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Aims: Over 50% of breast cancer cases are "Human epidermal growth factor receptor 2 (HER2) low breast cancer (BC)", characterized by HER2 immunohistochemistry (IHC) scores of 1+ or 2+ alongside no amplification on fluorescence in situ hybridization (FISH) testing. The development of new anti‐HER2 antibody‐drug conjugates (ADCs) for treating HER2‐low breast cancers illustrates the importance of accurately assessing HER2 status, particularly HER2‐low breast cancer. In this study we evaluated the performance of a deep‐learning (DL) model for the assessment of HER2, including an assessment of the causes of discordances of HER2‐Null between a pathologist and the DL model. We specifically focussed on aligning the DL model rules with the ASCO/CAP guidelines, including stained cells' staining intensity and completeness of membrane staining. Methods and Results: We trained a DL model on a multicentric cohort of breast cancer cases with HER2‐IHC scores (n = 299). The model was validated on two independent multicentric validation cohorts (n = 369 and n = 92), with all cases reviewed by three senior breast pathologists. All cases underwent a thorough review by three senior breast pathologists, with the ground truth determined by a majority consensus on the final HER2 score among the pathologists. In total, 760 breast cancer cases were utilized throughout the training and validation phases of the study. The model's concordance with the ground truth (ICC = 0.77 [0.68–0.83]; Fisher P = 1.32e‐10) is higher than the average agreement among the three senior pathologists (ICC = 0.45 [0.17–0.65]; Fisher P = 2e‐3). In the two validation cohorts, the DL model identifies 95% [93% ‐ 98%] and 97% [91% ‐ 100%] of HER2‐low and HER2‐positive tumours, respectively. Discordant results were characterized by morphological features such as extended fibrosis, a high number of tumour‐infiltrating lymphocytes, and necrosis, whilst some artefacts such as nonspecific background cytoplasmic stain in the cytoplasm of tumour cells also cause discrepancy. Conclusion: Deep learning can support pathologists' interpretation of difficult HER2‐low cases. Morphological variables and some specific artefacts can cause discrepant HER2‐scores between the pathologist and the DL model. [ABSTRACT FROM AUTHOR]

Malignant mesothelioma (MM) is an aggressive cancer primarily diagnosed on the basis of histological criteria.sup.1. The 2015 World Health Organization classification subdivides mesothelioma tumors into three histological types: epithelioid, biphasic and sarcomatoid MM. MM is a highly complex and heterogeneous disease, rendering its diagnosis and histological typing difficult and leading to suboptimal patient care and decisions regarding treatment modalities.sup.2. Here we have developed a new approach--based on deep convolutional neural networks--called MesoNet to accurately predict the overall survival of mesothelioma patients from whole-slide digitized images, without any pathologist-provided locally annotated regions. We validated MesoNet on both an internal validation cohort from the French MESOBANK and an independent cohort from The Cancer Genome Atlas (TCGA). We also demonstrated that the model was more accurate in predicting patient survival than using current pathology practices. Furthermore, unlike classical black-box deep learning methods, MesoNet identified regions contributing to patient outcome prediction. Strikingly, we found that these regions are mainly located in the stroma and are histological features associated with inflammation, cellular diversity and vacuolization. These findings suggest that deep learning models can identify new features predictive of patient survival and potentially lead to new biomarker discoveries. Deep convolutional neural networks predict survival of mesothelioma patients and identify histological features associated with outcome that transcend current histological classifications., Author(s): Pierre Courtiol [sup.1] , Charles Maussion [sup.1] , Matahi Moarii [sup.1] , Elodie Pronier [sup.1] , Samuel Pilcer [sup.1] , Meriem Sefta [sup.1] , Pierre Manceron [sup.1] , Sylvain [...]