AIMC Topic: Lung Neoplasms

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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
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
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
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)
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...

Clinical application of convolutional neural network lung nodule detection software: An Australian quaternary hospital experience.

Journal of medical imaging and radiation oncology
INTRODUCTION: Early-stage lung cancer diagnosis through detection of nodules on computed tomography (CT) remains integral to patient survivorship, promoting national screening programmes and diagnostic tools using artificial intelligence (AI) convolu...

Performance of machine learning algorithms for lung cancer prediction: a comparative approach.

Scientific reports
Due to the excessive growth of PM 2.5 in aerosol, the cases of lung cancer are increasing rapidly and are most severe among other types as the highest mortality rate. In most of the cases, lung cancer is detected with least symptoms at its later stag...

Detection of Lungs Tumors in CT Scan Images Using Convolutional Neural Networks.

IEEE/ACM transactions on computational biology and bioinformatics
Current human being's lifestyle has caused / exacerbated many diseases. One of these diseases is cancer, and among all kinds of cancers like, brain pulmonary; lung cancer is fatal. The cancers could be detected early to save lives using Computer Aide...

Big Data Analytics on Lung Cancer Diagnosis Framework With Deep Learning.

IEEE/ACM transactions on computational biology and bioinformatics
As the segment of diseased tissue in PET images is time-consuming, laborious and low accuracy, this work proposes an automated framework for PET image screening, denoising and diseased tissue segmentation. First, taking into account the characteristi...

Deep learning ensemble approach with explainable AI for lung and colon cancer classification using advanced hyperparameter tuning.

BMC medical informatics and decision making
Lung and colon cancers are leading contributors to cancer-related fatalities globally, distinguished by unique histopathological traits discernible through medical imaging. Effective classification of these cancers is critical for accurate diagnosis ...