Lymphoma

Artificial Intelligence Impact on Radiologist Performance in Breast Cancer Screening with Digital Breast Tomosynthesis.

PURPOSE: Assess impact of artificial intelligence (AI) on radiologists' detection of cancer on digital breast tomosynthesis (DBT) exams based on density, size, stage, and histopathology. METHODS AND MATERIALS: Retrospective analysis of mammography audit data and screening cancers was conducted at four sites during two time periods with nine dedicated breast radiologists. Data was collected from Ma...

Post-treatment infection prediction in CLL using domain adaptation of lymphoma electronic health records.

BACKGROUND AND PURPOSE: Infections are the leading cause of morbidity and mortality in patients with chronic lymphocytic leukemia (CLL) and occur during and after treatment. When deciding on the type of CLL treatment, the risk of infections is typically assessed based only on age and comorbidities; therefore, there is a need to develop a predictive model that incorporates information from multiple...

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 retr...

Evaluation of radiomics-based machine learning models for detection of cervical lymph node metastasis from oral cancer on PET images.

PURPOSE: In this study, we hypothesized that radiomics analysis applied to pre-treatment PET images would be more accurate in the detection of lymph n...

Discovery of potent ALK tyrosine kinase inhibitors for thyroid cancer via machine learning modeling, molecular docking, MD simulations, and DFT study.

The ever-increasing need for effective therapeutic management of thyroid cancer (TC) necessitates the exploration of novel approaches for advanced dru...

Integrating machine learning and molecular simulations for the design of potent HDAC2 inhibitors in diffuse large B-cell lymphoma.

Histone deacetylase 2 (HDAC2) plays a critical role in the pathogenesis of diffuse large B-cell lymphoma (DLBCL), positioning it as an attractive ther...

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 malignan...

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.

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...