Latest AI and machine learning research in oncology/hematology for healthcare professionals.
BACKGROUND: Artificial intelligence (AI) is expected to introduce an increasing number of biomarkers in oncology. To bridge the gap between oncology and computer science, it is timely to define recommendations for AI-based biomarkers suitable for routine clinical use. Here, we propose the ESMO (European Society for Medical Oncology) Basic Requirements for AI-based Biomarkers In Oncology (EBAI). DE...
The innovations in classifying breast cancer into malignant and benign categories and further categorizing it into molecular subtypes have reshaped healthcare services, enabling accurate diagnosis of these complex conditions. Identification of molecular subtypes of breast cancer is one of the most important treatment challenges, as these subtypes can have an enormous effect on the prognosis and tr...
PURPOSE: Pediatric adrenocortical tumors (pACTs) are rare and clinically heterogeneous. Existing risk stratification systems rely on fixed thresholds ...
OBJECTIVES: Early detection of colorectal cancer is critical for improving prognosis. However, assessing invasion depth-distinguishing between superfi...
Activation functions and their variable gradient action are pivotal in bridging artificial neural networks with the dynamic behavior of biological neu...
Advanced-stage lung squamous-cell carcinoma (LUSC) remains a therapeutic challenge. Although immune checkpoint inhibitors (ICIs) have revolutionized L...
The pathological grading of cervical squamous cell carcinoma (CSCC) is a fundamental and important index in tumor diagnosis. Pathologists tend to focu...
Pancreatic cancer remains one of the deadliest malignancies, primarily because of its subtle CT appearance and frequent late-stage diagnosis. We intro...
OBJECTIVE: Automated segmentation models for volumetric measurement of vestibular schwannoma (VS) have been developed for sporadic VS but not for bila...
BACKGROUND: Manual interpretation of brain tumor regions in MRI scans demands substantial medical expertise, is time-consuming, and is prone to human ...
Recent advances in spatial transcriptomics (ST) have significantly enhanced our understanding of tissue structure and intercellular interactions. Howe...
Acoustic angiography is a superharmonic contrast-enhanced ultrasound modality that maps 3-D microvasculature with fine spatial resolutions and has dem...
Lung adenocarcinoma (LUAD) is one of the most prevalent forms of cancer and continues to be associated with high mortality rates, despite recent advan...
PURPOSE: To develop an integrated predictive model combining radiomics, clinical risk factors, and machine learning for prognostic assessment in hepat...
Nuclear medicine has witnessed revolutionary progress, spurred by advances in radiopharmaceuticals, computational modeling, and artificial intelligenc...
PURPOSE: To develop and validate a 2.5D multi-angle deep learning (MADL) model for preoperative T-staging in patients with gastric cancer (GC) and to ...
OBJECTIVE: Pneumoconiosis is a common and highly hazardous occupational disease. The staging of pneumoconiosis is mainly carried out by experienced do...
To develop a noninvasive diagnostic model integrating deep learning and radiomics for improving the accuracy and clinical utility of early melanoma di...
Nasopharyngeal carcinoma (NPC) is a malignant tumor originating from the mucosal epithelium of the nasopharynx, which has a high incidence in southern...
Bone metastasis, a frequent complication of advanced cancers, requires early, precise detection to enable timely interventions and improve patient out...