Current staging systems of cancer are mainly based on the anatomical extent of disease. They need refinement by biological parameters to improve stratification of patients for tumor therapy or surveillance strategies. Thanks to developments in genomic, transcriptomic, and big-data technologies, we are now able to explore molecular characteristics of tumors in detail and determine their clinical relevance. This has led to numerous prognostic and predictive gene expression signatures that have the potential to establish a classification of tumor subgroups by biological determinants. However, only a few gene signatures have reached the stage of clinical implementation so far. In this review article, we summarize the current status, and present and future challenges of prognostic gene signatures in three relevant cancer entities: breast cancer, colorectal cancer, and hepatocellular carcinoma.

Background: Hepatocellular carcinoma (HCC) remains a significant global health burden with a high mortality rate. Over the past 40 years, significant progress has been achieved in the prevention and management of HCC. Summary: Hepatitis B vaccination programs, the development of direct acting antiviral drugs for Hepatitis C, and effective surveillance strategies provide a profound basis for the prevention of HCC. Advanced surgery and liver transplantation along with local ablation techniques potentially offer cure for the disease. Also, just recently, the introduction of immunotherapy opened a new chapter in systemic treatment. Finally, the introduction of the BCLC classification system for HCC, clearly defining patient groups and assigning reasonable treatment options, has standardized treatment and become the basis of almost all clinical trials for HCC. With this review, we provide a comprehensive overview of the evolving landscape of HCC management and also touch on current challenges. Key Message: A comprehensive and multidisciplinary approach is crucial for effective HCC management. Continued research and clinical trials are imperative to further enhance treatment options and will ultimately reduce the global burden of this devastating disease.

Autoimmune liver diseases (AILDs) are rare and require precise evaluation, which is often challenging for medical providers. Chatbots are innovative solutions to assist healthcare professionals in clinical management. In our study, ten liver specialists systematically evaluated four chatbots to determine their utility as clinical decision support tools in the field of AILDs. We constructed a 56-question questionnaire focusing on AILD evaluation, diagnosis, and management of Autoimmune Hepatitis (AIH), Primary Biliary Cholangitis (PBC), and Primary Sclerosing Cholangitis (PSC). Four chatbots -ChatGPT 3.5, Claude, Microsoft Copilot, and Google Bard- were presented with the questions in their free tiers in December 2023. Responses underwent critical evaluation by ten liver specialists using a standardized 1 to 10 Likert scale. The analysis included mean scores, the number of highest-rated replies, and the identification of common shortcomings in chatbots performance. Among the assessed chatbots, specialists rated Claude highest with a mean score of 7.37 (SD = 1.91), followed by ChatGPT (7.17, SD = 1.89), Microsoft Copilot (6.63, SD = 2.10), and Google Bard (6.52, SD = 2.27). Claude also excelled with 27 best-rated replies, outperforming ChatGPT (20), while Microsoft Copilot and Google Bard lagged with only 6 and 9, respectively. Common deficiencies included listing details over specific advice, limited dosing options, inaccuracies for pregnant patients, insufficient recent data, over-reliance on CT and MRI imaging, and inadequate discussion regarding off-label use and fibrates in PBC treatment. Notably, internet access for Microsoft Copilot and Google Bard did not enhance precision compared to pre-trained models. Chatbots hold promise in AILD support, but our study underscores key areas for improvement. Refinement is needed in providing specific advice, accuracy, and focused up-to-date information. Addressing these shortcomings is essential for enhancing the utility of chatbots in AILD management, guiding future development, and ensuring their effectiveness as clinical decision-support tools.

Angiogenesis is the process of new blood vessels growing from existing vasculature. Visualizing them as a three-dimensional (3D) model is a challenging, yet relevant, task as it would be of great help to researchers, pathologists, and medical doctors. A branching analysis on the 3D model would further facilitate research and diagnostic purposes. In this paper, a pipeline of vision algorithms is elaborated to visualize and analyze blood vessels in 3D from formalin-fixed paraffin-embedded (FFPE) granulation tissue sections with two different staining methods. First, a U-net neural network is used to segment blood vessels from the tissues. Second, image registration is used to align the consecutive images. Coarse registration using an image-intensity optimization technique, followed by finetuning using a neural network based on Spatial Transformers, results in an excellent alignment of images. Lastly, the corresponding segmented masks depicting the blood vessels are aligned and interpolated using the results of the image registration, resulting in a visualized 3D model. Additionally, a skeletonization algorithm is used to analyze the branching characteristics of the 3D vascular model. In summary, computer vision and deep learning is used to reconstruct, visualize and analyze a 3D vascular model from a set of parallel tissue samples. Our technique opens innovative perspectives in the pathophysiological understanding of vascular morphogenesis under different pathophysiological conditions and its potential diagnostic role.

Cell nuclei interpretation is crucial in pathological diagnostics, especially in tumor specimens. A critical step in computational pathology is to detect and analyze individual nuclear properties using segmentation algorithms. Conventionally, a semantic segmentation network is used, where individual nuclear properties are derived after post-processing a segmentation mask. In this study, we focus on showing that an object-detection-based instance segmentation network, the Mask R-CNN, after integrating it with a Feature Pyramidal Network (FPN), gives mature and reliable results for nuclei detection without the need for additional post-processing. The results were analyzed using the Kumar dataset, a public dataset with over 20,000 nuclei annotations from various organs. The dice score of the baseline Mask R-CNN improved from 76% to 83% after integration with an FPN. This was comparable with the 82.6% dice score achieved by modern semantic-segmentation-based networks. Thus, evidence is provided that an end-to-end trainable detection-based instance segmentation algorithm with minimal post-processing steps can reliably be used for the detection and analysis of individual nuclear properties. This represents a relevant task for research and diagnosis in digital pathology, which can improve the automated analysis of histopathological images.

The prevention of liver disease has improved significantly in the last few decades, to the point that it can now be considered a true success story. The wide variety of interventions, including comprehensive vaccination strategies, novel medications, lifestyle changes, and even preventive surgeries, have reduced the morbidity and mortality of chronic liver diseases. However, the prevalence of chronic liver diseases is increasing worldwide. Currently, fatty liver disease alone is estimated to be present in as much as 30% of the adult population. Furthermore, there is a trend towards increasing incidences of chronic hepatitis B, and a global lack of success in efforts to eliminate chronic hepatitis C. Thus, improving and efficiently rolling out existing and successful prevention strategies for chronic liver diseases will play an essential role in healthcare throughout the upcoming decades. In this review, we summarize the current options and concepts for preventing chronic liver diseases, highlight their limitations, and provide an outlook on probable future developments to improve awareness, integrated care, and the analysis of big data.