Lymphoma

Exploring Non-Embodied AI-Based Digital Companions for Older Adults in Aging and Care Contexts: Protocol for a Scoping Review.

BACKGROUND: Conversational artificial intelligence (AI) technologies are increasingly positioned as a response to social isolation, loneliness, and unmet psychosocial needs across health and care contexts. Non-embodied AI-based digital companions have attracted growing attention for their potential to support companionship, social interaction, communication, and psychosocial well-being among older...

CeraMIRScan: Mid-infrared OCT Scan Dataset for Ceramic Quality Assessment.

Mid-infrared Optical Coherence Tomography (MIR-OCT) is a promising Non-Destructive Testing (NDT) technique due to its high-resolution imaging capabilities and extensive applicability across various industrial domains. Studies developing Deep Learning (DL) models to detect defects in MIR-OCT scans are scarce, and few have been used for ceramic quality assessment. To address this gap, we introduce t...

Multimodal PET/CT-based PD-L1 status prediction in lung cancer via semi-supervised and unsupervised deep learning.

Deep learning (DL) techniques have been applied in lung cancer screening, assessing drug effectiveness, and enhancing prognosis prediction. Within thi...

Efficient training of machine learning models using non-converged iterates in nuclear reactor simulations.

Machine learning (ML) algorithms exhibit promising potential for enhancing safety, improving predictive accuracy, and streamlining nuclear reactor sys...

Using machine learning algorithms based on laboratory indicators to establish a diagnostic model for lung cancer.

BACKGROUND: Lung cancer remains one of the leading causes of cancer-related mortality worldwide. Current diagnostic strategies rely primarily on imagi...

Scalable nonlinear Cox modeling via random Fourier features with analytic uncertainty.

BACKGROUND: The Cox proportional hazards model often fails to capture complex biomedical risk structures, such as U-shaped biomarker associations, due...

A highly interpretable machine learning model for predicting lung cancer bone metastasis: uncovering the synergistic effect of routine biochemical markers.

BACKGROUND: Bone metastasis (BM) significantly impairs lung cancer prognosis and patient quality of life. Conventional imaging modalities often face l...

Kernel PCA for out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation.

Out-of-Distribution (OoD) detection is vital for the reliability of deep neural networks, the key of which lies in effectively characterizing the disp...

An intelligent MRI-based all-in-one diagnostic strategy for axillary lymph node status in breast cancer.

An integrated diagnostic strategy of preoperative identification of sentinel lymph node (SLN) metastasis, SLN metastatic burden, and non-SLN (NSLN) me...

Path beyond the blind end-unravel the imaging spectrum of appendiceal pathologies.

The appendix is involved in a diverse spectrum of inflammatory, infectious, benign, and malignant conditions that extend far beyond acute appendicitis...

Digital image analysis coupled with artificial neural networks for simultaneous prediction of biomass and pigments in microalgal cultivation.

Real-time, non-destructive monitoring of multiple physiological parameters in microalgal cultures remains a significant analytical challenge, as conve...

Assessing the performance of deep learning and hand-crafted radiomics models using MRI and ultrasound in predicting axillary lymph node status in breast cancer: a systematic review and meta-analysis.

BACKGROUND: Axillary lymph node metastasis (ALNM) is a critical prognostic factor in breast cancer. While sentinel lymph node biopsy remains the gold ...

Benchmarking deep learning models for laryngeal cancer staging using the LaryngealCT dataset.

Laryngeal cancer imaging research lacks standardised public datasets to enable reproducible deep learning (DL) model development. We present Laryngeal...

Denoising of 4D dynamic PET images using spatiotemporal regularization with integrated temporal restoration (SPRINTER).

PURPOSE: High-quality 4D dynamic PET imaging is often compromised by noise, especially in low-count frames, which limits clinical utility and quantita...

Human serum albumin profiling by top-down analysis enables multi-class liver fibrosis staging: a cross-platform validation study.

Chronic liver disease (CLD) affects millions worldwide, yet accurately staging its progression without liver biopsy remains a major clinical challenge...

Grey wolf optimized neural network with hybrid feature extraction for heart disease prediction.

With an increasing mortality rate due to heart diseases, there is a critical need for early and reliable cardiovascular disease prediction. However, w...

Deep integration of clinical metadata with [18F]FDG PET/CT imaging for histological subtyping in non-small cell lung cancer: a multi-center study.

PURPOSE: To develop and validate a multimodal deep learning framework that integrates clinical metadata with [18F]FDG PET/CT imaging to resolve overla...

Single-cell transcriptomic analysis reveals the T lymphocyte microenvironment and identifies HOPX as a hub gene in membranous nephropathy.

BACKGROUND: Membranous nephropathy (MN) is an autoimmune disease characterized by immune complex deposition and progressive renal function impairment....

Repeatability of AI-quantified incidental findings on lung cancer screening CT scans in the NELSON trial.

OBJECTIVES: Computed tomography (CT) scans for lung cancer screening provide the opportunity of quantifying incidental findings. We evaluated the repe...

Application of Hi-C sequencing to detect oncogene rearrangements for diagnosis and treatment of large B-cell lymphoma.

Diffuse large B-cell lymphoma (DLBCL), the most common type of lymphoma, arises from various pathogenic mechanisms including gene translocations and f...