Oncology/Hematology

Machine learning based hepatic safety score predicts decompensation in hepatocellular carcinoma systemic therapy.

Hepatocellular carcinoma (HCC) frequently coexists with portal hypertension, significantly increasing the risk of hepatic decompensation (HD) and variceal bleeding during systemic therapy. We developed a machine learning based hepatic safety score (MHSS) using data from 2026 patients with unresectable HCC to predict clinically significant portal hypertension (CSPH) and prognosis. A random forest m...

Decoding the role of polyamine metabolism in lung adenocarcinoma prognosis: A triangulated approach combining transcriptome, single-cell and Mendelian randomization analyses.

The progression of lung adenocarcinoma (LUAD) is influenced by polyamine metabolism, which modulates antitumor immunity, although the underlying mechanisms remain unclear. The present study investigates the role of polyamine metabolism-related genes (PMRGs) in LUAD using transcriptomic data, single-cell RNA sequencing (scRNA-seq) and Mendelian randomization. Differentially expressed PMRGs were ide...

Radiomics analysis of restaging MRI for detection of pathological complete response in locally advanced rectal cancer.

PURPOSE: magnetic resonance imaging (MRI)-based radiomics has emerged as a promising approach for non-invasive prediction of treatment response in rec...

Lung Cancer Screening and Prevention: Beyond Conventional Strategies in a Rapidly Changing Global Milieu.

Lung cancer remains the leading cause of cancer-related mortality worldwide despite advances in early detection and treatment. Furthermore, its epidem...

Diabetic retinal disease cure accelerator: Modernizing staging and endpoints.

The "Diabetic Retinal Disease (DRD) Cure Accelerator," a joint initiative by the Mary Tyler Moore Vision Initiative and the Collaborative Community on...

Representation of Ordinal Features: Supervised Embeddings in the Survival Prediction of Prostate Cancer Patients.

Prostate cancer (PCa) is the most common cancer in men. Treatment decisions for PCa consider factors like age, tumor stage, and grade, along with spec...

Finding Associative and Causal Effects of Temporal Changes in Health Features for Prevalent and Incident Cancer in Males: A Machine Learning Approach.

Cancer remains a major public health challenge driven by complex interactions among sociodemographic, behavioral, clinical, and environmental factors....

Towards the Definition of a Prognostic Model for Mantle Cell Lymphoma.

Mantle cell lymphoma (MCL) has a heterogeneous clinical course, making robust, usable prognostic tools essential for risk-adapted care. In this paper,...

Building an Ontology-Based Cohort of Liver Cancer Imaging Data for AI Development on the European Federated Platform EUCAIM.

Hepatocellular carcinoma (HCC) is steadily increasing in incidence worldwide and requires data-driven approaches to improve diagnosis, prognosis, and ...

A Comparison of Different Models to Recommend Variables for Research Data Requests to German Cancer Registries.

Cancer registry data is an important resource in cancer research, e.g. to formulate hypotheses for new interventions, to identify recruitment potentia...

A Knowledge Graph to Represent and Predict Cancer Mechanistic Associations.

A range of underlying mechanistic relationships influences the incidence of cancer. Understanding these mechanisms can help develop personalized inter...

Multimodal Graph-Based Model for Discrete-Time Survival Prediction in Liver Cancer.

Liver cancer is a leading cause of cancer mortality; hepatocellular carcinoma (HCC), its predominant form, requires accurate survival prediction to gu...

Identification of Cervical Cancer Biomarkers Using Gene Co-Expression Networks and Machine Learning Methods.

Cervical cancer (CC) causes significant mortality due to late diagnosis and limited understanding of its molecular drivers. The complex gene co-expres...

Deep Learning-Based Prediction of Pathogenicity for ABL1 Protein Variants Using Sequence Representation.

The ABL1 gene encodes a non-receptor tyrosine kinase implicated in leukemia and other genetic disorders. This study presents a deep learning-based app...

Real-World Application of a Machine Learning Pipeline for Overall Survival in Chronic Lymphocytic Leukemia.

We developed a machine learning model and validated it using a large-scale, real-world cohort to predict overall survival in patients with Chronic Lym...

From Report to Record: Prompt-Based Information Extraction from Gynecology Oncology Reports Using LLMs.

The growing capabilities of Large Language Models (LLMs) in understanding and generating clinical text are transforming the processing of unstructured...

Classifying Clinical Evidence Levels of Cancer Variants in Biomedical Literature Using Machine Learning and Large Language Models.

Automating the classification of clinical evidence levels in biomedical literature can support precision oncology by facilitating the acceleration of ...

A Real-Time Clinical Text Information Extractor via LLM.

The extraction of structured information from unstructured clinical text is a critical requirement for real-time decision support and research applica...

Describing Data Processing in FHIR: AI-Assisted Interoperability for Cancer Stage Extraction.

INTRODUCTION: Creating interoperable clinical data models in FHIR is essential but often labor-intensive. This study explores the use of Generative AI...

Enhancing Explainable AI Stability with Realistic Synthetic Data for Cardiovascular Risk Prediction.

Data-driven such as machine learning models in rare disease research faces a significant challenge: scarce data cause performance degradation and unst...