Lymphoma

Deep learning in breast cancer histopathology: predicting and detecting axillary lymph node metastasis.

Deep learning (DL) methods are increasingly applied to digitized hematoxylin and eosin (H&E) slides in breast cancer to assist in staging and prognosis. One emerging application is the assessment of axillary lymph node (ALN) status, either by detecting metastases on lymph node slides or by predicting nodal involvement from primary tumor histology. This narrative review summarizes recent progress i...

Interpretable machine learning for individualized survival prediction in node-positive, non-metastatic prostate cancer: a population-based study.

BACKGROUND: Prostate cancer with regional lymph node involvement but no distant metastasis (N1M0) has heterogeneous prognosis. This study aimed to develop and validate an interpretable machine learning model for predicting cancer-specific survival (CSS). METHODS: Data from 18,287 N1M0 patients (2000-2022) in the SEER database were divided into training (n = 4780), internal testing (n = 1193), and ...

Beyond the ceiling: A critical analysis of microRNA research in cutaneous T-cell lymphoma and a roadmap forward.

MicroRNAs (miRNAs) have been studied in cutaneous T-cell lymphoma (CTCL) for more than 15 years, revealing oncogenic and tumor-suppressive networks, t...

Machine learning-empowered electrochemical nanobiosensors: towards intelligent diagnostics and data analysis.

Electrochemical nanobiosensors now produce not only scalar analytical readouts but also voltammetric waveforms, impedance spectra, transistor transfer...

Discovery of novel acridine based inhibitors of Bruton's tyrosine kinase (BTK) via pharmacophore modeling and machine learning-driven virtual screening followed by molecular dynamic studies.

Bruton's Tyrosine Kinase (BTK) has been recently recognized as an important drug design target for treating B-cell malignancies. Unfortunately, drug r...

Dual-view scout scans with deep learning for ultra-low dose attenuation correction in PET.

BACKGROUND: Accurate attenuation correction (AC) is essential for quantitative positron emission tomography (PET). Conventional CT-based AC provides r...

Automating cost-effectiveness models with agentic artificial intelligence: Case study and implications for value assessment.

BACKGROUND: Health economic modeling is conceptually sophisticated but operationally repetitive and resource intensive. Recent advances in large langu...

AI for prognostic assessment of diffuse large B-cell lymphoma using H&E whole-slide images.

Prediction of the outcome of large B-cell lymphomas/high-grade B-cell lymphomas (DLBCLs/HGBCLs) is based on clinical parameters and molecular testing,...

Artificial intelligence-based detection of small and intermediate-to-large lymphocytes for cytologic diagnosis of canine lymphoma.

Lymphoma is a common cancer in dogs, which often presents as generalized peripheral lymphadenopathy. Fine needle aspiration is frequently used to inve...

Machine learning for prediction of large-for-gestational-age neonate with adverse outcome at routine ultrasound examination at 35-37 weeks.

OBJECTIVES: To evaluate the performance of a machine-learning (ML) model compared with traditional logistic regression models for predicting a large-f...

Automated artificial intelligence performance for longitudinal pulmonary nodule matching in lung cancer screening.

Accurate longitudinal nodule matching is a critical technical prerequisite for automated growth rate (volume doubling time) assessment in lung cancer ...

Non-invasive detection and monitoring of oral squamous cell carcinoma and oral potentially malignant disorders: an umbrella review on diagnostic accuracy.

OBJECTIVES: Various non-invasive diagnostic techniques have been developed and assessed to improve early detection and monitoring of oral squamous cel...

Cutaneous lymphomas - an update.

Primary cutaneous lymphomas (CL) and lymphoproliferative disorders (LPD) are heterogeneous T- and B-cell neoplasms defined by integrated clinical, his...

AI-based segmentation of occult cervical lymph node metastases: potential reduction of cytokeratin staining.

BACKGROUND: Detection of occult cervical lymph node metastases is critical for accurate staging and treatment planning in oral cavity squamous cell ca...

Quality and reliability of YouTube videos on PET/CT radiation safety: a comparative analysis between human experts and large language models.

PURPOSE: To evaluate the quality and accuracy of YouTube videos regarding PET/CT radiation safety and to assess the feasibility of using a Large Langu...

μNeuFMT: Background-Optical-Property-Adaptive Fluorescence Molecular Tomography via Implicit Neural Representation.

Fluorescence Molecular Tomography is a promising technique for non-invasive 3D visualization of fluorescent probes, but its reconstruction remains cha...

A Scalable High-Density Microwell Assay for Single-Cell Clonal Expansion Profiling.

Traditional clonogenic assays remain central to evaluating the self-renewal capacity of tumor cells. However, the assay relies on subjective endpoint ...

From accuracy to service: deciding what artificial intelligence outputs may do in veterinary diagnostic laboratories.

Artificial intelligence (AI) tools are entering veterinary diagnostic laboratory service, but reported model accuracy does not determine what the labo...

Analytical and artificial neural network modeling of double-diffusive magnetized electroosmotic ellis nanofluid flow through a tapered non-uniform channel.

This study investigates the nonlinear transport dynamics of an Ellis nanofluid in a tapered asymmetric peristaltic channel embedded in a Darcy porous ...

A multi-criteria benchmarking framework for direct torque control strategies in induction motor drives.

This paper introduces a systematic framework for the performance evaluation of various direct torque control (DTC) techniques for induction motor driv...