Lymphoma

MRI-based habitat radiomics for assessing synchronous metastatic risk in renal cell carcinoma: a multicenter study.

PURPOSE: To explore the role of MRI-based habitat radiomics in assessing the metastatic status of renal cell carcinoma (RCC). METHODS: This study retrospectively collected 241 patients with RCC who underwent nephrectomy and lymphadenectomy at four centers. MRI data from the first center were split into a training set (n = 150) and an internal test set (n = 38); data from the other centers (n = 53)...

Lesion-level dual-tracer PET biomarkers predict prognosis in multiple myeloma treated with CXCR4-directed radiopharmaceutical therapy.

BACKGROUND: C-X-C motif chemokine receptor 4 (CXCR4)-directed radiopharmaceutical therapy (RPT) represents a promising option for hematologic malignancies. Nevertheless, responses in relapsed and refractory (r/r) multiple myeloma (MM) are heterogenous, emphasizing the need for optimized patient selection before RPT. Current approaches mostly rely on qualitative assessments, confirming relevant CXC...

Coupling Remote Sensing and Artificial Intelligence: Mapping Hydrological Variables with GEE and Predicting Surface-water Extent with Neural Networks.

Managing water resources in water-scarce regions requires rigorous monitoring and reliable forecasting. To address this, we developed RSAI (Remote-Sen...

Recycled Thermocol Nanofibers Based Smart Triboelectric Nanogenerators for AI-Assisted Switching.

The accumulation of non-biodegradable thermocol (expanded polystyrene, EPS) waste presents a critical environmental challenge and an untapped resource...

Smart irrigation system and early plant disease detection using IoT and novel non-linear growing self-organizing map based artificial neural network.

The safety of the global food supply depends heavily on effective crop management, making early diagnosis of plant diseases vital for improving agricu...

Modeling the empathy-self-discovery paradox in Gen Z social behavior with NPD using artificial neural networks.

This study investigates the resurgence of the Myers-Briggs Type Indicator (MBTI) among Generation Z, theorizing its popularity as a social code that b...

Application of machine learning approaches to develop predictive models for diabetes and hypertension among Bangladeshi Adults.

With rapid urbanization, lifestyle changes, and an aging population, non-communicable diseases (NCDs), including hypertension and diabetes, pose signi...

Deep learning in lower gastrointestinal cancer detection: Advances in endoscopic, radiologic, and histopathologic diagnostics.

Gastrointestinal (GI) cancers, particularly colorectal cancer, continue to be a major contributor to global cancer-related morbidity and mortality. De...

Multimodal hybrid mamba classification model for tumor pathological grade prediction using magnetic resonance images.

Malignant tumors present a significant global health challenge, and accurate pathological grading is essential for personalized treatment. Traditional...

Minor salivary gland biopsy in Sjogren's disease: a diagnostic and prognostic tool toward precision medicine.

Minor salivary gland biopsy occupies a distinctive position in the evaluation of Sjögren disease (SjD), offering diagnostic and prognostic insights th...

Multi-view deep learning for automated lymphoma staging from 18F-FDG PET/CT: physician-level accuracy with high-throughput workflow.

Temporal influence maximization via continuous-time graph neural networks and deep reinforcement learning.

Influence maximization (IM) seeks to identify a subset of key nodes that maximize the spread of information or behavior through a network. While tradi...

Development of retrieval-augmented generation-based large language model for drug-induced liver injury using Livertox data.

BACKGROUND: Idiosyncratic DILI is a complex clinical challenge requiring timely and accurate decision support. LiverTox, curated by the National Insti...

Multi-modal AI for opportunistic screening, staging and progression risk stratification of steatotic liver disease.

The global rise in steatotic liver disease poses a significant public health challenge. While non-contrast computed tomography scans hold promise for ...

Machine learning-driven multi-objective optimization of Dunaliella salina cultivation for enhanced biomass and β-carotene production.

This study presented an interpretable multi-objective machine learning (ML) framework to navigate the trade-off between biomass accumulation and β-car...

Development of a hybrid deep learning-based framework for liver fibrosis classification using ultrasound images.

BACKGROUND AND AIMS: Liver fibrosis is a progressive accumulation of extracellular matrix proteins with distortion of hepatic architecture and can pro...

Spectral CT imaging in colorectal cancer: current applications, limitations, and future perspectives.

Colorectal cancer (CRC) is the third most common malignancy worldwide, and early detection is vital to prevent metastasis and postoperative recurrence...

Early Prediction of Adverse Stroke Outcomes using Non-clinical Factors and Missing Data: A Machine Learning Study.

Introduction Early prediction of stroke outcomes using prognostic tools may help clinical decision making and inform resource allocation. However, cli...

Profiling reasons for non-attendance in psychological assessments in adolescent suicide at-risk group using natural language processing.

BACKGROUND: Non-attendance at follow-up psychological evaluations is a major barrier to care for adolescents at risk of suicide. Identifying the facto...

Multi-omics profiling of chronic immune-mediated skin diseases: SKINERGY protocol and strategic evaluation.

BACKGROUND: The Dutch flagship project Next Generation ImmunoDermatology (NGID) aims to profile five chronic immune-mediated inflammatory skin disease...