Lymphoma

Metaheuristic optimization of deep CNNs for multi-class diagnosis of cervical cancer and lymphoma.

Accurate and early diagnosis of cancer is critical for determining effective treatment strategies and improving patient survival rates. However, automated multi-class cancer detection remains an enormous clinical and computational challenge due to the high visual heterogeneity within specific cancer classes and the morphological similarities across different types of malignancies. Deep convolution...

Convolutional neural network analysis of cervical CT images: classification of Kikuchi disease, lymphoma, lymphadenitis, and tuberculosis.

BACKGROUND: Kikuchi disease, lymphoma, lymphadenitis, and tuberculosis are common diseases affecting the head and neck. The causes and treatment methods differ; however, the initial symptoms of these diseases (fever, pain, and neck swelling) are generally similar; therefore, it is important to accurately determine the type of disease at its initial stage. RESULTS: During the performance evaluation...

High scan-rescan repeatability of AI-enabled coronary plaque quantification from coronary CT angiography.

OBJECTIVES: To evaluate the reproducibility of plaque quantification from serial cardiac computed tomography (CCTA) scans by a systematic side-by-side...

Pregnancy prediction in Nelore heifers using machine learning algorithms.

Beef cattle production systems, particularly those based on Nelore heifers in tropical regions, are under increasing pressure to improve reproductive ...

Comparative classification of spectrally overlapping Allium seed genotypes using Vis-NIR spectroscopy and hyperspectral imaging with chemometric, machine, and deep learning models.

Accurate identification of Allium seed genotypes is essential for cultivar authentication, breeding, and fraud prevention, yet remains challenging due...

Crowdsourcing enables robust cell annotation for breast cancer pathology.

The tumor microenvironment (TME) contains important morphological and molecular cues that help determine prognosis and therapeutic responses. Deep lea...

Four Directions, One Solution: Enabling Rapid Diffusion Tensor MRI for Ultra-Low Field Using Deep Learning.

PURPOSE: This study revisits the tetrahedral encoding strategy originally proposed to accelerate Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) ...

Near-infrared imaging-based high-content analysis for label-free assessment of internal cellular heterogeneity in spheroid.

Non-invasive analysis of spheroid quality was essential because spheroids better recapitulated in vivo-like cell-cell and cell-extracellular matrix (E...

Precision theranostics in oncology: integrating antibody-drug conjugates, radioimmunotherapy, and immuno‑PET for adaptive cancer care.

The century-old vision of a "magic bullet" in oncology is being realized through the paradigm of precision theranostics, which formally integrates tar...

Flux sampling and graph neural networks for improved gene essentiality prediction in mammalian genome-scale metabolic models.

Gene essentiality, the requirement of a gene for survival or proliferation, is central to understanding cellular processes and identifying drug target...

The geographic digital divide among dementia caregivers in the U.S.: a cross-sectional study.

BACKGROUND: Digital health tools, particularly patient portals, can support caregiving, but there is limited understanding of how sociodemographic and...

Top five Alzheimer Disease trial eligibility criteria favor men compared to women in a clinic-based cohort.

BACKGROUND: Less women participate in Alzheimer Disease (AD) trials compared to their estimated representation in the global dementia population. OBJE...

Deep learning model for noninvasive prediction of Ki-67 expression and prognostic stratification in breast cancer: a multicenter retrospective study.

OBJECTIVE: Ki-67 correlates with prognosis for patients with breast cancer. However, the evaluation of Ki-67expression relies on pathological analysis...

Intelligent optimization of micro-electrical discharge machining of aluminum nitride ceramics using nano abrasive zirconium oxide-enhanced biodegradable dielectric.

This study explores the use of nano-enhanced Canola Oil Methyl Ester (COME) formulated with 0.5 wt% of zirconium oxide (ZrO2) nanoparticles as a biode...

Comparison of automated robotic arm-assisted PET/computed tomography-guided metabolic bone marrow biopsy with conventional bone marrow trephine biopsy for evaluating bone marrow infiltration in newly diagnosed lymphoma.

OBJECTIVES: Bone marrow involvement (BMI) upstages lymphoma and influences prognosis and treatment. Conventional bone marrow trephine biopsy (BMTB) ma...

Design and in silico evaluation of an mRNA vaccine against HTLV-1 using AI-driven reverse vaccinology approaches.

Human T-lymphotropic virus type 1 (HTLV-1) is the first discovered human oncogenic retrovirus that can cause adult T-cell leukemia/lymphoma, HTLV-1-as...

Non-small Cell Lung Cancer in Metastatic Renal Cell Carcinoma Patients.

INTRODUCTION: Renal cell carcinoma (RCC) most commonly metastasizes to the lungs and shares risk factors with lung cancer. However, primary lung cance...

CellFuse enables Multi-modal Integration of Single-cell and Spatial Proteomics Data for Systems-level Analysis in Cancer.

Analysis of tumors using single-cell and spatial modalities is critical to advance our understanding of cancer. The growth of technologies that enable...

Aerial LiDAR-Based, Source-Resolved Methane Emissions Inventory: Permian Basin Case Study for Benchmarking U.S. Emissions.

Reducing methane emissions can slow near-term warming, yet building accurate inventories to inform mitigation efforts and track progress toward reduct...

Metabolic neural networks of the marmoset brain in awake and isoflurane-anesthetized resting states.

The common marmoset is a key model in translational neuroscience, yet the reliance on anesthesia for neuroimaging introduces significant confounds. To...