Lung Cancer

Accurate diagnosis of non-small cell and small cell lung cancer by using machine learning models trained with physical science features extracted from pathological images.

BACKGROUND: Accurate differentiation between non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) is crucial for optimising treatment strategies and improving patient outcomes in lung cancer management. Early and precise classification supports tailored therapeutic decisions and enhances prognosis prediction. PURPOSE: This study develops a novel method to use machine learning model...

High-throughput strategy for targeting MDM2 in uveal melanoma to reverse radiation therapy resistance.

Uveal melanoma (UM) presents a formidable clinical challenge due to its marked resistance to radiotherapy. In this study, an integrative strategy combining machine learning models with high-throughput screening platforms was employed to identify novel small-molecule inhibitors targeting MDM2, with the aim of overcoming this intrinsic resistance. Transcriptome sequencing and machine learning analys...

Deep learning on genome-wide association studies to predict the patient-specific risk of radiation-induced erectile dysfunction.

OBJECTIVE: Radiation-induced erectile dysfunction (RIED) is a frequent, unpredictable complication following radiotherapy for prostate cancer. We hypo...

Mechanized precision: the rise of robotic systems in spinal surgery.

BACKGROUND: Robot-assisted spinal surgery has rapidly evolved into a transformative innovation. Initially developed for pedicle screw placement, curre...

Efferocytosis-related biomarkers and immune infiltration in pancreatic ductal adenocarcinoma based on single-cell sequencing and machine learning: a comprehensive bioinformatics analysis and experimental validation.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is a highly invasive and lethal malignancy, with its complex tumor microenvironment (TME) severely...

An informed monotone neural network for multi-stage chronic kidney disease prediction with integrated GFR estimation.

Chronic kidney disease (CKD) is a progressive condition affecting over 850 million people worldwide, where timely detection and accurate staging are c...

Federated CT foundation models for multi-center detection of lymph node metastasis in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies, with prognosis strongly influenced by the presence of lymph node ...

Preoperative MRI-Derived Navigation Reduces Radiation Exposure in Pediatric Spinal Fusion.

BACKGROUND: Minimizing radiation exposure during pediatric spinal deformity correction is critical due to the cumulative lifetime effects of ionizing ...

Artificial intelligence (AI) for early identification of radiotherapy related toxicities from the electronic health records of patients with head and neck cancer.

We evaluated artificial intelligence (AI) for detecting osteoradionecrosis, fibrosis, trismus, and dysphagia in 207 head and neck cancer patient elect...

Artificial intelligence-enhanced three-dimensional radiological analysis for predicting spread through air spaces in early-stage lung adenocarcinoma.

OBJECTIVES: Spread through air space (STAS) is a pathological feature that correlates with poor prognosis, especially in patients with lung adenocarci...

Large-scale AI analysis reveals missed opportunities in albuminuria testing and disease-modifying therapy implementation.

BACKGROUND: Albuminuria is a key diagnostic and prognostic biomarker of chronic kidney disease (CKD), associated with adverse cardiovascular and renal...

CT-Free Quantitative Thyroid SPECT Based on Artificial Intelligence: A Prospective Multicenter Noninferiority Clinical Trial.

BACKGROUND: Hybrid single-photon emission computed tomography (SPECT)/computed tomography (CT) is used for the differential diagnosis of thyrotoxicosi...

Inverse design of an ultra-wideband endfire grooved half-mode waveguide (G-HMWG) antenna based on the CNN approach.

This study presents an AI-assisted inverse design methodology for a compact and ultra-wideband grooved half-mode waveguide (G-HMWG) end-fire antenna. ...

Climate-aware hybrid Kolmogorov-Arnold networks for interpretable solar radiation forecasting.

Accurate short-term solar radiation forecasting is essential for the reliable integration of photovoltaic systems into modern power grids, particularl...

Imaging-based stratification of Claudin 18.2-positive gastric adenocarcinoma using 18F-FDG PET/CT radiomics.

PURPOSE: This study aimed to develop and validate a non-invasive, multimodal radiomics model based on preoperative 1⁸F-FDG PET/CT to predict CLDN18.2 ...

Survival prediction in non-small cell lung cancer using layer-wise radiomics and stacked radscore integration: a multi-institutional study.

OBJECTIVES: To enhance prognostic modeling in patients with non-small cell lung cancer (NSCLC), we developed and externally validated a novel radiomic...

Artificial Intelligence-Aided Lung Ultrasound Detection of Interstitial Lung Disease in Systemic Sclerosis and Inflammatory Myopathy.

BACKGROUND: Lung ultrasound (LUS) is a sensitive, low-cost, and radiation-free modality for ILD detection. We previously developed and validated LUS i...

Deep learning reconstruction for 40-keV virtual monoenergetic CT of colon cancer: evaluation of image quality and edge sharpness.

BACKGROUND: Virtual monoenergetic imaging (VMI) at 40 keV improves iodine attenuation in colon cancer CT but is constrained by severe image noise. Dee...

Construction of chronic inflammation and mitochondrial energy metabolism-associated predictive and therapeutic models for lung adenocarcinoma patients.

Integration of multi-omics and machine learning to identify core genes in PANoptosisof lung adenocarcinoma and their mechanisms in the tumor microenvironment and therapeutic potential.

Lung adenocarcinoma (LUAD) is one of the leading causes of cancer-related deaths worldwide, and its complex tumor microenvironment (TME) is a key barr...