Latest AI and machine learning research in lung cancer for healthcare professionals.
This paper introduces a deep learning-based framework for phase-only synthesis of cosecant-squared (csc²) radiation patterns in planar antenna arrays with high efficiency and accuracy. The proposed method employs a physics-informed deep neural network (PIDNN), where the training process is guided by a loss function that enforces consistency between the desired and generated radiation patterns. By ...
INTRODUCTION: Exposure to ionizing radiation by endoscopy personnel during fluoroscopy-guided procedures remains a health hazard. We aimed to evaluate a novel lead flap-enhanced artificial intelligence-enabled fluoroscopy (LE-AIF) system to minimize radiation scatter. METHODS: We conducted prospective study of 218 patients who underwent fluoroscopy-guided endoscopic procedures using conventional A...
AIMS: We aimed to develop a machine learning-based tool for accurate quantitative prediction of diuretic response in acute heart failure (AHF). METHOD...
OBJECTIVE: Accurate attenuation correction (AC) is critical in quantitative brain PET imaging. Conventional CT-based AC methods increase radiation exp...
PURPOSE: Non-small cell lung cancer (NSCLC) remains a major clinical challenge, with Programmed death-ligand 1 (PD-L1) expression serving as a crucial...
Due to its high genomic heterogeneity and dense desmoplastic microenvironment, traditional diagnosis and treatment for pancreatic ductal adenocarcinom...
BACKGROUND: Barrett's oesophagus (BE), the precursor to oesophageal adenocarcinoma, progresses through a stepwise dysplastic sequence. Accurate dyspla...
PURPOSE: Early radiation-induced lung injury remains a clinically relevant complication after thoracic radiotherapy. We compared pretreatment, posttre...
Computed tomography [CT] is the frontline imaging modality for the assessment of polytrauma patients because of its speed, diagnostic accuracy and inf...
Artificial intelligence (AI) is poised to fundamentally transform radiation medicine, with growing influence across clinical decision-making, workflow...
Chimeric antigen receptor (CAR) T cells have demonstrated curative potential in hematologic cancers and increasing efficacy in solid tumors and non-ma...
BACKGROUND: Although fibroblast growth factor receptor (FGFR) inhibitors (FGFRi) have demonstrated clinical promise, the inevitable emergence of acqui...
BACKGROUND: To develop and validate a multimodal deep learning model for pre-treatment prediction of radiation-induced temporal lobe injury (RTLI), an...
OBJECTIVE: To develop and validate a high-fidelity super-resolution (SR)-enhanced radiomics framework using a Residual Channel Attention Network (RCAN...
Releases from nuclear or radiological security events can result in significant internal radiation contamination through inhalation of particulate con...
Accurate, noninvasive prediction of invasiveness in ground-glass nodules (GGNs) is important for surgical planning in lung adenocarcinoma. This multic...
Growing concerns regarding per- and polyfluoroalkyl substances (PFAS) as pervasive environmental contaminants have prompted increasing scrutiny regard...
Metabolic reprogramming is a core hallmark of cancer, yet how it contributes to the clinical heterogeneity of lung adenocarcinoma (LUAD) remains poorl...
BACKGROUND: Bone metastasis (BM) significantly impairs lung cancer prognosis and patient quality of life. Conventional imaging modalities often face l...
The increasing use of nuclear technology in medicine, industry, and energy requires effective durable radiation shielding. This study aimed to develop...