H3K27me3 Immunohistochemical Loss Predicts Lower Response to Neo-Adjuvant Chemo-Radiotherapy in Rectal Carcinoma

A watch-and-wait approach was suggested to avoid the possible complications related to surgery in patients with rectal carcinoma showing clinical complete response after neoadjuvant chemo-radiotherapy (CRT). Since clinical response may not correlate with pathological response, markers with higher accuracy are needed to identify patients who are likely responders and could be spared surgery. This study aims to assess whether H3K27me3 immunohistochemical expression in pre-treatment rectal carcinoma predicts response to neoadjuvant CRT or shows prognostic relevance. We assessed H3K27me3 immunostaining in 46 endoscopic biopsies of rectal carcinomas treated with neoadjuvant CRT and surgery. H3K27me3 immunostaining was lost in 20, retained in 19, and inconclusive (absent in neoplastic and non-neoplastic cells) in 7 cases. Retained H3K27me3 immuno-expression was significantly associated with ypTNM stage 0 (p = 0.0111) and high tumor regression, measured using either five-tiered (p = 0.0042) or two-tiered Dworak tumor regression grade (p = 0.0009). Poor differentiation, determined counting the number of poorly differentiated clusters (PDC grade) or tumor budding (TB) foci (TB grade), in the pre-treatment biopsy, was significantly associated with a shorter time to progression after surgery (p = 0.008; p = 0.0093). However, only PDC grade (p = 0.0023), together with radial margin involvement (p = 0.0001), retained prognostic significance in the multivariate analysis. The assessment of H3K27me3 immunostaining in pre-treatment endoscopic biopsy of rectal carcinoma could be useful to predict response to neo-adjuvant CRT and to identify patients who could safely undergo watch-and-wait approach. PDC and TB grade in the pre-treatment biopsy could provide additional prognostic information in patients with rectal carcinoma treated with neoadjuvant CRT and surgery

Value of Artificial Intelligence in Evaluating Lymph Node Metastases

One of the most relevant prognostic factors in cancer staging is the presence of lymph node (LN) metastasis. Evaluating lymph nodes for the presence of metastatic cancerous cells can be a lengthy, monotonous, and error-prone process. Owing to digital pathology, artificial intelligence (AI) applied to whole slide images (WSIs) of lymph nodes can be exploited for the automatic detection of metastatic tissue. The aim of this study was to review the literature regarding the implementation of AI as a tool for the detection of metastases in LNs in WSIs. A systematic literature search was conducted in PubMed and Embase databases. Studies involving the application of AI techniques to automatically analyze LN status were included. Of 4584 retrieved articles, 23 were included. Relevant articles were labeled into three categories based upon the accuracy of AI in evaluating LNs. Published data overall indicate that the application of AI in detecting LN metastases is promising and can be proficiently employed in daily pathology practice

Digital pathology world tour

Objective Digital pathology (DP) is currently in the spotlight and is rapidly gaining ground, even though the history of this field spans decades. Despite great technological progress, the adoption of DP for routine clinical diagnostic use remains limited.Methods A systematic search was conducted in the electronic databases Pubmed-MEDLINE and Embase. Inclusion criteria were all published studies that encompassed any application of DP.Results Of 4888 articles retrieved, 4041 were included. Relevant articles were categorized as "diagnostic" (147/4041, 4%) where DP was utilized for routine diagnostic workflow and "non-diagnostic" (3894/4041, 96%) for all other applications. The "non-diagnostic" articles were further categorized according to DP application including "artificial intelligence" (33%), "education" (5%), "narrative" (17%) for reviews and editorials, and "technical" (45%) for pure research publications.Conclusion This manuscript provided temporal and geographical insight into the global adoption of DP by analyzing the published scientific literature