Lymphoma

Predicting transcranial ultrasound insertion loss using skull CT: A deep learning approach.

Transcranial ultrasound (tUS) is a non-invasive neuromodulation technique with applications in brain disorders. However, ultrasound attenuation induced by the skull significantly affects focal energy transmission. The loss of ultrasound intensity can be quantified by insertion loss (IL). Accurate IL prediction is crucial for optimizing ultrasound delivery. Conventional grid-based numerical methods...

Integrated omics and machine learning uncover the molecular basis of environmental toxicant 6PPD-Qinduced non-obstructive azoospermia.

BACKGROUND: N-(1,3-dimethylbutyl)-N'-phenyl-p-phenylenediamine-quinone (6PPD-Q), a transformation product of tire rubber antioxidants, has been increasingly recognized as an emerging environmental contaminant with potential reproductive toxicity. Although growing evidence implicates 6PPD-Q exposure in male infertility, its role in specific pathological conditions such as sertoli cell-only syndrome...

Double Graph Attention Network for predicting non-alcoholic fatty liver disease in patients with type 2 diabetes.

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disease, while non-alcoholic fatty liver disease (NAFLD) is the most prevalent chronic liver di...

Organ preservation in rectal cancer: an endoscopist's perspective on current evidence and future directions.

Rectal cancer management has increasingly shifted toward organ-preserving strategies that aim to maintain oncologic control while preserving bowel, ur...

Multi-omics deep learning improves FDG PET-CT-based long-term prognostication of breast cancer.

[18F] fluorodeoxyglucose positron emission tomography - computed tomography (FDG PET-CT) is increasingly used for staging of breast cancer in the prim...

18F-FDG PET radiomics model for predicting TARE response in patients with colorectal cancer liver metastases.

PURPOSE: Predicting treatment response in patients with colorectal cancer liver metastases (CRCLM) who have undergone transarterial radioembolization ...

A comprehensive multi-task deep learning model for kidney cancer: histological subtyping, clinical staging, and anatomical complexity grading.

OBJECTIVES: To develop and validate a multi-task deep learning (MTDL) model using multiphase contrast-enhanced CT (CECT) for simultaneously assessing ...

Large-scale DNA organization analysis of primary breast cancers for prediction of axillary lymph node metastases.

Breast cancer is the leading cause of cancer-related deaths among women worldwide. It is standard practice for patients to undergo a sentinel lymph no...

Microbiome-Based Modeling of CAR-T Therapy Response in Lymphoma: Insights From Shotgun Metagenomics Sequencing.

The interplay between the commensal microbiota and the mammalian immune system may influence the outcomes of T cell-driven cancer immunotherapies. How...

Machine learning-driven dynamic prognosis for primary colorectal lymphoma.

Regularized Single-Cell Imaging Enables Generalizable AI Models for Stain-Free Cell Viability Screening.

Cell viability assays are essential tools in biomedical research and drug development. Artificial intelligence (AI) offers the potential to simplify t...

Deep learning assessment of fetal brain maturation on 3D ultrasound volumes in early-onset fetal growth restriction.

OBJECTIVES: To quantify fetal brain maturation in fetuses with early-onset fetal growth restriction (FGR) by estimating gestational age (GA) based on ...

Lung scintigraphy for the diagnosis of acute pulmonary embolism.

Pulmonary embolism (PE) remains a major diagnostic challenge due to its potentially life-threatening nature and the clinical burden associated with an...

Hyperspectral imaging dataset for non-destructive fertility and structural evaluation of chicken eggs.

Hyperspectral imaging (HSI) is a smart, non-destructive sensing that integrates spectral and spatial information, advancing fertility prediction and s...

Artificial Intelligence Applications in Thyroid Cancer Care.

CONTEXT: Artificial intelligence (AI) has created tremendous opportunities to improve thyroid cancer care. EVIDENCE ACQUISITION: We used the "artifici...

Proteomic profiling of single extracellular vesicles as a promising new approach for the diagnosis and treatment modality of advanced ovarian cancer.

This study utilized a novel Proximity Barcoding Assay to perform high-resolution proteomic profiling of individual plasma extracellular vesicles from ...

Machine learning using entropy-based texture features from MRI to differentiate histological subtypes of non-small cell lung cancer identified as metabolically active on PET/MRI.

Texture analysis is a foundational approach in imaging studies and demonstrates excellent diagnostic performance, with radiomic analysis being the mos...

Translational and real-world evidence of trastuzumab biosimilar CT-P6 plus pertuzumab in neoadjuvant HER2-positive early breast cancer.

BACKGROUND: Data on neoadjuvant treatment with trastuzumab biosimilars, particularly CT-P6, in combination with pertuzumab, are limited. This study ev...

Decoding Allosteric Inhibition in MALT1: The Hidden Role of Conformational Plasticity in Metastable States via Biased MD and Deep Learning.

The mucosa-associated lymphoid tissue lymphoma-translocation protein 1 (MALT1) is a key protein in the adaptive immune response system in humans. This...

The Potential of Ensemble-Based Automated Sleep Staging on Single-Channel EEG Signal From a Wearable Device.

Machine-learning-based sleep staging models have achieved expert-level performance on standard polysomnographic (PSG) data. However, their application...