Lung Cancer

ProteoBoostR: an interactive framework for supervised machine learning in clinical proteomics.

BACKGROUND: Mass spectrometry-based proteomics enables high-throughput quantification of thousands of proteins in clinical samples, fueling biomarker discovery for disease diagnosis and prognosis. However, leveraging complex proteomic profiles for predictive modeling often requires advanced machine learning (ML) expertise that many biomedical researchers lack. User-friendly tools are needed to app...

Exploration Novel Therapeutic Targets for Periodontitis via Stress Granules Biomarkers.

BACKGROUND: Periodontitis (PD) is associated with stress granules (SGs), which are involved in cellular stress responses. Identifying biomarkers related to SGs in PD is key to grasping its pathogenesis and devising novel therapeutic approaches. METHODS: Microarray datasets GSE10334 and GSE106090 were downloaded from public databases, and experimental verification was conducted. Differentially expr...

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...

Amplifying image quality gain in x-ray phase contrast imaging of mastectomy samples with deep learning denoising.

Phase-contrast computed tomography (PCT) of the breast has previously been shown to produce higher-quality images at lower radiation doses without the...

Immune Checkpoint Activity and Prognostic Roles of TNFRSF8, CD160, and TNFSF9 in Primary and Brain Metastatic NSCLC.

Here, we utilized advanced bioinformatics approaches alongside experimental validation to identify key prognostic biomarkers and potential immune chec...

Numerical modeling and prediction of late estimated glomerular filtration rate in kidney transplant recipients based on machine learning models and the Monte Carlo simulation method.

OBJECTIVES: This paper presents an experimental numerical method for modeling and analyzing stochastic systems. For this purpose, various machine pred...

A screening strategy based on machine learning for diagnostic biomarkers in small cell lung cancer.

Small cell lung cancer (SCLC) is the most aggressive subtype with high mortality rates due to the lack of specific diagnostic biomarkers to delay the ...

An integrative multiomics random forest framework for robust biomarker discovery.

BACKGROUND: High-throughput technologies now produce a wide array of omics data, from genomic and transcriptomic profiles to epigenomic and proteomic ...

Semi-supervised learning for dose prediction in targeted radionuclide therapy: a synthetic data study.

Objective.Accurate and personalized radiation dose estimation is crucial for effective targeted radionuclide therapy (TRT). Deep learning (DL) holds p...

Deep radiomics for prognostic prediction in locally advanced non-small cell lung cancer by leveraging OmicsMap-based image representation.

Patients with locally advanced non-small cell lung cancer (LA-NSCLC) exhibit heterogeneous prognoses despite receiving standard treatments, highlighti...

CT-based Prediction of Visceral Pleural Invasion in Lung Adenocarcinoma ≤ 3 cm: Enhancing Deep Learning Specificity by Waiving Chest Wall Information.

BACKGROUND: Accurate preoperative prediction of visceral pleural invasion (VPI) in lung adenocarcinoma is essential for guiding surgical decision-maki...

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...

LungSurg: A Generative AI System for Segmentation and Phase Classification in Thoracoscopic Lobectomy.

The integration of artificial intelligence (AI) into surgical practices is advancing towards greater intelligence and precision. This study assesses t...

Acetyl tributyl citrate as a new risk factor for osteoporosis: An integrated study from multi-level toxicological assessment and experimental validation.

This study aimed to elucidate the toxicological effects and underlying mechanisms of the plasticizer acetyl tributyl citrate (ATBC) on osteoporosis (O...

Validation of histopathology-based deep learning algorithms for detection of actionable non-small cell lung cancer biomarkers.

Non-small cell lung cancer (NSCLC) patient management relies on molecular analysis to determine eligibility for targeted therapy. Furthermore, neoadju...

Improved visualization of pancreas and tumor boundaries using high-frequency kernels with deep-learning image reconstruction at high-strength level.

PURPOSE: This study aimed to investigate the feasibility of combining high-frequency reconstruction kernels and deep-learning image reconstruction at ...

Deep-learning pipeline for automated skeletal muscle segmentation and sarcopenia detection.

BACKGROUND: Sarcopenia, characterized by progressive skeletal muscle loss, is associated with poor outcomes in various diseases. Traditional methods f...

Attenuation-based ultra-low-dose lung computed tomography at 0.1 mSv to 0.3 mSv effective dose in children.

BACKGROUND: Radiation dose reduction is essential in paediatric lung computed tomography (CT). Advances in energy-integrating detector CT and deep-lea...

OLink proteomic biomarker to predict clinical efficacy of Macaranga sinensis Müll.Arg: A nested case-control study.

ETHNOPHARMACOLOGICAL RELEVANCE: Despite the fact that herbal medicine has been used for a long time, their clinical application is challenged by uncle...

Impact of a new deep-learning image reconstruction algorithm on potential dose reduction and quality of chest CT images: a phantom study.

PURPOSE: To evaluate the impact of a new deep-learning image reconstruction (DLR) algorithm on image quality and potential dose reduction compared wit...