Latest AI and machine learning research in oncology/hematology for healthcare professionals.
Artificial intelligence (AI)-based mobile health (mHealth) smartphone apps for skin cancer detection are increasingly available to the general population, but their impact on care is unclear. The SPOT study is an investigator-initiated and -designed, unblinded, randomized controlled trial. Participants from a Dutch non-profit health insurance living in and around region Rotterdam the Netherlands, ...
Accurate molecular profiling and prognostication from routine histopathology slides could transform precision oncology. We developed a Vision Transformer (ViT)-based multi-instance learning (MIL) framework for combined predictions of 32 solid tumour types, TP53 biomarker detection, and survival prediction directly from Whole Slide Images (WSIs). 11,060 primary tumours were curated from the TCGA Pa...
Microsatellite instability (MSI) is a key biomarker in colorectal cancer (CRC). Accurate distinction between MSI and microsatellite stable (MSS) tumor...
Simulation models inform health policy decisions by integrating data from multiple sources and forecasting outcomes when there is a lack of comprehens...
To leverage sleep foundation models trained on large datasets of polysomnography for neurological disorder detection during an awake state. Three publ...
Precise glioma segmentation in MRI is essential for accurate diagnosis, optimal treatment planning, and advancing clinical research. However, most dee...
Lymphoma diagnosis remains challenging due to diverse subtypes and nonspecific presentations. While prior research focused primarily on clinical accur...
Clinically manifested pneumonia associated with COVID-19 infection in cancer patients has been associated with worse prognosis. The prognostic signifi...
Large language models can help with clinical decision-making tasks. Complex oncology cases are best managed through multidisciplinary tumor boards but...
Immune checkpoint inhibitors have become standard care across many cancers, but most patients do not respond. Predicting response remains challenging ...
The administration of gadolinium-based contrast agents (GBCAs) for acquiring contrast-enhanced T1-weighted magnetic resonance imaging (T1C MRI) is ass...
Oral squamous cell carcinoma (OSCC) accounts for a major part of cancer mortality, with survival outcomes highly dependent on early diagnosis. While m...
Recent large-scale plasma proteomic studies have identified a set of biomarkers for the diagnosis of early cancer onset, but the predictive performanc...
Glioblastoma is a highly malignant brain tumor in which maximal safe resection is associated with improved survival, yet the oncological benefit of re...
Precision oncology has informed cancer care by enabling the discovery and application of diagnostic, prognostic, and/or predictive molecular biomarker...
To assess whether an artificial intelligence (AI) chest radiograph (CXR) tool could enhance lung cancer detection on primary care–referred CXRs in the...
Homologous recombination deficiency (HRD) confers sensitivity to poly (ADP-ribose) polymerase (PARP) inhibitors and platinum-based chemotherapy, repre...
Intratumour heterogeneity (ITH) drives the clinical trajectory of HCC, yet routine pathology relies on global classifications that often mask local ar...
The growing volume of biomedical literature, especially in oncology, necessitates automated tools for extracting clinically relevant information. Larg...
Manual data extraction from clinical text is resource intensive. Locally hosted large language models (LLMs) may offer a privacy-preserving solution, ...