Latest AI and machine learning research in oncology/hematology for healthcare professionals.
Despite continued advances in oncology, cancer remains a leading cause of global mortality, highlighting the need for diagnostic and prognostic tools that are both accurate and interpretable. Unimodal approaches often fail to capture the biological and clinical complexity of tumors. In this study, we present a suite of task-specific AI models that leverage CT imaging, multi-omics profiles, and str...
Early-onset colorectal cancer (EOCRC) is rising rapidly, especially among populations at risk who experience disproportionate incidence and mortality. The TP53 pathway, frequently altered in CRC, regulates key processes such as DNA repair and apoptosis. Despite its clinical relevance, TP53 dysregulation remains understudied in EOCRC, particularly in populations at risk. Current tools lack support ...
PSMA PET/CT imaging has been increasingly utilized in the management of patients with metastatic prostate cancer (mPCa). Imaging biomarkers derived fr...
Accurate integration of histological and molecular features is central to modern cancer diagnostics, but it is often hampered by extended turnaround t...
White matter (WM) tract detection is critical in presurgical planning of tumor resection however, standard-of-care imaging techniques including T1-wei...
This study reveals that pulmonary nodules exhibit distinct multifractal characteristics, with malignant nodules demonstrating significantly higher fra...
Volatile Organic Compounds (VOCs) are organic chemicals that readily vaporize at room temperature and are emitted from diverse sources, including pain...
Cancer is one of the leading lethal causes worldwide, with enormous impact on healthcare, economy and society. One of the main challenges of clinical ...
Tertiary lymphoid structures (TLSs) within the tumor microenvironment have emerged as potential indicators of treatment response to immune checkpoint ...
Rare haematological diseases (RHD) pose significant clinical challenges due to their heterogeneity, limited patient populations, and fragmented datase...
Effective risk communication is essential to shared decision-making in prostate cancer care. However, the quality of physician communication of key tr...
Major depressive disorder (MDD) is a leading cause of disability worldwide, yet treatment response to antidepressants remains highly variable, with a ...
Brain tumor classification using MRI scans is crucial for early diagnosis and treatment planning. In this study, we first train a single Convolutional...
Breast cancer originates from rare “cancer stem cells”. Stem cells are especially susceptible to becoming cancerous because they readily become differ...
There is a growing interest in performing automated, longitudinal tracking of sleep in the home environment using wearables and machine learning. Wear...
HONeYBEE (Harmonized ONcologY Biomedical Embedding Encoder) is an open-source framework that integrates multimodal biomedical data for oncology applic...
Prostate cancer remains one of the most prevalent malignancies and a leading cause of cancer-related deaths among men worldwide. Despite advances in t...
Widespread access to imaging technologies and stronger machine learning (ML) architectures for dermatology tasks such as malignancy prediction have sp...
The VISION study1 found that Lutetium-177 (177Lu)–PSMA-617 (“Lu-177”) improved overall survival in metastatic castrate resistant prostate cancer (mCRP...
Disparities of lung cancer incidence exist in Black populations and screening criteria underserve Black populations due to disparately elevated risk i...