Latest AI and machine learning research in oncology/hematology for healthcare professionals.
Castration-resistant prostate cancer emerges from coupling between cell-intrinsic heterogeneity and microenvironmental constraints. Mechanistically dissecting this coupling, rather than either factor in isolation, is the central aim of this study. To systematically study the effect of intrinsic and extrinsic spatial axes on disease trajectories, we developed an integrated multiscale framework: a c...
Background: Changes in the extracellular matrix (ECM) are a recognised feature of aggressive prostate cancer, but they are not exploited in clinical decision-making. We aimed to develop automated quantitative ECM parameters to facilitate risk stratification for localised prostate cancer. Methods: 378 quantitative ECM parameters were derived from picrosirius red-stained diagnostic prostate biopsies...
Minimally invasive endovascular procedures offer reduced surgical trauma, shorter recovery times, and improved outcomes, but rely on 2D fluoroscopic X...
Single-cell transcriptomics resolves CAR T-cell states, yet translating heterogeneous cellular signals into patient-level therapeutic response remains...
Generative models are increasingly used to model adaptive immune receptor repertoire (AIRR) sequence distributions, promising to decode the sequence d...
Rising costs, slow accrual and molecular substratification of cancers necessitate novel clinical trial designs. We demonstrate that artificial intelli...
Purpose: To compare the cross-site generalization of radiomic features and deep learning embeddings for MRI prediction of substantial lymphovascular s...
The therapeutic landscape for hepatocellular carcinoma (HCC) is evolving rapidly, necessitating scalable approaches to synthesize the expanding scient...
Recent advances in large language models (LLMs) and their extension to vision-language models (VLMs) have made it easier to combine text and images fo...
Most spatial transcriptomic analyses of solid tumors focus on individual cell states or single-sample spatial domains rather than on recurrent multice...
Positive margins in head and neck oncologic surgery require mapping specimen-side pathology findings to the patient resection bed. This is challenging...
Accurate determination of pancreatic ductal adenocarcinoma (PDAC) resectability relies on evaluating how the tumor interacts with major peripancreatic...
Brain tumor progression exhibits spatially heterogeneous growth, patient-specific treatment response, and complex interactions with surrounding anatom...
Longitudinal tumor measurements, dropout information, and genetic covariates provide complementary information about treatment response, but integrati...
Next-generation sequencing technologies, including RNA-sequencing, provide genome-wide measurements of gene expression and enable broad explorations o...
ThinPrep Cytologic Test (TCT) enables early cervical cancer screening, but manual reading is time-consuming and yields inconsistent diagnostic results...
Accurate dermatological diagnosis naturally necessitates equitable performance across diverse populations, yet a systematic lack of expertly annotated...
Diffusion generative models have demonstrated immense potential for synthetic medical image generation. However, these models often struggle to captur...
Accurate differentiation between gastric adenoma and carcinoma during endoscopy is critical for clinical decision-making. Yet, this task is highly cha...
Colorectal cancer (CRC) is the third most common cancer worldwide and the second leading cause of cancer-related deaths globally, with approximately 1...