensus Conference [ITBCC]). Tumor budding assessment using the ITBCC criteria has been validated in large cohorts of cancer patients and incorporated into several international colorectal cancer pathology and clinical guidelines. With the wider reporting of tumor budding, new issues have emerged that require further clarification. To better inform researchers and health-care professionals on these issues, an international group of experts in gastrointestinal pathology participated in a modified Delphi process to generate consensus and highlight areas requiring further research. This effort serves to re-affirm the importance of tumor budding in colorectal cancer and support its continued use in routine clinical practice.

Lymph Node Tumor Burden Correlates With Tumor Budding and Poorly Differentiated Clusters: A New Prognostic Factor in Colorectal Carcinoma?

INTRODUCTION

Molecular lymph node (LN) staging in early colorectal cancer (CRC) has demonstrated to be more precise than conventional histopathology pN staging. Tumor budding (TB) and poorly differentiated clusters (PDCs) are associated with LN metastases, recurrences, and lower survival in CRC. We evaluated the correlation between the total tumor load (TTL) in LNs from CRC surgical specimens with patient outcome, TB, and PDC.

METHODS

In this retrospective multicentre study, 5,931 LNs from 342 stage I-III CRC were analyzed by both hematoxylin and eosin and molecular detection of tumor cytokeratin 19 mRNA by one-step nucleic acid amplification. TB and PDC were evaluated by hematoxylin and eosin and cytokeratin 19 immunohistochemistry.

RESULTS

One-step nucleic acid was positive in 38.3% patients (n = 131). Tumor Budding was low in 45% cases, intermediate in 25%, and high in 30%. Poorly Differentiated Clusters were low-grade G1 in 53%, G2 in 32%, and G3 in 15%. TB and PDC correlated with TTL, high-grade, lymphovascular and perineural invasion, pT, pN and stage (P < 0.001). TB, PDC, and TTL ≥ 6,000 copies/µL were associated with worse overall survival (P = 0.002, P = 0.013, and P = 0.046) and disease-free survival (P < 0.001).

DISCUSSION

The implementation of more sensitive molecular methods to assess LN status is a promising alternative approach to pN staging, which could be integrated to other factors to help risk stratification and management of patients with early-stage CRC. This study demonstrates the correlation of the amount of LN tumor burden with TB and PDCs. TTL is related to the outcome and could be used as a new prognostic factor in CRC (see Visual Abstract, Supplementary Digital Content 2, http://links.lww.com/CTG/A512).

Subcellular Expression of Maspin in Colorectal Cancer: Friend or Foe

In this review the authors aimed to emphasize the practical value of nuclear expression of the mammary serine protease inhibitor (maspin), also known as serpin B5 protein, in colorectal carcinoma (CRC), from pre-malignant disorders to carcinogenesis and metastasis. As the role of maspin is controversial and not yet understood, the present update highlights the latest data revealed by literature which were filtrated through the daily experience of the authors, which was gained at microscopic examination of maspin expression in CRCs and other tumors for daily diagnosis. Data regarding the subcellular localization of maspin, in correlation with the microsatellite status, grade of tumor dedifferentiation, and epithelial-mesenchymal transition (EMT) phenomenon of the tumor buds were presented with details. An original observation refers to the maspin capacity to mark the tumor cells which are "at the point of budding" that were previously considered as having "hybrid EMT phenotype". It refers to the transitional status of tumor cell that is between "epithelial status" and "mesenchymal status". The second original hypothesis highlights the possible role of maspin in dysregulating the intestinal microbiota, in patients with idiopathic inflammatory bowel diseases (IBD) and inducing IBD-related CRC. The dynamic process of budding and EMT of tumor buds, possible mediated by maspin, needs further investigation and validation in many human CRC samples. The histological and molecular data reveal that synthesis of maspin-based therapeutics might represent a novel individualized therapeutic strategy for patients with CRC.

Prognostic impact of tumor budding in endometrial carcinoma within distinct molecular subgroups

Tumor budding is a robust prognostic parameter in several tumor entities but is rarely investigated in endometrial carcinoma. We applied the recently standardized counting method from the International Tumor Budding Consensus Conference for colorectal cancer (ITBCC) on a cohort of 255 endometrial carcinomas with known molecular profiles according to The Cancer Genome Atlas (TCGA) subgroups. Our investigation aims to clarify the potential prognostic role of tumor budding in endometrial carcinoma in contrast to other known prognostic factors, including molecular factors. In addition, the microcystic elongated and fragmented (MELF) pattern and tumor budding were compared with respect to their potential as markers for epithelial-mesenchymal transition (EMT). Tumor budding was found in n = 67 (26.3%) tumors, with a very low mean of 0.7 buds per ×20 HE field. Tumor budding was significantly associated with depth of invasion, nodal status, lymphatic invasion (each p < 0.001), grading (p = 0.004), and vascular invasion (p = 0.01). Tumor budding showed moderate inter-observer-variability with prognostic stratification irrespective of the observer (κ-value = 0.448). In multivariate analysis, tumor budding served as a significant independent prognosticator for worse outcomes in overall and recurrence-free survival (HR 2.376 and 2.736, p < 0.001), but not when the TCGA subgroups entered into the analysis. In consequence, dependency had to be clarified in the subgroup analysis for Polymerase E mutated (POLEmut), mismatch repair deficient (MMRdef), nonspecific mutation profile (NSMP), and P53 aberrant (P53abn) endometrial carcinomas. A particular impact was identified in the intermediate prognostic groups of NSMP and MMRdef carcinomas. Tumor budding outperformed the MELF pattern in single and combined prognostic information. In conclusion, the presence of tumor budding alone is a promising, robust, and easy-to-apply prognostic parameter in endometrial carcinoma. In a morpho-molecular approach, it exerts its prognostic potential in the most clinically relevant subgroups of endometrial carcinoma and serves as a good biomarker for EMT.

Taking tumour budding to the next frontier - a post International Tumour Budding Consensus Conference (ITBCC) 2016 review

Tumour budding in colorectal cancer, defined as single tumour cells or small clusters containing four or fewer tumour cells, is a robust and independent biomarker of aggressive tumour biology. On the basis of published data in the literature, the evidence is certainly in favour of reporting tumour budding in routine practice. One important aspect of implementing tumour budding has been to establish a standardised and evidence-based scoring method, as was recommended by the International Tumour Budding Consensus Conference (ITBCC) in 2016. Further developments have aimed at establishing methods for automated tumour budding assessment. A digital approach to scoring tumour buds has great potential to assist in performing an objective budding count but, like the manual consensus method, must be validated and standardised. The aim of the present review is to present general considerations behind the ITBCC scoring method, and a broad overview of the current situation and challenges regarding automated tumour budding detection methods.

Reproducibility of tumor budding assessment in pancreatic cancer based on a multicenter interobserver study

Tumor budding has been reported to be an independent prognostic factor in pancreatic ductal adenocarcinoma (PDAC). Its use in daily diagnostics would improve the prognostic stratification of patients. We performed a multicenter interobserver study to test various budding assessment methods for their reproducibility. Two serial sections of 50 resected, treatment-naïve PDACs were stained for Hematoxylin and Eosin (H&E) and pancytokeratin. Tumor budding was scored by independent observers at five participating centers in Switzerland, Germany, and Canada. Pathologists assessed tumor budding on a digital platform comparing H&E with pancytokeratin staining in 10 high-power fields (10HPF) and one HPF hotspot (1HPF). Additionally, tumor budding was assessed in one H&E hotspot at × 20 magnification, as suggested by the International Tumor Budding Consensus Conference (ITBCC). Correlation coefficients for bud counts between centers ranged from r = 0.58648 to r = 0.78641 for H&E and from r = 0.69288 to r = 0.81764 for pancytokeratin. The highest interobserver agreement across all centers was observed for pancytokeratin 10HPFs (ICC = 0.6). ICC values were 0.49, 0.48, 0.41, and 0.4 for H&E in 1HPF hotspot, H&E in 10HPFs, pancytokeratin in 1HPF, and H&E in one hotspot at ×20, respectively (ITBCC method). This interobserver study reveals a range between moderately poor to moderate agreement levels between pathologists for the different tumor budding assessment methods in PDAC. Acceptable levels of agreement were reached with the pancytokeratin 10HPF method, which can thus be recommended for the assessment of tumor budding in PDAC resection specimens. To improve the levels of interobserver agreement, the implementation of machine learning applications should be considered.

The histopathological diagnosis of atypical meningioma: glass slide versus whole slide imaging for grading assessment

Limited studies on whole slide imaging (WSI) in surgical neuropathology reported a perceived limitation in the recognition of mitoses. This study analyzed and compared the inter- and intra-observer concordance for atypical meningioma, using glass slides and WSI. Two neuropathologists and two residents assessed the histopathological features of 35 meningiomas-originally diagnosed as atypical-in a representative glass slide and corresponding WSI. For each histological parameter and final diagnosis, we calculated the inter- and intra-observer concordance in the two viewing modes and the predictive accuracy on recurrence. The concordance rates for atypical meningioma on glass slides and on WSI were 54% and 60% among four observers and 63% and 74% between two neuropathologists. The inter-observer agreement was higher using WSI than with glass slides for all parameters, with the exception of high mitotic index. For all histological features, we found median intra-observer concordance of ≥ 79% and similar predictive accuracy for recurrence between the two viewing modes. The higher concordance for atypical meningioma using WSI than with glass slides and the similar predictive accuracy for recurrence in the two modalities suggest that atypical meningioma may be safely diagnosed using WSI.

Interobserver Variability in the Assessment of Tumor Budding in pT 3/4 Colon Cancer: Improvement by Supporting Immunohistochemistry?

The prognostic significance of tumor budding in colon cancer is unequivocally documented, and the recommendations of the International Tumor Budding Consensus Conference (ITBCC) are currently the accepted basis for its assessment. Up to now, it is unknown whether the general use of a supporting cytokeratin immunohistochemistry can improve the interobserver variability and prognostic significance. Six investigators with different levels of experience reassessed 229 cases of colon carcinoma (pT3/4, N+/−, M0) with a supporting cytokeratin immunohistochemistry. The results were compared to previous assessments, which have been performed only on H & E. Bd3 was significantly associated with the occurrence of distant metastases according to the assessments of three out of six investigators (p < 0.05). Only one single investigator reached significant results concerning the cancer specific survival (p = 0.01). The pairwise kappa values range between a poor and moderate level of agreement (range 0.17–0.45; median 0.21). In conclusion, the results show no superiority of the use of an additional cytokeratin immunohistochemistry compared to the conventional analysis on sole H & E slides. Therefore, the general supporting use of a cytokeratin immunohistochemical staining seems to be inadvisable in colon cancer in consideration of necessary resources and costs.

Tumour budding in solid cancers

Tumour budding is an emerging prognostic biomarker in colorectal cancer (CRC) and other solid cancers. Tumour buds are usually defined as isolated single cancer cells or clusters of up to four cancer cells located at the invasive tumour front. The prognostic value of tumour budding is now supported by a large body of evidence, whereas the utility of this phenotype as a predictive biomarker remains under investigation. The application of tumour budding indices in clinical practice requires a standardized scoring system that can be tailored to specific tumour types and clinical scenarios. In the context of CRC, tumour budding can be assessed according to the method agreed at the International Tumour Budding Consensus Conference (ITBCC) in 2016. Using the ITBCC scoring system, tumour budding is an independent predictor of lymph node metastasis in patients with pT1 CRC and of unfavourable survival in patients with stage II colon cancer. Regardless of the clinical scenario or tumour type, the assertion that 'the more tumour buds, the worse the clinical outcome' applies. In this Review, we provide an overview of tumour budding in solid cancers, highlighting the molecular and biological aspects of this phenomenon, including its associations with epithelial-mesenchymal transition and features of the tumour microenvironment. We also describe the available evidence demonstrating the value of tumour budding as a biomarker across various solid cancers.

Tumour budding and its clinical implications in gastrointestinal cancers

Tumour budding in colorectal cancer has become an important prognostic factor. Represented by single cells or small tumour cell clusters at the invasion front of the tumour mass, these tumour buds seem to reflect cells in a ‘hybrid’ state of epithelial–mesenchymal transition, and evidence indicates that the presence of these entities is associated with lymph node metastasis, local recurrence and distant metastatic disease. The International Tumour Budding Consensus Conference (ITBCC) has highlighted a scoring system for the reporting of tumour budding in colorectal cancer, as well as different clinical scenarios that could affect patient management. Other organs are not spared: tumour budding has been described in numerous gastrointestinal and non-gastrointestinal cancers. Here, we give an update on ITBCC validation studies in the context of colorectal cancer and the clinical implications of tumour budding throughout the upper gastrointestinal and pancreatico-biliary tract.