The prognostic value of cytonuclear grade in ductal carcinoma in situ (DCIS) is debated, partly due to high interobserver variability and the use of multiple guidelines. The aim of this study was to evaluate interobserver agreement in grading DCIS between Dutch, British, and American pathologists. Haematoxylin and eosin-stained slides of 425 women with primary DCIS were independently reviewed by nine breast pathologists based in the Netherlands, the UK, and the USA. Chance-corrected kappa (κ ma ) for association between pathologists was calculated based on a generalised linear mixed model using the ordinal package in R. Overall κ ma for grade of DCIS (low, intermediate, or high) was estimated to be 0.50 (95% confidence interval [CI] 0.44-0.56), indicating a moderate association between pathologists. When the model was adjusted for national guidelines, the association for grade did not change (κ ma = 0.53; 95% CI 0.48-0.57); subgroup analysis for pathologists using the UK pathology guidelines only had significantly higher association (κ ma = 0.58; 95% CI 0.56-0.61). To assess if concordance of grading relates to the expression of the oestrogen receptor (ER) and HER2, archived immunohistochemistry was analysed on a subgroup (n = 106). This showed that non-high grade according to the majority opinion was associated with ER positivity and HER2 negativity (100 and 89% of non-high grade cases, respectively). In conclusion, DCIS grade showed only moderate association using whole slide images scored by nine breast pathologists. As therapeutic decisions and inclusion in ongoing clinical trials are guided by DCIS grade, there is a pressing need to reduce interobserver variability in grading. ER and HER2 might be supportive to prevent the accidental and unwanted inclusion of high-grade DCIS in such trials.

AimsPrimary lung adenocarcinoma consists of a spectrum of clinical and pathological subtypes that may impact on overall survival (OS). Our study aims to evaluate the impact of adenocarcinoma subtype and intra-alveolar spread on survival after anatomical lung resection and identify different prognostic factors based on stage and histological subtype.MethodsNewly diagnosed patients undergoing anatomical lung resections without induction therapy, for pT1-3, N0-2 lung adenocarcinoma from April 2011 to March 2013, were included. The effect of clinical–pathological factors on survival was retrospectively assessed.ResultsTwo hundred and sixty-two patients were enrolled. The 1-year, 3-year and 5-year OS were 88.8%, 64.3% and 51.1%, respectively. Univariate analysis showed lymphovascular, parietal pleural and chest wall invasion to confer a worse 1-year and 5-year prognosis (all p<0.0001). Solid predominant adenocarcinomas exhibited a significantly worse OS (p=0.014). Multivariate analysis did not identify solid subtype as an independent prognostic factor; however, identified stage >IIa, lymphovascular invasion (p=0.002) and intra-alveolar spread (p=0.009) as significant independent predictors of worse OS. Co-presence of intra-alveolar spread and solid predominance significantly reduced OS. Disease-free survival (DFS) was reduced with parietal pleural (p=0.0007) and chest wall invasion (p<0.0001), however, adenocarcinoma subtype had no significant impact on DFS.ConclusionsOur study demonstrates that solid predominant adenocarcinoma, intra-alveolar spread and lymphovascular invasion confer a worse prognosis and should be used as a prognostic tool to determine appropriate adjuvant treatment.

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Computational pathology refers to applying deep learning techniques and algorithms to analyse and interpret histopathology images. Advances in artificial intelligence (AI) have led to an explosion in innovation in computational pathology, ranging from the prospect of automation of routine diagnostic tasks to the discovery of new prognostic and predictive biomarkers from tissue morphology. Despite the promising potential of computational pathology, its integration in clinical settings has been limited by a range of obstacles including operational, technical, regulatory, ethical, financial, and cultural challenges. Here, we focus on the pathologists’ perspective of computational pathology: we map its current translational research landscape, evaluate its clinical utility, and address the more common challenges slowing clinical adoption and implementation. We conclude by describing contemporary approaches to drive forward these techniques. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

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