Lymphoma

Deep learning-based malignancy probability estimation of pulmonary nodules in PET/CT imaging.

OBJECTIVE: The current BTS guidelines recommend evaluation of suspicious pulmonary nodules using [18F]FDG-PET/CT imaging, followed by Herder model risk stratification. However, it is based on limited imaging features, which may limit diagnostic accuracy. This study aims to develop a PET/CT-based deep learning (DL) model for malignancy probability estimation (AITO-PETCT-MP) and compare its performa...

A Multimodal Multitask Deep Learning Model Based on Ultrasound RF Signals for Joint Assessment of Breast Masses and Axillary Lymph Nodes Status.

PURPOSE: Developing a deep learning model to simultaneously evaluate lymph node status and distinguish between benign and malignant breast masses has been a challenging clinical task. This study aimed to use radio frequency (RF) signal data to create a deep learning, multimodal multitasking model, incorporating Class Activation Mapping (CAM) heatmaps to assist ultrasonographers in assessing the ov...

Metal-organic frameworks (MOFs) for nonlinear optical properties: design principles, DFT insights, and future directions for photonic applications.

Metal-organic frameworks (MOFs) have emerged as a uniquely versatile platform for nonlinear optical (NLO) applications, combining the large hyperpolar...

Development and validation of a novel multimodal deep neural network model based on CBC digit parameters and scattergrams for rapid hematolymphoid malignancy classification: a multicenter cohort study.

BACKGROUND: Rapid screening of hematolymphoid malignancies (HMs) by complete blood cell count (CBC) is crucial for choosing the next appropriate worku...

Multi-regional radiomics based on planning CT and information complementarity between GTVp and GTVn: an explainable model for predicting 5-year recurrence risk in HPV-positive oropharyngeal cancer.

PURPOSE: This study aims to develop and validate a multi-regional radiomics machine learning model integrating the spatial heterogeneity features of b...

Spiky Magnetic Titania Particles for Integrated Exosome Capture and Metabolic Profiling Toward Cancer Diagnosis.

Exosomal metabolite profiling represents a promising non-invasive approach for cancer diagnosis. However, its widespread application has been constrai...

Radiomics Models as Tools for Predicting Genetic Mutations in Colorectal Cancer: A Systematic Review and Meta-Analysis.

BACKGROUND: The evaluation of genetic mutations is crucial for personalized therapy in colorectal cancer (CRC), but the invasive tissue biopsy is subj...

Large language models enable prognostic stratification of cancer patients using real-world clinical notes.

In medical documentation, vast amounts of unstructured text are generated that are still underutilized in current prognostic models. We investigate th...

Automatic Sleep Staging Using Cardiorespiratory Signals: A Systematic Review of Methodologies and Performance.

Cardiorespiratory-based methods offer promising alternatives to traditional PSG for longitudinal sleep monitoring, holding significant systemic medica...

A Predictive Nomogram Integrating AI-Assisted Morphological Feature Extraction with Clinical and Ultrasound Parameters for Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer.

OBJECTIVE: To develop and validate a nomogram integrating artificial intelligence (AI)-extracted ultrasound features with clinic pathologic data for n...

Mantle cell lymphoma artificial intelligence prognostic index using hematoxylin and eosin histology.

The clinical course of Mantle Cell Lymphoma (MCL) varies between individual patients. Early detection of risk is crucial to assign MCL patients to nov...

Effects of 5D built environment and non-built-environment factors on injury crash risk: An interpretable machine learning analysis.

To identify the key determinants of traffic injury risk and clarify the relative roles of built environment factors and crash-context factors, this st...

Rhodamine-benzothiazole dyad: AIE-active probe with solid-state optoelectronic properties, selective sensing of 1,3-diaminopropane and hydrazine detection guided by DFT and machine learning.

The widespread release of organic amines from industrial waste and food spoilage poses a significant environmental and food safety concern. Herein, to...

The application of artificial intelligence in adrenal imaging: current state of knowledge, challenges, and future directions.

Adrenal incidentalomas are frequently detected on abdominal imaging and require evaluation for malignancy and hormonal activity. Although most are ben...

Novel Imaging Approaches for Visualising Root-Mycorrhizal Fungal Interactions.

Mycorrhizal fungi form essential symbiotic relationships with plant roots, facilitating nutrient exchange and promoting plant health. Understanding th...

Innovation districts and transformative workspaces: A scoping review of AI-pet robots companionship for aging employees balancing productivity and wellbeing.

BACKGROUND: Considering the future of work and an aging workforce, emerging technologies such as artificial intelligence (AI) and robots are promising...

Machine learning-guided discovery of poly(ethylene terephthalate)-binding modules to enhance durable whole-cell degradation.

The enzymatic degradation of poly(ethylene terephthalate) (PET) offers a sustainable route for plastic recycling but is often hindered by limited enzy...

Artificial intelligence-based automated sleep staging using heart rate variability: Assessment of performance and clinical prospects.

Background Some artificial intelligence models use heart rate variability (HRV) features to classify sleep stages. Estimation of HRV indices requires ...

APEX-NET: automated pancreatic evaluation network using early non-contrast CT.

OBJECTIVES: To develop and validate APEX-NET for early diagnosis and severity stratification of acute pancreatitis (AP) using non-contrast CT (NCCT), ...

Artificial intelligence (AI)-assisted diagnosis of skin diseases: From image classification to dermatology-specific multimodal clinical reasoning.

Artificial intelligence (AI) in dermatology has moved beyond the early paradigm of single-image classification. Dermatological diagnosis is achieved b...