This paper proposes a novel multimodal deep learning framework integrating bidirectional LSTM, multi-head attention mechanism, and variational mode decomposition (BiLSTM-AM-VMD) for early liver cancer diagnosis. Using heterogeneous data that include clinical characteristics, biochemical markers, and imaging-derived variables, our approach improves both prediction accuracy and interpretability. Experimental results on real-world datasets demonstrate superior performance over traditional machine learning and baseline deep learning models.

--Magnetic resonance imaging (MRI) is a leading modality for the diagnosis of liver cancer, significantly improving the classification of the lesion and patient outcomes. However, traditional MRI faces challenges including risks from contrast agent (CA) administration, time-consuming manual assessment, and limited annotated datasets. T o address these limitations, we propose a Time-Conditioned Autoregressive Contrast Enhancement (T -CACE) framework for synthesizing multi-phase contrast-enhanced MRI (CEMRI) directly from non-contrast MRI (NCMRI). T -CACE introduces three core innovations: a conditional token encoding (CTE) mechanism that unifies anatomical priors and temporal phase information into latent representations; and a dynamic time-aware attention mask (DT AM) that adaptively modulates inter-phase information flow using a Gaussian-decayed attention mechanism, ensuring smooth and physiologically plausible transitions across phases. Extensive experiments on two independent liver MRI datasets demonstrate that T -CACE outperforms state-of-the-art methods in image synthesis, segmentation, and lesion classification. This framework offers a clinically relevant and efficient alternative to traditional contrast-enhanced imaging, improving safety, diagnostic efficiency, and reliability for the assessment of liver lesion. The implementation of T -CACE is publicly available at: https://github.com/xiaojiao929/T IVER cancer remains one of the leading causes of cancer-related mortality worldwide, posing a substantial public health burden [1]. Contrast-enhanced magnetic resonance imaging (CEMRI) plays a pivotal role in the diagnosis of liver disease by providing high-resolution soft tissue contrast and enabling accurate differentiation between benign and malignant lesions [2].

Liver cancer is a leading cause of cancer-related mortality worldwide, with its high genetic heterogeneity complicating diagnosis and treatment. This study introduces DLSOM, a deep learning framework utilizing stacked autoencoders to analyze the complete somatic mutation landscape of 1,139 liver cancer samples, covering 20,356 protein-coding genes. By transforming high-dimensional mutation data into three low-dimensional features, DLSOM enables robust clustering and identifies five distinct liver cancer subtypes with unique mutational, functional, and biological profiles. Subtypes SC1 and SC2 exhibit higher mutational loads, while SC3 has the lowest, reflecting mutational heterogeneity. Novel and COSMIC-associated mutational signatures reveal subtype-specific molecular mechanisms, including links to hypermutation and chemotherapy resistance. Functional analyses further highlight the biological relevance of each subtype. This comprehensive framework advances precision medicine in liver cancer by enabling the development of subtype-specific diagnostics, biomarkers, and therapies, showcasing the potential of deep learning in addressing cancer complexity.

Background: Liver cancer ranks as the fifth most common malignant tumor and the second most fatal in our country. Early diagnosis is crucial, necessitating that physicians identify liver cancer in patients at the earliest possible stage. However, the diagnostic process is complex and demanding. Physicians must analyze a broad spectrum of patient data, encompassing physical condition, symptoms, medical history, and results from various examinations and tests, recorded in both structured and unstructured medical formats. This results in a significant workload for healthcare professionals. In response, integrating knowledge graph technology to develop a liver cancer knowledge graph-assisted diagnosis and treatment system aligns with national efforts toward smart healthcare. Such a system promises to mitigate the challenges faced by physicians in diagnosing and treating liver cancer. Methods: This paper addresses the major challenges in building a knowledge graph for hepatocellular carcinoma diagnosis, such as the discrepancy between public data sources and real electronic medical records, the effective integration of which remains a key issue. The knowledge graph construction process consists of six steps: conceptual layer design, data preprocessing, entity identification, entity normalization, knowledge fusion, and graph visualization. A novel Dynamic Entity Replacement and Masking Strategy (DERM) for named entity recognition is proposed. Results: A knowledge graph for liver cancer was established, including 7 entity types such as disease, symptom, and constitution, containing 1495 entities. The recognition accuracy of the model was 93.23%, the recall was 94.69%, and the F1 score was 93.96%.

The diagnosis of primary liver cancers (PLCs) can be challenging, especially on biopsies and for combined hepatocellular-cholangiocarcinoma (cHCC-CCA). We automatically classified PLCs on routine-stained biopsies using a weakly supervised learning method. Weak tumour/non-tumour annotations served as labels for training a Resnet18 neural network, and the network's last convolutional layer was used to extract new tumour tile features. Without knowledge of the precise labels of the malignancies, we then applied an unsupervised clustering algorithm. Our model identified specific features of hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (iCCA). Despite no specific features of cHCC-CCA being recognized, the identification of HCC and iCCA tiles within a slide could facilitate the diagnosis of primary liver cancers, particularly cHCC-CCA. Method and results: 166 PLC biopsies were divided into training, internal and external validation sets: 90, 29 and 47 samples. Two liver pathologists reviewed each whole-slide hematein eosin saffron (HES)-stained image (WSI). After annotating the tumour/non-tumour areas, 256x256 pixel tiles were extracted from the WSIs and used to train a ResNet18. The network was used to extract new tile features. An unsupervised clustering algorithm was then applied to the new tile features. In a two-cluster model, Clusters 0 and 1 contained mainly HCC and iCCA histological features. The diagnostic agreement between the pathological diagnosis and the model predictions in the internal and external validation sets was 100% (11/11) and 96% (25/26) for HCC and 78% (7/9) and 87% (13/15) for iCCA, respectively. For cHCC-CCA, we observed a highly variable proportion of tiles from each cluster (Cluster 0: 5-97%; Cluster 1: 2-94%).

This paper presents a new approach for classifying 2D histopathology patches using few-shot learning. The method is designed to tackle a significant challenge in histopathology, which is the limited availability of labeled data. By applying a sliding window technique to histopathology slides, we illustrate the practical benefits of transductive learning (i.e., making joint predictions on patches) to achieve consistent and accurate classification. Our approach involves an optimization-based strategy that actively penalizes the prediction of a large number of distinct classes within each window. We conducted experiments on histopathological data to classify tissue classes in digital slides of liver cancer, specifically hepatocellular carcinoma. The initial results show the effectiveness of our method and its potential to enhance the process of automated cancer diagnosis and treatment, all while reducing the time and effort required for expert annotation.

Tissue biomarkers are crucial for cancer diagnosis, prognosis assessment and treatment planning. However, there are few known biomarkers that are robust enough to show true analytical and clinical value. Deep learning (DL)-based computational pathology can be used as a strategy to predict survival, but the limited interpretability and generalizability prevent acceptance in clinical practice. Here we present an interpretable human-centric DL-guided framework called PathFinder (Pathological-biomarker-finder) that can help pathologists to discover new tissue biomarkers from well-performing DL models. By combining sparse multi-class tissue spatial distribution information of whole slide images with attribution methods, PathFinder can achieve localization, characterization and verification of potential biomarkers, while guaranteeing state-of-the-art prognostic performance. Using PathFinder, we discovered that spatial distribution of necrosis in liver cancer, a long-neglected factor, has a strong relationship with patient prognosis. We therefore proposed two clinically independent indicators, including necrosis area fraction and tumour necrosis distribution, for practical prognosis, and verified their potential in clinical prognosis according to criteria derived from the Reporting Recommendations for Tumor Marker Prognostic Studies. Our work demonstrates a successful example of introducing DL into clinical practice in a knowledge discovery way, and the approach may be adopted in identifying biomarkers in various cancer types and modalities. The potential of deep learning in pathological prognosis has been hampered by limited interpretability in clinical applications. Liang and colleagues present a human-centric deep learning framework that supports the discovery of prognostic biomarkers in an interpretable way.

A new study by Cedars-Sinai investigators describes how ChatGPT, an artificial intelligence (AI) chatbot, may help improve health outcomes for patients with cirrhosis and liver cancer by providing easy-to-understand information about basic knowledge, lifestyle and treatments for these conditions. The findings, published in the peer-reviewed journal Clinical and Molecular Hepatology, highlights the AI system's potential to play a role in clinical practice. "Patients with cirrhosis and/or liver cancer and their caregivers often have unmet needs and insufficient knowledge about managing and preventing complications of their disease," said Brennan Spiegel, MD, MSHS, director of Health Services Research at Cedars-Sinai and co-corresponding author of the study. "We found ChatGPT--while it has limitations--can help empower patients and improve health literacy for different populations." Patients diagnosed with liver cancer and cirrhosis, an end-stage liver disease that is also a major risk factor for the most common form of liver cancer, often require extensive treatment that can be complex and challenging to manage.

New research uses AlphaFold, an artificial intelligence (AI)-powered protein structure database, to accelerate the design and synthesis of a drug to treat hepatocellular carcinoma (HCC), the most common type of primary liver cancer. It is the first successful application of AlphaFold to hit identification process in drug discovery. This study by an international team of researchers, published last week in Chemical Science, is led by the University of Toronto's Acceleration Consortium director Alán Aspuru-Guzik, Chemistry Nobel laureate Michael Levitt, and Insilico Medicine founder and CEO Alex Zhavoronkov. AI is revolutionizing drug discovery and development. In 2022, the AlphaFold computer program, developed by Alphabet's DeepMind, predicted protein structures for the whole human genome––a remarkable breakthrough in both AI applications and structural biology.

A novel artificial intelligence blood testing technology developed and used by Johns Hopkins Kimmel Cancer Center researchers to successfully detect lung cancer in a 2021 study has now detected more than 80% of liver cancers in a new study of 724 people. The blood test, called DELFI (DNA evaluation of fragments for early interception) detects fragmentation changes among DNA from cancer cells shed into the bloodstream, known as cell-free DNA (cfDNA). In the most recent study, investigators used the DELFI technology on blood plasma samples obtained from 724 individuals in the U.S., the European Union (E.U.) and Hong Kong to detect hepatocellular cancer (HCC), a type of liver cancer. The researchers believe this is the first genome-wide fragmentation analysis independently validated in two high-risk populations and across different racial and ethnic groups with different causes associated with their liver cancers. Their findings were reported Nov. 18 in Cancer Discovery and at the American Association for Cancer Research Special Conference: Precision Prevention, Early Detection, and Interception of Cancer.