Latest AI and machine learning research in oncology/hematology for healthcare professionals.
The health consequences of ionizing radiation have long been studied, yet significant uncertainties remain, particularly at low doses. In particular, traditional dose-response models such as linear, linear-quadratic, threshold, or hormesis models, all impose specific assumptions about low-dose effects. In addition, while the goal of radiation epidemiological studies is ideally to uncover causal re...
Despite the widespread use of mammography as the standard of care for breast cancer screening, its accuracy remains limited for select patient populations, such as women with high breast density. Liquid biopsy-based tests offer an accessible complement to conventional screening methods. Here, we conducted a case-control study to develop a plasma-based protein classifier to distinguish between earl...
Atherosclerosis (AS) is a major cause for cardiovascular disease, and mitochondrial permeability transition driven necrosis (MPTDN) is often associate...
Brain metastases represent one of the most common intracranial malignancies, yet early and accurate detection remains challenging, particularly in cli...
Current radiotherapy (RT) planning workflows rely on pre-treatment simulation CT (sCT), which can significantly delay treatment initiation, particular...
The success of drug development relies heavily on the use of animal models. However, increasing evidence shows that discoveries in these models often ...
This study aims to enhance breast cancer diagnosis by developing an automated deep learning framework for real-time, quantitative ultrasound imaging. ...
Microsatellite instability (MSI) is a key biomarker for immunotherapy response and prognosis across multiple cancers, yet its identification from rout...
This study evaluated the prognostic performance of RlapsRisk BC, a multimodal deep learning tool designed to predict distant recurrence-free interval ...
Goals-of-care (GOC) discussions and their documentation are important process measures in palliative care. However, existing natural language processi...
Acute lymphoblastic leukemia is a highly heterogeneous hematologic malignancy that poses significant challenges for clinicians in terms of early detec...
The marked heterogeneity of cancer poses a substantial challenge to precision drug therapy, resulting in considerable variability in patient responses...
Accurate disease prognosis is essential for patient care but is often hindered by the lack of long-term data. This study explores deep learning traini...
Prostate cancer diagnosis and treatment planning depend on accurate histopathological assessment of needle biopsies, particularly through the Gleason ...
Cancer staging plays a critical role in treatment planning and prognosis but is often embedded in unstructured clinical narratives. To automate the ex...
Skin cancer is one of the most common types of cancer worldwide, and early detection is crucial for improving patient survival rates. In this study, w...
The ability of pre-trained large language models (LLMs) to rapidly master novel natural language processing tasks holds transformative potential. Howe...
We overcome current limitations in Acute Myeloid Leukemia (AML) diagnosis by leveraging a transfer learning approach from Acute Lymphoblastic Leukemia...
Cancer cachexia, a multifactorial metabolic syndrome characterized by severe muscle wasting and weight loss, contributes to poor outcomes across vario...
Malaysia faces a significant burden of breast cancer, compounded by a chronic shortage of pathologists. This leads to prolonged diagnostic turnaround ...