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Abstract: Background: Nasopharyngeal carcinoma (NPC) treatment is mainly based on clinical staging. We hypothesize that better understanding of the molecular heterogeneity of NPC can aid in better treatment decisions. Therefore, the purpose of this study was to present our exploration of cancer gene copy‐number alterations (CNAs) of Epstein‐Barr virus (EBV)‐positive and EBV‐negative NPC. Methods: Multiplex ligation‐dependent probe amplification was applied to detect CNAs of 36 cancer genes (n = 103). Correlation between CNAs, clinicopathological features, and survival were examined. Results: The CNAs occurred significantly more in EBV‐negative NPC, with PIK3CA and MCCC1 (P < .001) gain/amplification occurring more frequently. Gain/amplification of cyclin‐L1 (CCNL1) and PTK2 (P < .001) predict worse disease‐free survival (DFS) in EBV‐positive NPC. Conclusion: The EBV‐positive and EBV‐negative NPC show some similarities in cancer gene CNAs suggesting a common pathogenic route but also important differences possibly indicating divergence in oncogenesis. Copy number gain/amplification of CCNL1 and PTK2 are possibly good predictors of survival in EBV‐positive NPC. [ABSTRACT FROM AUTHOR]

Manual counting of mitotic tumor cells in tissue sections constitutes one of the strongest prognostic markers for breast cancer. This procedure, however, is time-consuming and error-prone. We developed a method to automatically detect mitotic figures in breast cancer tissue sections based on convolutional neural networks (CNNs). Application of CNNs to hematoxylin and eosin (H&E) stained histological tissue sections is hampered by noisy and expensive reference standards established by pathologists, lack of generalization due to staining variation across laboratories, and high computational requirements needed to process gigapixel whole-slide images (WSIs). In this paper, we present a method to train and evaluate CNNs to specifically solve these issues in the context of mitosis detection in breast cancer WSIs. First, by combining image analysis of mitotic activity in phosphohistone-H3 restained slides and registration, we built a reference standard for mitosis detection in entire H&E WSIs requiring minimal manual annotation effort. Second, we designed a data augmentation strategy that creates diverse and realistic H&E stain variations by modifying H&E color channels directly. Using it during training combined with network ensembling resulted in a stain invariant mitosis detector. Third, we applied knowledge distillation to reduce the computational requirements of the mitosis detection ensemble with a negligible loss of performance. The system was trained in a single-center cohort and evaluated in an independent multicenter cohort from the cancer genome atlas on the three tasks of the tumor proliferation assessment challenge. We obtained a performance within the top three best methods for most of the tasks of the challenge. [ABSTRACT FROM AUTHOR]

Background The presence of lymph node metastases is one of the most important factors in breast cancer prognosis. The most common way to assess regional lymph node status is the sentinel lymph node procedure. The sentinel lymph node is the most likely lymph node to contain metastasized cancer cells and is excised, histopathologically processed, and examined by a pathologist. This tedious examination process is time-consuming and can lead to small metastases being missed. However, recent advances in whole-slide imaging and machine learning have opened an avenue for analysis of digitized lymph node sections with computer algorithms. For example, convolutional neural networks, a type of machine-learning algorithm, can be used to automatically detect cancer metastases in lymph nodes with high accuracy. To train machine-learning models, large, well-curated datasets are needed. Results We released a dataset of 1,399 annotated whole-slide images (WSIs) of lymph nodes, both with and without metastases, in 3 terabytes of data in the context of the CAMELYON16 and CAMELYON17 Grand Challenges. Slides were collected from five medical centers to cover a broad range of image appearance and staining variations. Each WSI has a slide-level label indicating whether it contains no metastases, macro-metastases, micro-metastases, or isolated tumor cells. Furthermore, for 209 WSIs, detailed hand-drawn contours for all metastases are provided. Last, open-source software tools to visualize and interact with the data have been made available. Conclusions A unique dataset of annotated, whole-slide digital histopathology images has been provided with high potential for re-use. [ABSTRACT FROM AUTHOR]