Latest AI and machine learning research in oncology/hematology for healthcare professionals.
Despite their potential, patient-reported outcomes (PROs) are often underutilized in clinical decision-making, especially when improvements in PROs do not align with clinical outcomes. This misalignment may result from insufficient analytical methods that overlook the temporal dynamics and substantial variability of PROs data. To address these gaps, we developed a novel approach to investigate the...
Foundation models (FMs) show promise in medical AI by learning flexible features from large datasets, potentially surpassing handcrafted radiomics. Outcome prediction of head and neck squamous cell carcinomas (HNSCC) with FMs using routine imaging remains unexplored. To evaluate end-to-end FM-based multiple instance learning (MIL) for 2-year overall survival (OS), locoregional control (LRC), and f...
Evaluation of bone marrow morphology by experienced hematologists is key in the diagnosis of myeloid neoplasms, especially to detect subtle signs of d...
Deep-learning models for prostate cancer detection often require large datasets, which can be challenging to obtain and may lead to domain shift issue...
Given the high prevalence of vertebral fractures post-radiotherapy in patients with metastatic spine disease, accurate and rapid muscle segmentation c...
Lung cancer is the leading cause of cancer-related deaths. Diagnosis at late stages is common due to the largely non-specific nature of presenting sym...
The concomitant development and evolution of lung computed tomography (CT) and artificial intelligence (AI) has allowed non-invasive lung imaging to b...
The humoral immune system plays a significant role in the immune response to cancer but is challenging to study at scale. We used programmable phage i...
Digital pathology has significantly advanced cancer diagnosis by enabling high-resolution visualisation and assessment of tissue specimens. However, t...
Based on the Fukuoka and Kyoto international consensus guidelines, the current clinical management of intraductal papillary mucinous neoplasm (IPMN) l...
While mammography is commonly used for breast cancer detection, its widespread implementation in resource-constrained nations is challenging. Artifici...
Blood diseases and disorders, including leukemia and infectious diseases of red blood cells, pose significant diagnostic challenges due to their compl...
Recurrent/metastatic head and neck squamous cell carcinoma (R/M HNSCC) is an aggressive cancer with a median overall survival of only 12 months. Exist...
Chronic Myeloid Leukemia (CML) progresses through chronic, accelerated, and blast crisis phases, making disease stratification and therapeutic respons...
Epidermal Growth Factor Receptor (EGFR) mutations are critical biomarkers for targeted therapies in non-small cell lung cancer (NSCLC). However, conve...
Background and aims. The first direct-acting antivirals (DAAs) to treat the viral hepatitis C (HCV) became available in 2011. Despite numerous clinica...
Predicting outcomes in schizophrenia spectrum disorders is challenging due to the variability of individual trajectories. While machine learning (ML) ...
HER2 expression level is a key factor in determining the optimal treatment course for breast cancer patients. Roughly 15% of breast cancers are HER2(+...
The TNM staging system is the primary tool for treatment decisions in nasopharyngeal carcinoma (NPC). However, therapeutic outcomes vary considerably ...
Extracting and structuring relevant clinical information from electronic health records (EHRs) remains a challenge due to the heterogeneity of systems...