Latest AI and machine learning research in oncology/hematology for healthcare professionals.
Breast cancer remains one of the leading causes of mortality among women worldwide, and early and accurate diagnosis is essential for effective treatment. In this study, we propose an ensemble deep learning approach for classifying histopathological images of breast cancer using the BreaKHis dataset. Two state-of-the-art convolutional neural network architectures, ResNet50 and DenseNet121, were fi...
Acute Myeloid Leukemia (AML) is a genetically and clinically heterogeneous disease that can develop at any age. While AML incidence increases with age and distinct genetic alterations are observed in younger versus older patients, current classification systems do not incorporate age as a defining factor. In this study, we analyzed RNA-seq data from 404 AML patients at initial diagnosis, leveragin...
To develop and validate an artificial intelligence (AI)-based model that automatically measures choroidal mass dimensions on Bâ–¡scan ophthalmic ultraso...
The objective of this study was to develop a flexible risk stratification strategy for Acute Myeloid Leukemia (AML) that is specific for venetoclax pl...
Early-onset colorectal cancer (EOCRC), defined as diagnosis before age 50, is rising rapidly and disproportionately affects high-risk populations, par...
In medical documentation, vast amounts of unstructured text are generated that are still underutilized in current prognostic models. We investigate th...
Each piece of cell-free DNA (cfDNA) has a length determined by the exact metabolic conditions in the cell it belonged to at the time of cell death. Th...
Breast cancer is the most frequently diagnosed malignancy among women worldwide and a major cause of mortality. Early and accurate detection is vital ...
Interest in the use of prediction models to support referrals to palliative care is surging. Few high-performing models have been developed, implement...
Head and neck cancer (HNC) patients face an increased risk of malnutrition due to lifestyle, tumor localization, and treatment effects. While skeletal...
The tumor–stroma ratio (TSR) is an established prognostic biomarker across several cancer types, yet its manual assessment remains labour-intensive an...
Artificial intelligence (AI) and statistical models designed to predict same-admission outcomes for hospitalized patients, such inpatient mortality, o...
To evaluate the performance of SROTAS IQ, a custom fine-tuned large language model (LLM), in automating clinical trial eligibility screening for breas...
Lung cancer remains the leading cause of cancer-related mortality in the United States, with screening adherence rates below 16% nationally and even l...
The early detection of skin cancer is of critical importance, as it can lead to in fatal outcomes if left unaddressed. Given the limited accessibility...
The inbuilt ability to adapt existing models to new applications has been one of the key drivers of the success of deep learning models. Thereby, shar...
Deep learning models applied to digital histology can predict gene expression signatures (GES) and offer a low-cost, rapidly available alternative to ...
Building on evidence linking urinary glyphosate to chronic liver disease (CLD) and hepatocellular carcinoma (HCC), we developed urinary pesticide prof...
A hallmark of glioblastoma (GBM) is disease recurrence, which occurs in all patients despite tumor resection, radiation, and chemotherapy. A critical ...
Accurate prognostic models are essential for guiding treatment decisions and improving patient outcomes in breast cancer. To achieve this, population-...