AIMC Topic: Lung Neoplasms
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2.5D deep learning based on multi-parameter MRI to differentiate primary lung cancer pathological subtypes in patients with brain metastases.
European journal of radiology
Aug 28, 2024
BACKGROUND: Brain metastases (BMs) represents a severe neurological complication stemming from cancers originating from various sources. It is a highly challenging clinical task to accurately distinguish the pathological subtypes of brain metastatic ...
Survival analysis for lung cancer patients: A comparison of Cox regression and machine learning models.
International journal of medical informatics
Aug 26, 2024
INTRODUCTION: Survival analysis based on cancer registry data is of paramount importance for monitoring the effectiveness of health care. As new methods arise, the compendium of statistical tools applicable to cancer registry data grows. In recent ye...
Machine learning enabled classification of lung cancer cell lines co-cultured with fibroblasts with lightweight convolutional neural network for initial diagnosis.
Journal of biomedical science
Aug 23, 2024
BACKGROUND: Identification of lung cancer subtypes is critical for successful treatment in patients, especially those in advanced stages. Many advanced and personal treatments require knowledge of specific mutations, as well as up- and down-regulatio...
Incidental pulmonary nodules: Natural language processing analysis of radiology reports.
Respiratory medicine and research
Aug 22, 2024
BACKGROUND: Pulmonary nodules are a common incidental finding on chest Computed Tomography scans (CT), most of the time outside of lung cancer screening (LCS). We aimed to evaluate the number of incidental pulmonary nodules (IPN) found in 1 year in o...
An end-to-end deep learning method for mass spectrometry data analysis to reveal disease-specific metabolic profiles.
Nature communications
Aug 20, 2024
Untargeted metabolomic analysis using mass spectrometry provides comprehensive metabolic profiling, but its medical application faces challenges of complex data processing, high inter-batch variability, and unidentified metabolites. Here, we present ...
Speed and efficiency: evaluating pulmonary nodule detection with AI-enhanced 3D gradient echo imaging.
European radiology
Aug 18, 2024
OBJECTIVES: Evaluating the diagnostic feasibility of accelerated pulmonary MR imaging for detection and characterisation of pulmonary nodules with artificial intelligence-aided compressed sensing.
Clinical applications of radiomics and deep learning in breast and lung cancer: A narrative literature review on current evidence and future perspectives.
Critical reviews in oncology/hematology
Aug 14, 2024
Radiomics, analysing quantitative features from medical imaging, has rapidly become an emerging field in translational oncology. Radiomics has been investigated in several neoplastic malignancies as it might allow for a non-invasive tumour characteri...
Comprehensive Potential of Artificial Intelligence for Predicting PD-L1 Expression and EGFR Mutations in Lung Cancer: A Systematic Review and Meta-Analysis.
Journal of computer assisted tomography
Aug 12, 2024
OBJECTIVE: To evaluate the methodological quality and the predictive performance of artificial intelligence (AI) for predicting programmed death ligand 1 (PD-L1) expression and epidermal growth factor receptors (EGFR) mutations in lung cancer (LC) ba...
Efficient model-informed co-segmentation of tumors on PET/CT driven by clustering and classification information.
Computers in biology and medicine
Aug 12, 2024
Automatic tumor segmentation via positron emission tomography (PET) and computed tomography (CT) images plays a critical role in the prevention, diagnosis, and treatment of this disease via radiation oncology. However, segmenting these tumors is chal...
Family history of cancer and lung cancer: Utility of big data and artificial intelligence for exploring the role of genetic risk.
Lung cancer (Amsterdam, Netherlands)
Aug 9, 2024
OBJECTIVES: Lung Cancer (LC) is a multifactorial disease for which the role of genetic susceptibility has become increasingly relevant. Our aim was to use artificial intelligence (AI) to analyze differences between patients with LC based on family hi...
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